From 942322093547f511bd458d05c016b80c85464c28 Mon Sep 17 00:00:00 2001 From: Wendy V Gaultier Date: Wed, 25 Oct 2023 17:39:49 +0000 Subject: [PATCH 01/15] Overview Section --- docs/en/index.md | 4 +- docs/en/welcome-message.md | 4 +- docs/en/welcome.md | 2 +- .../Machine-Learning-Model-Cloud-Storage.md | 53 ++ .../7-MLOps/Machine-Learning-Model-Serving.md | 39 ++ .../Machine-Learning-Training-Pipelines.md | 414 ++++++++++++++ docs/fr/7-MLOps/Machine-Learning.md | 367 ++++++++++++ docs/fr/7-MLOps/Overview.md | 18 + docs/fr/7-MLOps/PaaS-Integration.md | 55 ++ docs/fr/7-MLOps/Serving.md | 18 + docs/fr/7-MLOps/sklearn_iris_jsondata.ipynb | 523 ++++++++++++++++++ docs/fr/Aide.md | 8 +- docs/fr/images/Kubeflow.PNG | Bin 1573 -> 76115 bytes docs/fr/images/SlackAAW copy.PNG | Bin 0 -> 44547 bytes docs/fr/images/statistiques-sur-la-lune.jpg | Bin 0 -> 113308 bytes docs/fr/index.md | 14 +- docs/fr/message-de-bienvenue.md | 7 +- mkdocs-fr.yml | 12 + 18 files changed, 1519 insertions(+), 19 deletions(-) create mode 100644 docs/fr/7-MLOps/Machine-Learning-Model-Cloud-Storage.md create mode 100644 docs/fr/7-MLOps/Machine-Learning-Model-Serving.md create mode 100644 docs/fr/7-MLOps/Machine-Learning-Training-Pipelines.md create mode 100644 docs/fr/7-MLOps/Machine-Learning.md create mode 100644 docs/fr/7-MLOps/Overview.md create mode 100644 docs/fr/7-MLOps/PaaS-Integration.md create mode 100644 docs/fr/7-MLOps/Serving.md create mode 100644 docs/fr/7-MLOps/sklearn_iris_jsondata.ipynb create mode 100644 docs/fr/images/SlackAAW copy.PNG create mode 100644 docs/fr/images/statistiques-sur-la-lune.jpg diff --git a/docs/en/index.md b/docs/en/index.md index 96236e6f4..fdf7d091a 100644 --- a/docs/en/index.md +++ b/docs/en/index.md @@ -10,7 +10,7 @@ _Welcome to the world of data science and machine learning!_ !!! info "What is the AAW?" - **[Advanced Analytics Workspace](https://analytics-platform.statcan.gc.ca/)** is an open source platform designed for data scientists, data stewards, analysts and researchers familiar with open source tools and coding. Developed by data scientists for data scientists, AAW provides a flexible environment that enables advanced practitioners to get their work done with ease. + **[Advanced Analytics Workspace](https://www.statcan.gc.ca/data-analytics-services/aaw)** is an open source platform designed for data scientists, data stewards, analysts and researchers familiar with open source tools and coding. Developed by data scientists for data scientists, AAW provides a flexible environment that enables advanced practitioners to get their work done with ease. The AAW is a comprehensive solution for data science and data analytics. With the AAW, you can customize notebook server deployments to suit your specific data science needs. We have a small number of custom Docker images made by our team. @@ -35,7 +35,7 @@ The AAW portal homepage is available for internal users only. However, external !!! info annotate "AAW Portal Homepage" - [**Portal Homepage for Statistics Canada Employees**](https://www.statcan.gc.ca/data-analytics-service/aaw) - - [**Portal Homepage for External Users**](https://analytics-platform.statcan.gc.ca/covid19) + - [**Portal Homepage for External Users**](https://www.statcan.gc.ca/data-analytics-services/overview) ### Kubeflow Account diff --git a/docs/en/welcome-message.md b/docs/en/welcome-message.md index 3c8ba1cba..3e2732fe0 100644 --- a/docs/en/welcome-message.md +++ b/docs/en/welcome-message.md @@ -1,4 +1,4 @@ -![image](https://user-images.githubusercontent.com/8212170/158243976-0ee25082-f3dc-4724-b8c3-1430c7f2a461.png) +![Welcome](https://user-images.githubusercontent.com/8212170/158243976-0ee25082-f3dc-4724-b8c3-1430c7f2a461.png) # 🧙🔮 Welcome to Advanced Analytics Workspace (AAW) @@ -18,7 +18,7 @@ AAW is based on the Kubeflow project which is an open source comprehensive solut - 🌀 AAW Portal Homepage - **Internal Only** [https://www.statcan.gc.ca/data-analytics-service/aaw](https://www.statcan.gc.ca/data-analytics-service/aaw) - - **Internal/External** [https://analytics-platform.statcan.gc.ca/covid19](https://analytics-platform.statcan.gc.ca/covid19) + - **Internal/External** [https://www.statcan.gc.ca/data-analytics-service/aaw](https://www.statcan.gc.ca/data-analytics-service/aaw) - 🤖 Kubeflow Dashboard - [https://kubeflow.aaw.cloud.statcan.ca/](https://kubeflow.aaw.cloud.statcan.ca/) diff --git a/docs/en/welcome.md b/docs/en/welcome.md index d800063c2..2e451e808 100644 --- a/docs/en/welcome.md +++ b/docs/en/welcome.md @@ -1,4 +1,4 @@ -![image](https://user-images.githubusercontent.com/8212170/158243976-0ee25082-f3dc-4724-b8c3-1430c7f2a461.png) +message-de-bienvenue/![image](https://user-images.githubusercontent.com/8212170/158243976-0ee25082-f3dc-4724-b8c3-1430c7f2a461.png) # 🧙🔮 Welcome to Advanced Analytics Workspace (AAW) diff --git a/docs/fr/7-MLOps/Machine-Learning-Model-Cloud-Storage.md b/docs/fr/7-MLOps/Machine-Learning-Model-Cloud-Storage.md new file mode 100644 index 000000000..cd1dd41fc --- /dev/null +++ b/docs/fr/7-MLOps/Machine-Learning-Model-Cloud-Storage.md @@ -0,0 +1,53 @@ +# Statcan Protected B Data + + +!!! info "Protected B" + The AAW is certified for hosting Protected B data! + +In order to upload Protected B data to the AAW, users will need to request access through the Customer Success Unit (CSU). AAW users will also need to provide a namespace, get a sponsor and get approval from OPMIC. Once the process has been approved, our Fair Data Infrastructure (FDI) team will then create a folder on Net A which in turn will give access to user(s) through the active directory. The data will then be able to transfer from Net A to AAW Cloud + +Storing machine learning models in a protected cloud storage environment is essential for ensuring the security and privacy of sensitive data. The Advanced Analytics Workspace (AAW) provides a secure and robust cloud storage environment that can be used to store machine learning models and other data assets. + +The AAW platform provides a protected cloud storage environment that is designed to meet the most stringent data security and privacy requirements. The storage environment is protected by industry-standard encryption and access controls, which ensures that only authorized personnel can access sensitive data. This protects against unauthorized access, data breaches, and other security threats. + +In addition to its robust security features, the AAW cloud storage environment is also highly scalable and flexible. This means that data scientists and machine learning engineers can easily scale their storage needs as their datasets and model sizes grow. This enables them to store and manage large volumes of data and models without having to worry about storage limitations or performance bottlenecks. + +Storing machine learning models in a protected cloud storage environment on the Advanced Analytics Workspace provides a secure, scalable, and flexible solution for managing and protecting sensitive data. By leveraging the cloud storage capabilities provided by the AAW platform, data scientists and machine learning engineers can focus on building and deploying their models with confidence, knowing that their data is protected and secure. + +## Cloud Storage + + +!!! info "Cloud Storage Advantages" + Cloud storage offers several advantages for data science and machine learning, particularly in terms of scalability, accessibility, and cost-effectiveness. + +Firstly, cloud storage enables data scientists to store and process large amounts of data without having to worry about the limitations of local storage. This is particularly important in the context of machine learning, where large datasets are required for training and testing models. Cloud storage allows data scientists to scale up their storage capacity as needed, without having to invest in expensive hardware. + +Secondly, cloud storage allows data scientists to access data from anywhere, using any device with an internet connection. This enables collaboration across geographically distributed teams and allows data scientists to work remotely. Additionally, cloud storage makes it easier to share data with other stakeholders, such as business partners or customers. Finally, cloud storage is typically more cost-effective than on-premises storage, particularly for smaller organizations or those with limited IT resources. + +Overall, cloud storage is a reliable and convenient solution for storing and managing your data. Whether you need to store large amounts of data or just a few files, cloud storage makes it easy to manage your storage needs without the hassle of traditional storage solutions. + +The AAW platform provides several types of storage: + +- Disks (also called Volumes on the Kubeflow Notebook Server creation screen) +- Data Lakes (coming soon) + +Depending on your use case, either disk or bucket may be most suitable. Our [storage overview](../5-Storage/Overview.md) will help you compare them. + +### Disks +
+ [![Disks](../images/Disks.PNG)](../5-Storage/Disks.md) +
+**[Disks](../5-Storage/Disks.md)** are added to your notebook server by adding Data Volumes. + +### Data Lakes (Coming Soon) + +A data lake is a central repository that allows you to store all your structured and unstructured data at any scale. It's a cost-effective way to store and manage all types of data, from raw data to processed data, and it's an essential tool for data scientists. + +One of the primary advantages of a data lake is its flexibility. It allows you to store all types of data without the need to define a schema in advance, which is especially useful when dealing with unstructured data. This flexibility allows data scientists to easily explore, experiment, and extract insights from their data without being constrained by the limitations of a traditional relational database. + +Data lakes also enable data scientists to centralize their data, making it easier to manage and analyze large volumes of data from various sources. With a data lake, data scientists can easily ingest and store data from a variety of sources, such as databases, cloud storage, and third-party APIs. Additionally, data lakes often provide features for data governance, metadata management, and access control, which helps ensure the data is of high quality and compliant with relevant regulations. + +Furthermore, cloud-based data lakes provide scalable, cost-effective storage solutions that can be easily expanded at the click of a button. As a data scientist's data storage needs grow, they can add additional storage capacity to their data lake with minimal effort, without worrying about the underlying infrastructure or maintenance. + +Overall, data lakes are a critical tool for data scientists, as they provide the flexibility, scalability, and ease of use needed to store and manage large volumes of data, enabling data scientists to focus on extracting insights and value from the data. + diff --git a/docs/fr/7-MLOps/Machine-Learning-Model-Serving.md b/docs/fr/7-MLOps/Machine-Learning-Model-Serving.md new file mode 100644 index 000000000..2b02e2e10 --- /dev/null +++ b/docs/fr/7-MLOps/Machine-Learning-Model-Serving.md @@ -0,0 +1,39 @@ +# Introduction to Model Serving + +In the context of governments, serving machine learning models means making trained models available for use by other applications and systems. This could include making predictions or classifications based on input data, or providing insights and recommendations based on the results of data analysis. + +Serving machine learning models in a government context raises important issues related to data privacy. Government agencies are often responsible for collecting and managing sensitive personal data, such as health records, financial data, and criminal records. When serving machine learning models, it's critical to ensure that this data is protected and that access to it is strictly controlled. + +To address these concerns, government agencies must implement robust data privacy and security measures when serving machine learning models. This could include encrypting data both at rest and in transit, implementing access controls and user authentication, and regularly monitoring for data breaches and vulnerabilities. + +In addition to data privacy and security, it's also important to consider the ethical implications of serving machine learning models in a government context. Machine learning models can be biased or discriminatory, leading to unfair treatment of certain groups of people. To mitigate these risks, government agencies must carefully evaluate and monitor their machine learning models, and take steps to address any biases or discrimination that may arise. + +Overall, serving machine learning models in a government context requires careful consideration of data privacy, security, and ethical concerns. By implementing robust measures to protect personal data and prevent bias, government agencies can leverage the power of machine learning to make better decisions and improve outcomes for citizens while maintaining trust and transparency. + +## Why serve with us? + +Serving machine learning models with the Advanced Analytics Workspace (AAW) has several advantages. First, the AAW is an open-source data analytics platform that provides access to a variety of advanced analytics tools, including Python, R, and SAS. This makes it easy to deploy machine learning models and integrate them into existing workflows. + +Second, the AAW supports multiple MLOps frameworks, including Couler, Seldon, and Argo Workflows. These frameworks provide a range of features, including model versioning, model serving, and model monitoring, that simplify the process of deploying and managing machine learning models in production. + +Third, the AAW provides a secure and scalable platform for serving machine learning models with Protected B status. Models can be served using containerized environments, such as Docker, which provide a high level of isolation and security. The AAW also provides access to cloud computing resources, allowing users to scale up their compute power as needed to handle high volumes of requests. + +Finally, the AAW is a collaborative platform that allows users to share code and data with other researchers and analysts. This fosters a community of users who can learn from each other's work and collaborate on projects that require advanced analytics capabilities. + +In summary, serving machine learning models with the Advanced Analytics Workspace provides access to advanced analytics tools, multiple MLOps frameworks, a secure and scalable Proteced B platform, and a collaborative community of users, making it an ideal platform for data scientists and analysts who want to deploy and manage machine learning models in production. + +## Seldon Core + +Seldon Core is an open-source platform for deploying, scaling, and monitoring machine learning models on Kubernetes. It provides a simple and efficient way to deploy machine learning models as microservices in a production environment. + +Serving machine learning models using Seldon Core involves the following steps: + +1. Model packaging: The first step is to package the trained machine learning model in a container image with all the required dependencies. Seldon Core supports various machine learning frameworks, including TensorFlow, PyTorch, and Scikit-learn. + +2. Model deployment: Once the container image is created, the next step is to deploy the model on Kubernetes using Seldon Core. This involves defining the deployment configuration file, which specifies the resources required for the deployment, such as the number of replicas and the compute resources. + +3. Model serving: Once the model is deployed, Seldon Core exposes a REST API endpoint that can be used to make predictions. Clients can send requests to the endpoint with input data, and the model will return the corresponding output. Seldon Core also supports various deployment patterns, such as canary deployment and A/B testing, to enable easy experimentation and testing of different models. + +4. Model monitoring: Seldon Core provides various monitoring capabilities to track the performance of deployed models. This includes real-time monitoring of model metrics, such as latency and throughput, as well as logging of request and response data for debugging purposes. + +Seldon Core makes it easy to serve machine learning models at scale, with support for high availability, scalability, and fault tolerance. It also provides integration with various Kubernetes-native tools, such as Istio and Prometheus, to enable advanced monitoring and observability. diff --git a/docs/fr/7-MLOps/Machine-Learning-Training-Pipelines.md b/docs/fr/7-MLOps/Machine-Learning-Training-Pipelines.md new file mode 100644 index 000000000..8d2fa5817 --- /dev/null +++ b/docs/fr/7-MLOps/Machine-Learning-Training-Pipelines.md @@ -0,0 +1,414 @@ +# Training Machine Learning Models on the AAW + +
+![Robots in work](../images/mlops.jpg) +
+ + +!!! info + Training machine learning models involves using algorithms to learn patterns and relationships in data. This process involves identifying features or variables that are relevant to the problem at hand and using these features to make predictions or classifications. + +## Why train with us? + +_Training machine learning models on the Advanced Analytics Workspace (AAW) has several advantages._ + +1. **Open Source:** The AAW is an *open source data platform hosted by Statistics Canada* that provides secure (Protected B) access to a variety of data sources, including census data, surveys, and administrative records. This data can be used to train machine learning models and generate insights that can inform policy decisions and improve business processes. + +2. **Versatile:** The AAW is *designed to handle large and complex datasets*. It provides access to a range of advanced analytics tools, in any language you like, including *Python, R, and SAS*, which can be used to preprocess data, train machine learning models, and generate visualizations. *Because the AAW leverages cloud technologies, *users can scale up their computing power as needed*. +* +3. **Secure:** The AAW is a *secure platform (Protected B) that adheres to the highest standards of data privacy and security*. Data can be stored and processed on the platform without risk of unauthorized access or data breaches. + +## MLOps and Data Pipelines + + +!!! info "Optimize Data Workflows" + MLOps and data pipelines are important tools used in the field of data science to manage and optimize data workflows. + +### MLOps + +MLOps refers to the set of practices and tools used to manage the entire lifecycle of a machine learning model. This includes everything from developing and training the model to deploying it in production and maintaining it over time. MLOps ensures that machine learning models are reliable, accurate, and scalable, and that they can be updated and improved as needed. + +### Data Pipelines + +Data pipelines are a series of steps that help move data from one system or application to another. This includes collecting, cleaning, transforming, and storing data, as well as retrieving it when needed. Data pipelines are important for ensuring that data is accurate, reliable, and accessible to those who need it. + + +!!! info "Automation and Reliability" + MLOps and data pipelines help organizations manage the complex process of working with large amounts of data and developing machine learning models. By automating these processes and ensuring that data is accurate and reliable, organizations can save time and resources while making better decisions based on data-driven insights. + +### Why Containerized MLOps? + +The advantages of using a containerized approach for training machine learning models with Argo Workflows include: + +1. **Reproducibility:** Containerizing the machine learning model and its dependencies ensures that the environment remains consistent across different runs, making it easy to reproduce results. + +2. **Scalability:** Argo Workflows can orchestrate parallel jobs and complex workflows, making it easy to scale up the training process as needed. + +3. **Portability:** Containers can be run on any platform that supports containerization, making it easy to move the training process to different environments or cloud providers. + +4. **Collaboration:** By pushing the container to a container registry, other users can easily download and use the container for their own purposes, making it easy to collaborate on machine learning projects. + +Argo Workflows and containerization provide a powerful and flexible approach for training machine learning models. By leveraging these tools, data scientists and machine learning engineers can build, deploy, and manage machine learning workflows with ease and reproducibility. + +## How to Train Models + +There are multiple ways to train machine learning models and it is not our place to tell anyone how to do it. That being said we have provided below a couple of guides on how to train machine learning models using the tools available on the AAW. The first tutorial is about training a simple model directly in a JupyterLab notebook. The second tutorial assumes the user is more advanced and is interested in defining an MLOps pipeline for training models using Argo Workflows. + +### Create a Notebook Server on the AAW + + +!!! info "Notebook Servers" + Regardless of whether you plan on working in JupyterLab, R Studio or something more advanced with Argo Workflows, you'll need the appropriate notebook server. [Follow the instructions found here to get started.](https://docs.google.com/presentation/d/12yTDlbMCmbg0ccdea2h0vwhs5YTa_GHm_3DieG5A-k8/edit?usp=sharing) + +### Using JupyterLab + +![JupyterLab](../images/jupyterlab.jpg) + + +!!! info "JupyterLab is Popular" + Training machine learning models with JupyterLab is a popular approach among data scientists and machine learning engineers. + +Here you will find the steps required to train a machine learning model with JupyterLab on the AAW. Because we are a multi-lingual environment, we've done our best to provide code examples in our most popular languages, `Python`, `R` and `SAS`. + +#### 1. Import the required libraries + +Once you have a JupyterLab session running, you need to import the required libraries for your machine learning model. This could include libraries such as `NumPy`, `Pandas`, `Scikit-learn`, `Tensorflow`, or `PyTorch`. If you are using `R`, you'll want `tidyverse`, `caret` and `janitor`. + +=== "Python" + ``` py title="libraries.py" linenums="1" + #!/usr/bin/env python + + # tensorflow and keras for building and training deep learning models + import tensorflow as tf + from tensorflow import keras + + # numpy for numerical computations + import numpy as np + + # pandas for data manipulation and analysis + import pandas as pd + + # matplotlib for data visualization + import matplotlib.pyplot as plt + ``` +=== "R" + ``` r title="libraries.R" linenums="1" + #!/usr/bin/env Rscript + + # tidyverse for awesome data analysis and munging tools + library(tidyverse) + + # janitor to clean your data + library(janitor) + + # caret for easy machine learning + library(caret) + ``` +=== "SASPy" + ``` py title="libraries.py" linenums="1" + #!/usr/bin/env python + + # the only library you'll need to access SAS from Python + import saspy + ``` +=== "SAS" + ``` sas title="libraries.sas" linenums="1" + + ``` + + +!!! note "About the Code" + The code examples you see in this document and throughout the documentation are for illustrative purposes to get you started on your projects. Depending on the specific task or project, other libraries and steps may be required. + +#### 2. Load and preprocess the data + +Next, you need to load and preprocess the data you'll be using to train your machine learning model. This could include data cleaning, feature extraction, and normalization. The exact preprocessing steps you'll need to perform will depend on the specific dataset you're working with, the requirements of your machine learning model and the job to be done. + +=== "Python" + ``` py title="load_data.py" linenums="1" + #!/usr/bin/env python + + # Import necessary packages + import pandas as pd + from sklearn.preprocessing import StandardScaler + + # Load data from a CSV file + data = pd.read_csv('data.csv') + + # Data cleaning! A lot more can be done, this is basic + data = data.dropna() # Drop rows with missing values + data = data.drop_duplicates() # Drop duplicate rows + + # Feature extraction + X = data[['feature1', 'feature2', 'feature3']] # Select relevant features + + # Normalization + scaler = StandardScaler() # Create a scaler object + X_norm = scaler.fit_transform(X) # Normalize the feature values + ``` +=== "R" + ``` r title="load_data.R" linenums="1" + #!/usr/bin/env Rscript + + # Import necessary packages + library(tidyverse) + library(janitor) + + # Load data from a CSV file + data <- read_csv("data.csv") + + # Clean data using janitor + data_cleaned <- data %>% + # Remove leading/trailing whitespace in column names + clean_names() %>% + # Remove rows with missing values + remove_empty() %>% + # Convert date column to Date format + mutate(date = as.Date(date, format = "%m/%d/%Y")) %>% + # Remove duplicate rows + distinct() %>% + # Reorder columns + select(date, column2, column1, column3) + ``` +=== "SASPy" + ``` py title="load_data.py" linenums="1" + #!/usr/bin/env python + + # Import necessary packages + import saspy + + # Start a SAS session and check configuration information + sas = saspy.SASsession(cfgname='default') + + # Load data from a CSV file + data = sas.read_csv("./data.csv") + ``` +=== "SAS" + ``` sas title="load_data.sas" linenums="1" + /* Reading a comma delimited file with a .csv extension */ + /* */ + /* Since the DBMS= value is CSV, you do not have to use the DELIMITER= */ + /* statement. By default, it is assumed the variable names are on the first */ + /* row, so the GETNAMES= statement is not required. */ + /* */ + /* Create comma delimited test file to read using PROC IMPORT below. */ + + /* Load data from a CSV file */ + proc import + datafile='data.csv' + out=data + dbms=csv + replace; + run; + + /* Display data */ + proc print; + run; + ``` + +#### 3. Split the data into training and testing sets + + Once the data is preprocessed, you need to split it into training and testing sets. The training set will be used to train the machine learning model, while the testing set will be used to evaluate its performance. + +=== "Python" + ``` py title="train_test.py" linenums="1" + #!/usr/bin/env python + + # Import necessary packages + import pandas as pd + from sklearn.model_selection import train_test_split + + # Split the data into training and testing sets + X_train, X_test, y_train, y_test = train_test_split(X_norm, + data['target'], test_size=0.2, random_state=42) + ``` +=== "R" + ``` r title="train_test.R" linenums="1" + #!/usr/bin/env Rscript + + # Import necessary packages + library(caret) + + # Load the dataset + data <- read.csv("my-dataset.csv") + + # Set the seed for reproducibility + set.seed(123) + + # Split the dataset into train and test using caret's createDataPartition function + train_index <- createDataPartition(data$target_variable, p = 0.7, list = FALSE) + train_data <- data[train_index, ] + test_data <- data[-train_index, ] + ``` +=== "SASPy" + ``` py title="train_test.py" linenums="1" + #!/usr/bin/env python + + ``` +=== "SAS" + ``` sas title="train_test.sas" linenums="1" + + ``` + + +!!! note + We split the data into training and testing sets using the `train_test_split` function from `scikit-learn`, which randomly splits the data into two sets based on the specified test size and random seed. + +#### 4. Define and train the machine learning model + +With the data split, you can now define and train your machine learning model using the training set. This could involve selecting the appropriate algorithm, hyperparameter tuning, and cross-validation. + +=== "Python" + ``` py title="train.py" linenums="1" + #!/usr/bin/env python + + # Import necessary packages + from sklearn.ensemble import RandomForestClassifier + from sklearn.model_selection import train_test_split + + # Load the dataset + data = pd.read_csv("my-dataset.csv") + + # Split the dataset into train and test + X_train, X_test, y_train, y_test = train_test_split(data.iloc[:, :-1], data.iloc[:, -1], test_size=0.3, random_state=123) + + # Train the model + model = RandomForestClassifier(n_estimators=100, random_state=123) + model.fit(X_train, y_train) + + # Print the accuracy score on the test data + print("Accuracy on test set: {:.3f}".format(model.score(X_test, y_test))) + ``` +=== "R" + ``` r title="train.R" linenums="1" + #!/usr/bin/env Rscript + + # Import necessary packages + library(caret) + + # Load the dataset + data <- read.csv("my-dataset.csv") + + # Set the seed for reproducibility + set.seed(123) + + # Split the dataset into train and test using caret's createDataPartition function + train_index <- createDataPartition(data$target_variable, p = 0.7, list = FALSE) + train_data <- data[train_index, ] + test_data <- data[-train_index, ] + + # Define the training control + train_control <- trainControl(method = "cv", number = 5) + + # Train the model using caret's train function, (method = "rf" is for random forest) + model <- train(target_variable ~ ., data = train_data, method = "rf", trControl = train_control) + + # Print the model object to view the results + print(model) + ``` +=== "SASPy" + ``` py title="train.py" linenums="1" + #!/usr/bin/env python + + # Import necessary packages + import saspy + import pandas as pd + + # Establish a connection to a SAS session + sas = saspy.SASsession() + + # Load the dataset + data = pd.read_csv("my-dataset.csv") + + # Upload the dataset to SAS + sas_df = sas.df2sd(data, "mydata") + + # Split the dataset into train and test + train, test = sas.surveyselect(data=sas_df, + method="SRS", + seed=123, + samprate=0.7, + outall=True, + strata="target_variable", + partind=True) + + # Train the model using the HPFOREST procedure + model = sas.hpforest(data=train, + target="target_variable", + input="input_variable_1-input_variable_n", + partition="rolevar", + rolevars={"test": "0", "train": "1"}, + nominals=["input_variable_1-input_variable_n"], + forestopts={"ntree": 100, "seed": 123}) + + # Score the model on the test data + predictions = model.predict(newdata=test, out=pred_out) + + # Compute the accuracy score on the test data + accuracy = sas.freq(data=predictions, tables="target_variable*p_target_variable", nocum=True, nocol=True) + + # Print the accuracy score + print("Accuracy on test set: {:.3f}".format(accuracy.Frequency.iloc[0, 1] / accuracy.Frequency.iloc[:, 1].sum())) + + # Disconnect from the SAS session + sas.disconnect() + ``` +=== "SAS" + ``` sas title="train.sas" linenums="1" + /* Load the dataset */ + proc import datafile="my-dataset.csv" out=mydata dbms=csv replace; + run; + + /* Split the dataset into train and test */ + proc surveyselect data=mydata method=srs seed=123 out=selected outall + samprate=0.7; + strata target_variable; + run; + + /* Train the model */ + proc hpforest data=selected; + class _all_; + target target_variable / level=nominal; + partition rolevar=target_variable(test="0" train="1"); + input _all_; + forest ntree=100 seed=123; + run; + + /* Score the model on the test data */ + proc hpforest predict testdata=selected out=testout; + run; + + /* Print the accuracy score on the test data */ + proc freq data=testout; + tables target_variable*p_target_variable / nocum nocol; + run; + ``` + +#### 5. Evaluate the model + +After training the model, you need to evaluate its performance on the testing set. This will give you an idea of how well the model will perform on new, unseen data. + +#### 6. Deploy the model + +Finally, you can deploy the trained machine learning model in a production environment. + +### Using Argo Workflows + +![Workflow Production Art](../images/argo-workflows-assembly-line.jpg) + + +!!! info "MLOps Best Practices" + Argo Workflows is an excellent tool for anyone looking to implement MLOps practices and streamline the process of training and deploying machine learning models or other data science tasks such as ETL. + +**[Argo Workflows](https://argoproj.github.io/argo-workflows/)** is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Argo Workflows is implemented as a Kubernetes CRD (Custom Resource Definition). It is particularly well-suited for use in machine learning and data science workflows. + +Argo Workflows allows you to + +- Define workflows where each step in the workflow is a container. +- Model multi-step workflows as a sequence of tasks or capture the dependencies between tasks using a directed acyclic graph (DAG). +- Easily run compute intensive jobs for machine learning or data processing in a fraction of the time using Argo Workflows on Kubernetes. +- Run CI/CD pipelines natively on Kubernetes without configuring complex software development products. + +making it easy to manage the entire end-to-end machine learning pipeline. With Argo Workflows, you can easily build workflows that incorporate tasks such as data preprocessing, model training, and model deployment, all within a Kubernetes environment. + +See the [argo workflows section](../3-Pipelines/Argo.md) for more details. \ No newline at end of file diff --git a/docs/fr/7-MLOps/Machine-Learning.md b/docs/fr/7-MLOps/Machine-Learning.md new file mode 100644 index 000000000..49fa0022a --- /dev/null +++ b/docs/fr/7-MLOps/Machine-Learning.md @@ -0,0 +1,367 @@ +# Machine Learning Models + +Machine learning models are computational algorithms that are designed to automatically learn patterns and relationships from data. These models are trained on a dataset, which is typically a collection of examples or instances, each of which consists of a set of features or variables, as well as a target variable or output. + +The goal of a machine learning model is to identify patterns and relationships within the data that can be used to make predictions or decisions about new, unseen data. This involves developing a mathematical representation of the relationship between the input features and the output variable, based on the patterns observed in the training data. Once the model is trained, it can be used to make predictions or decisions about new, unseen data. + +There are several different types of machine learning models, each of which is designed to address different types of problems or data. Some of the most common types of machine learning models include: + +1. **Regression Models:** Regression models are used to predict continuous numerical values, such as stock prices or housing prices. + +2. **Classification Models:** Classification models are used to predict discrete categorical values, such as whether a customer will buy a product or not. + +3. **Clustering Models:** Clustering models are used to identify groups or clusters within a dataset based on similarities between instances. + +4. **Recommendation Models:** Recommendation models are used to recommend products or services to users based on their past behavior or preferences. + +5. **Neural Networks:** Neural networks are a type of machine learning model that is designed to mimic the structure and function of the human brain. They are commonly used in image recognition, speech recognition, and natural language processing applications. + + +!!! info "Machine Learning Models Can be Biased" + Machine learning models are a powerful tool for analyzing and making predictions about data, and they have a wide range of applications in fields such as finance, healthcare, marketing, and more. However, it is important to note that machine learning models are not perfect and can sometimes make errors or produce biased results. Therefore, it is important to carefully evaluate and test machine learning models before using them in real-world applications. + +## Examples + +### Linear Regression + +![Linear Regression](../images/linear-regression.jpg) + + +!!! info "Linear Regression" + $$ + \hat{Y}_i = \hat{\beta}_0 + \hat{\beta}_1 X_i + \hat{\epsilon}_i + $$ + + _Where $\hat{Y}_i$ denotes the $i$th estimator of the true value $Y$ based on the $i$th training epoch. Each $\hat{\beta}$ is a parameter to be learned. $\hat{\epsilon}_i$ is the amount of noise permitted in the model and may vary depending on the training epoch number denoted by $i$. Each $X_i$ represents the $i$th batch of training data._ + +In classical statistical models like linear regression, the goal is to find a line that best fits the data, allowing us to make predictions about new data points. + +As the complexity of the problem increases, more sophisticated algorithms are needed, such as decision trees, support vector machines, and random forests. However, these methods have limitations, and they may not be able to capture complex patterns in large datasets. + +#### Example Code + +=== "Python" + ``` py title="linear_regression.py" linenums="1" + #!/usr/bin/env python + + # Load the required libraries + import pandas as pd + from sklearn.model_selection import train_test_split + from sklearn.linear_model import LinearRegression + from sklearn.metrics import mean_squared_error + + # Load the dataset + data = pd.read_csv('path/to/dataset.csv') + + # Split the data into training and testing sets + X_train, X_test, y_train, y_test = train_test_split(data.drop('target_variable', axis=1), data['target_variable'], test_size=0.2) + + # Train the linear regression model + linear_model = LinearRegression() + linear_model.fit(X_train, y_train) + + # Make predictions on the testing set + y_pred = linear_model.predict(X_test) + + # Evaluate the model performance + mse = mean_squared_error(y_test, y_pred) + rmse = mse ** 0.5 + print('Root Mean Squared Error:', rmse) + ``` + +=== "R" + ``` r title="linear_regression.r" linenums="1" + #!/usr/bin/env Rscript + + # Set random seed for reproducibility + set.seed(123) + + # Load the dataset + data <- read.csv('path/to/dataset.csv') + + # Split the data into training and testing sets + train_index <- sample(1:nrow(data), size=0.8*nrow(data)) + train_data <- data[train_index,] + test_data <- data[-train_index,] + + # Train the linear regression model + lm_model <- lm(target_variable ~ ., data=train_data) + + # Make predictions on the testing set + y_pred <- predict(lm_model, newdata=test_data[,-which(names(test_data)=='target_variable')]) + + # Evaluate the model performance + mse <- mean((y_pred - test_data$target_variable)^2) + rmse <- sqrt(mse) + print(paste('Root Mean Squared Error:', rmse)) + ``` + +### Support Vector Machine (SVM) + +![Support Vector Machine](../images/svm.jpg) + + +!!! note "SVM" + $$ + \underset{\mathbf{w},b,\boldsymbol{\xi}}{\operatorname{minimize}} \hspace{0.2cm} \frac{1}{2} ||\mathbf{w}||^2 + C \sum_{i=1}^{N} \xi_i + $$ + + $$ + \text{where} \hspace{0.2cm} y_i(\mathbf{w}^T\mathbf{x}_i + b) \geq 1-\xi_i \quad \text{and} \quad \hspace{0.2cm} \xi_i \geq 0 \hspace{0.2cm} \forall i \in {1,2,...,N} + $$ + + _In this formula, we use the standard SVM formulation where $\mathbf{w}$ is the weight vector, $b$ is the bias term, and $\boldsymbol{\xi}$ is the slack variable vector. The objective is to minimize the L2-norm of the weight vector $\mathbf{w}$, subject to the constraint that all training examples are classified correctly with a margin of at least 1, plus an allowance for some margin violations controlled by the regularization parameter $C$. The target variable $y_i$ takes values of either 1 or -1, representing the two classes in the binary classification problem, and $\mathbf{x}_i$ is the feature vector for the $i$th training example._ + +A support vector machine (SVM) is a supervised machine learning algorithm that can be used for classification, regression, and outlier detection. It is a popular algorithm in the field of machine learning, especially for solving classification problems. + +The basic idea behind SVM is to find a hyperplane that best separates the input data into different classes. In a two-class classification problem, the hyperplane is a line that divides the data points of one class from the data points of the other class. SVM tries to find the hyperplane that maximizes the margin between the two classes, where the margin is the distance between the hyperplane and the nearest data points from each class. + +#### Example Code + +=== "Python" + ``` py title="svm.py" linenums="1" + #!/usr/bin/env python + + # Load the required libraries + import pandas as pd + from sklearn.model_selection import train_test_split + from sklearn.svm import SVC + from sklearn.metrics import accuracy_score + + # Load the dataset + data = pd.read_csv('path/to/dataset.csv') + + # Split the data into training and testing sets + X_train, X_test, y_train, y_test = train_test_split(data.drop('target_variable', axis=1), data['target_variable'], test_size=0.2) + + # Train the SVM model + svm_model = SVC(kernel='linear', C=1.0, random_state=42) + svm_model.fit(X_train, y_train) + + # Make predictions on the testing set + y_pred = svm_model.predict(X_test) + + # Evaluate the model performance + accuracy = accuracy_score(y_test, y_pred) + print('Accuracy:', accuracy) + ``` + +=== "R" + + ``` r title="svm.r" linenums="1" + #!/usr/bin/env Rscript + + # Load the required libraries + library(e1071) + + # Load the dataset + data <- read.csv('path/to/dataset.csv') + + # Split the data into training and testing sets + set.seed(123) + train_index <- sample(1:nrow(data), size=0.8*nrow(data)) + train_data <- data[train_index,] + test_data <- data[-train_index,] + + # Train the SVM model + svm_model <- svm(target_variable ~ ., data=train_data, kernel='linear', cost=1) + + # Make predictions on the testing set + y_pred <- predict(svm_model, newdata=test_data[,-which(names(test_data)=='target_variable')]) + + # Evaluate the model performance + accuracy <- mean(y_pred == test_data$target_variable) + print(paste('Accuracy:', accuracy)) + ``` + +### Random Forest + +![Random Forest](../images/random-forest.jpg) + + +!!! note "Random Forest" + $$ + \hat{y} = \frac{1}{T} \sum_{t=1}^{T} f_t(\mathbf{x}), + $$ + + _where $\hat{y}$ is the predicted output, $f_t(\mathbf{x})$ is the prediction of the $t$th tree in the forest for the input $\mathbf{x}$, and $T$ is the number of trees in the forest._ + +Random Forests are an ensemble learning method that can be used for classification and regression problems. They are often used for their ability to handle high-dimContinuous Improvement:ensional datasets and nonlinear relationships between features and targets. + +Each tree is trained on a bootstrapped subset of the original training data, and at each split, a random subset of features is considered for determining the split. The final prediction is obtained by averaging the predictions of all the trees in the forest. + +#### Example Code + +=== "Python" + ``` py title="random_forest.py" linenums="1" + #!/usr/bin/env python + + # Load the required libraries + import pandas as pd + from sklearn.model_selection import train_test_split + from sklearn.ensemble import RandomForestRegressor + from sklearn.metrics import mean_squared_error + + # Load the dataset + data = pd.read_csv('path/to/dataset.csv') + + # Split the data into training and testing sets + X_train, X_test, y_train, y_test = train_test_split(data.drop('target_variable', axis=1), data['target_variable'], test_size=0.2) + + # Train the random forest model + rf_model = RandomForestRegressor(n_estimators=100, random_state=42) + rf_model.fit(X_train, y_train) + + # Make predictions on the testing set + y_pred = rf_model.predict(X_test) + + # Evaluate the model performance + mse = mean_squared_error(y_test, y_pred) + rmse = mse ** 0.5 + print('Root Mean Squared Error:', rmse) + ``` + +=== "R" + ``` r title="random_forest.r" linenums="1" + #!/usr/bin/env Rscript + + # Load the required libraries + library(randomForest) + + # Load the dataset + data <- read.csv('path/to/dataset.csv') + + # Split the data into training and testing sets + set.seed(123) + train_index <- sample(1:nrow(data), size=0.8*nrow(data)) + train_data <- data[train_index,] + test_data <- data[-train_index,] + + # Train the random forest model + rf_model <- randomForest(target_variable ~ ., data=train_data, ntree=100, importance=TRUE) + + # Make predictions on the testing set + y_pred <- predict(rf_model, newdata=test_data[,-which(names(test_data)=='target_variable')]) + + # Evaluate the model performance + mse <- mean((y_pred - test_data$target_variable)^2) + rmse <- sqrt(mse) + print(paste('Root Mean Squared Error:', rmse)) + ``` + +### Deep Learning + +![Deep Learning](../images/deep-learning.jpg) + + +!!! note "Deep Learning" + $$ + \hat{y} = f(\mathbf{W}_L f(\mathbf{W}_{L-1} f(\dots f(\mathbf{W}_1\mathbf{x}+\mathbf{b}_1)\dots)+\mathbf{b}_{L-1})+\mathbf{b}_L) + $$ + + _where $\mathbf{x}$ is the input vector, $\mathbf{W}_i$ and $\mathbf{b}_i$ are the weight matrix and bias vector, respectively, for the $i$th layer, and $f$ is the activation function._ + + _This formula represents a feedforward neural network with $L$ layers, where each layer applies a linear transformation to the output of the previous layer, followed by a non-linear activation function. The output of the final layer, $\hat{y}$, represents the predicted output of the neural network for the given input $\mathbf{x}$._ + +Deep learning is a subset of machine learning that involves training neural networks with many layers of interconnected nodes. This approach can handle large and complex datasets and is used in a wide range of applications, including image recognition, natural language processing, and speech recognition. The training process involves feeding the neural network a large dataset and adjusting the weights of the connections between the nodes to minimize the error between the predicted outputs and the actual outputs. Through repeated iterations, the neural network can learn to recognize patterns in the data and make accurate predictions on new data. + +#### Example Code + +=== "Python" + ``` py title="deep_learning.py" linenums="1" + #!/usr/bin/env python + + # Load the required libraries + import pandas as pd + import numpy as np + import tensorflow as tf + from tensorflow import keras + from sklearn.model_selection import train_test_split + from sklearn.preprocessing import StandardScaler + from sklearn.metrics import accuracy_score + + # Load the dataset + data = pd.read_csv('path/to/dataset.csv') + + # Split the data into training and testing sets + X_train, X_test, y_train, y_test = train_test_split(data.drop('target_variable', axis=1), data['target_variable'], test_size=0.2) + + # Standardize the input features + scaler = StandardScaler() + X_train_scaled = scaler.fit_transform(X_train) + X_test_scaled = scaler.transform(X_test) + + # Define the deep learning model + model = keras.Sequential([ + keras.layers.Dense(64, activation='relu', input_shape=[X_train_scaled.shape[1]]), + keras.layers.Dropout(0.2), + keras.layers.Dense(1, activation='sigmoid') + ]) + + # Compile the model + model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) + + # Train the model + history = model.fit(X_train_scaled, y_train, epochs=50, batch_size=32, validation_split=0.1) + + # Evaluate the model performance + y_pred = model.predict_classes(X_test_scaled) + accuracy = accuracy_score(y_test, y_pred) + print('Accuracy:', accuracy) + ``` + +=== "R" + + ``` r title="deep_learning.r" linenums="1" + #!/usr/bin/env Rscript + + # Load the required libraries + library(keras) + library(tensorflow) + + # Load the dataset + data <- iris + x <- as.matrix(data[, 1:4]) + y <- to_categorical(as.numeric(data[, 5]) - 1) + + # Split the data into training and testing sets + set.seed(123) + train_index <- sample(1:nrow(data), size=0.8*nrow(data)) + x_train <- x[train_index,] + y_train <- y[train_index,] + x_test <- x[-train_index,] + y_test <- y[-train_index,] + + # Define the neural network architecture + model <- keras_model_sequential() %>% + layer_dense(units = 8, input_shape = c(4)) %>% + layer_activation('relu') %>% + layer_dense(units = 3) %>% + layer_activation('softmax') + + # Compile the model + model %>% compile( + loss = 'categorical_crossentropy', + optimizer = 'adam', + metrics = c('accuracy') + ) + + # Train the model + history <- model %>% fit( + x_train, y_train, + epochs = 50, + batch_size = 10, + validation_split = 0.2, + verbose = 0 + ) + + # Evaluate the model performance + metrics <- model %>% evaluate(x_test, y_test) + print(paste('Test Loss:', metrics[1])) + print(paste('Test Accuracy:', metrics[2])) + + # Plot the training and validation accuracy over time + plot(history$metrics$accuracy, type='l', col='blue', ylim=c(0,1), ylab='Accuracy', xlab='Epoch') + lines(history$metrics$val_accuracy, type='l', col='red') + legend('bottomright', legend=c('Training', 'Validation'), col=c('blue', 'red'), lty=1) + ``` diff --git a/docs/fr/7-MLOps/Overview.md b/docs/fr/7-MLOps/Overview.md new file mode 100644 index 000000000..d3df5e162 --- /dev/null +++ b/docs/fr/7-MLOps/Overview.md @@ -0,0 +1,18 @@ + +MLOps, or Machine Learning Operations, refers to the set of practices and tools that enable organizations to develop, deploy, and maintain machine learning models at scale. MLOps aims to streamline the end-to-end process of building and deploying machine learning models by integrating the various stages of the machine learning lifecycle into a cohesive and automated workflow. + +MLOps involves a range of different activities, including data preparation and preprocessing, model training and optimization, model deployment and serving, monitoring and maintenance, and continuous improvement. Some of the key components of MLOps include: + +1. **Data Management:** MLOps involves managing and processing large amounts of data to ensure the quality and accuracy of machine learning models. This involves activities such as data cleaning, data integration, and data transformation. + +2. **Model Training and Optimization:** MLOps involves developing and testing machine learning models, as well as optimizing them for performance and accuracy. This may involve experimenting with different algorithms, hyperparameters, and data pre-processing techniques. + +3. **Model Deployment:** MLOps involves deploying machine learning models to production environments, making them available for use in real-world applications. This may involve containerizing models for easy deployment and scaling, as well as setting up APIs and other interfaces for model serving. + +4. **Monitoring and Maintenance:** MLOps involves monitoring machine learning models in production to ensure that they are performing as expected. This may involve setting up alerts and notifications for model failures, as well as implementing processes for model maintenance and updates. + +5. **Continuous Improvement:** MLOps involves continually improving machine learning models over time, based on feedback from users and ongoing analysis of performance data. This may involve retraining models with new data or incorporating feedback from users to refine models. + +In order to implement MLOps effectively, organizations typically need to adopt a range of different tools and technologies, including data management platforms, machine learning frameworks, containerization tools, and monitoring and logging tools. They also need to establish clear workflows and processes for managing the various stages of the machine learning lifecycle, as well as implementing governance and compliance measures to ensure data privacy and security. + +In summary, MLOps is a critical component of the machine learning lifecycle, enabling organizations to develop, deploy, and maintain machine learning models at scale. By adopting MLOps practices and tools, organizations can streamline their machine learning workflows, improve model accuracy and performance, and deliver more value to users and stakeholders. \ No newline at end of file diff --git a/docs/fr/7-MLOps/PaaS-Integration.md b/docs/fr/7-MLOps/PaaS-Integration.md new file mode 100644 index 000000000..5ce7f47f9 --- /dev/null +++ b/docs/fr/7-MLOps/PaaS-Integration.md @@ -0,0 +1,55 @@ +# Overview + +_One of the main advantages of the AAW platform is its ability to integrate with popular machine learning platforms such as Databricks and AzureML._ + +The Advanced Analytics Workspace (AAW) is an open source data analytics platform that is designed to be highly integrable. This means that it can be easily integrated with other platforms and tools to extend its capabilities and streamline workflows. + +An example diagram depicting a possible PaaS connection strategy: + +
+ +
+![PaaS](../images/PaaS.png) +
+ +**Setup:** If you need help integrating with a platform as a service offering, we're happy +to help! + +## Integration with External Platform as a Service (PaaS) Offerings + +_Integration is key to success._ + +
+[![Integrate with PaaS](../images/IntegratePaaS.PNG)]() +
+ +Our open source platform offers unparalleled optionality to our users. By allowing users to use open source tools, we empower them to use their preferred data science and machine learning frameworks. But the real power of our platform comes from its ability to integrate with many Platform as a Service (PaaS) offerings, like Databricks or AzureML. This means that our users can leverage the power of the cloud to run complex data processing and machine learning pipelines at scale. With the ability to integrate with PaaS offerings, our platform enables our users to take their work to the next level, by giving them the power to scale their workloads with ease, and take advantage of the latest innovations in the field of data science and machine learning. By providing this level of optionality, we ensure that our users can always choose the right tool for the job, and stay ahead of the curve in an ever-changing field. + +We can integrate with many Platform as a Service (PaaS) offerings, like Databricks or AzureML. + + +## Databricks + +- [Databricks from Microsoft](https://azure.microsoft.com/en-ca/services/databricks/) + +Databricks is a cloud-based platform that provides a unified analytics platform for big data processing and machine learning. With its powerful distributed computing engine and streamlined workflow tools, Databricks is a popular choice for building and deploying machine learning models. By integrating with Databricks, the AAW platform can leverage its distributed computing capabilities to train and deploy machine learning models at scale. + +## AzureML + +- [Azure ML from Microsoft](https://azure.microsoft.com/en-us/services/machine-learning/) + +AzureML is another popular machine learning platform that provides a wide range of tools for building, training, and deploying machine learning models. By integrating with AzureML, the AAW platform can leverage its powerful tools for building and training models, as well as its ability to deploy models to the cloud. + +### Examples + +_Examples of how to integrate the AAW platform with these and other platforms can be found on the MLOps Github repository._ + +- [MLOps Github Repository](https://github.com/StatCan/aaw-kubeflow-mlops) + +This repository contains a range of examples and tutorials for using the AAW platform in various machine learning workflows, including data preparation, model training, and model deployment. + +## Conclusion + +By integrating with popular machine learning platforms like Databricks and AzureML, the AAW platform provides a powerful and flexible solution for building, deploying, and managing machine learning workflows at scale. + +By leveraging the integrations and tools provided by these platforms, data scientists and machine learning engineers can accelerate their workflows and achieve better results with less effort. \ No newline at end of file diff --git a/docs/fr/7-MLOps/Serving.md b/docs/fr/7-MLOps/Serving.md new file mode 100644 index 000000000..e786fa8e7 --- /dev/null +++ b/docs/fr/7-MLOps/Serving.md @@ -0,0 +1,18 @@ +# Model Serving with Seldon Core and KFServing + + +!!! warning "⚒ This page is under construction ⚒" + The person writing this entry does not know enough about + this feature to write about it, but you can ask on our Slack channel. + +## Serverless with KNative + +Kubernetes and [KNative](https://knative.dev/) let your services scale up and +down on demand. This lets you create APIs to serve Machine Learning models, +without the need to manage load balancing or scale-up. The platform can handle +all of your scaling for you, so that you can focus on the program logic. + + +!!! warning "⚒ This page is under construction ⚒" + The person writing this entry does not know enough about this + feature to write about it, but you can ask on our Slack channel. diff --git a/docs/fr/7-MLOps/sklearn_iris_jsondata.ipynb b/docs/fr/7-MLOps/sklearn_iris_jsondata.ipynb new file mode 100644 index 000000000..e0a993312 --- /dev/null +++ b/docs/fr/7-MLOps/sklearn_iris_jsondata.ipynb @@ -0,0 +1,523 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Serving an Iris Classifier with Seldon\n", + "\n", + "## Goal of Notebook\n", + "\n", + "The goal of this notebook is to wrap a `scikit-learn` Python model for use as a prediction microservice with Seldon Core." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup Python\n", + "\n", + "Write requirements.txt and configure our Python virtual environment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-04-27T15:57:57.958799Z", + "iopub.status.busy": "2023-04-27T15:57:57.957866Z", + "iopub.status.idle": "2023-04-27T15:57:57.964285Z", + "shell.execute_reply": "2023-04-27T15:57:57.963484Z", + "shell.execute_reply.started": "2023-04-27T15:57:57.958767Z" + }, + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "%%writefile requirements.txt\n", + "scikit-learn\n", + "dill\n", + "pandas\n", + "sklearn\n", + "seldon-core\n", + "requests\n", + "matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-04-27T13:58:18.412667Z", + "iopub.status.busy": "2023-04-27T13:58:18.411840Z", + "iopub.status.idle": "2023-04-27T13:58:19.144219Z", + "shell.execute_reply": "2023-04-27T13:58:19.143136Z", + "shell.execute_reply.started": "2023-04-27T13:58:18.412630Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!if [ ! -d \"./venv\" ]; then python -m venv venv; fi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-04-27T13:58:19.302968Z", + "iopub.status.busy": "2023-04-27T13:58:19.302445Z", + "iopub.status.idle": "2023-04-27T13:58:20.355237Z", + "shell.execute_reply": "2023-04-27T13:58:20.353560Z", + "shell.execute_reply.started": "2023-04-27T13:58:19.302929Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!source ./venv/bin/activate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-04-27T13:58:20.713063Z", + "iopub.status.busy": "2023-04-27T13:58:20.712216Z", + "iopub.status.idle": "2023-04-27T13:58:21.461588Z", + "shell.execute_reply": "2023-04-27T13:58:21.460429Z", + "shell.execute_reply.started": "2023-04-27T13:58:20.713016Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!if [ ! -d \"/home/jovyan/.config/pip\" ]; then mkdir /home/jovyan/.config/pip; fi" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Seldon only runs in the AAW dev environment; modify `pip.conf` to retrieve packages from the internet." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-04-27T13:58:21.463776Z", + "iopub.status.busy": "2023-04-27T13:58:21.463360Z", + "iopub.status.idle": "2023-04-27T13:58:21.469135Z", + "shell.execute_reply": "2023-04-27T13:58:21.468420Z", + "shell.execute_reply.started": "2023-04-27T13:58:21.463740Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "%%writefile /home/jovyan/.config/pip/pip.conf\n", + "[global]\n", + "index-url = https://jfrog.aaw.cloud.statcan.ca/artifactory/api/pypi/pypi-remote/simple" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-04-27T15:58:01.566928Z", + "iopub.status.busy": "2023-04-27T15:58:01.566106Z", + "iopub.status.idle": "2023-04-27T15:58:05.178871Z", + "shell.execute_reply": "2023-04-27T15:58:05.177536Z", + "shell.execute_reply.started": "2023-04-27T15:58:01.566896Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "%%capture\n", + "%pip install -r requirements.txt" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can add the kernel to the JupyterLab launcher for easy reusing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-04-27T13:58:48.714311Z", + "iopub.status.busy": "2023-04-27T13:58:48.713884Z", + "iopub.status.idle": "2023-04-27T13:58:50.178610Z", + "shell.execute_reply": "2023-04-27T13:58:50.177586Z", + "shell.execute_reply.started": "2023-04-27T13:58:48.714273Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!python -m ipykernel install --user --name=sklearn_iris_jsondata" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy Model\n", + "\n", + "Write a SeldonDeployment YAML file for the containerized model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-04-27T14:48:15.600923Z", + "iopub.status.busy": "2023-04-27T14:48:15.600036Z", + "iopub.status.idle": "2023-04-27T14:48:15.606528Z", + "shell.execute_reply": "2023-04-27T14:48:15.605436Z", + "shell.execute_reply.started": "2023-04-27T14:48:15.600890Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "%%writefile sklearn_iris_jsondata_deployment.yaml\n", + "apiVersion: machinelearning.seldon.io/v1alpha2\n", + "kind: SeldonDeployment\n", + "metadata:\n", + " name: seldon-deployment-example\n", + " namespace: bryanpaget\n", + "spec:\n", + " name: sklearn-iris-deployment\n", + " annotations:\n", + " sidecar.istio.io/inject: \"false\"\n", + " predictors:\n", + " - componentSpecs:\n", + " - spec:\n", + " containers:\n", + " - image: seldonio/sklearn-iris:0.3\n", + " imagePullPolicy: IfNotPresent\n", + " name: sklearn-iris-classifier\n", + " graph:\n", + " children: []\n", + " endpoint:\n", + " type: REST\n", + " name: sklearn-iris-classifier\n", + " type: MODEL\n", + " name: sklearn-iris-predictor\n", + " replicas: 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Deploy the SeldonDeployment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-04-27T13:58:50.189468Z", + "iopub.status.busy": "2023-04-27T13:58:50.188796Z", + "iopub.status.idle": "2023-04-27T13:58:53.893779Z", + "shell.execute_reply": "2023-04-27T13:58:53.892661Z", + "shell.execute_reply.started": "2023-04-27T13:58:50.189441Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!kubectl create -f sklearn_iris_jsondata_deployment.yaml" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-04-27T13:58:53.896155Z", + "iopub.status.busy": "2023-04-27T13:58:53.895313Z", + "iopub.status.idle": "2023-04-27T13:58:55.209852Z", + "shell.execute_reply": "2023-04-27T13:58:55.208787Z", + "shell.execute_reply.started": "2023-04-27T13:58:53.896117Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!kubectl rollout status deploy \\\n", + " $(kubectl get deploy -l seldon-deployment-id=seldon-deployment-example \\\n", + " -o jsonpath='{.items[0].metadata.name}')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Is the service available?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-04-27T13:58:55.211582Z", + "iopub.status.busy": "2023-04-27T13:58:55.211279Z", + "iopub.status.idle": "2023-04-27T13:58:55.435452Z", + "shell.execute_reply": "2023-04-27T13:58:55.434211Z", + "shell.execute_reply.started": "2023-04-27T13:58:55.211552Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "for i in range(60):\n", + " state = !kubectl get sdep seldon-deployment-example -o jsonpath='{.status.state}'\n", + " state = state[0]\n", + " print(state)\n", + " if state == \"Available\":\n", + " break\n", + " time.sleep(1)\n", + "assert state == \"Available\"" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test Model\n", + "\n", + "With the virtual environment setup and activated, import the necessary Python libraries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-04-27T15:58:43.902715Z", + "iopub.status.busy": "2023-04-27T15:58:43.901670Z", + "iopub.status.idle": "2023-04-27T15:58:43.906588Z", + "shell.execute_reply": "2023-04-27T15:58:43.905572Z", + "shell.execute_reply.started": "2023-04-27T15:58:43.902678Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import json\n", + "import pandas as pd" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Iris Classifier takes a set of four measurements, in this case `[5.964, 1.006, 2.081, 1.031]`, and it returns a set of probabilities, one for each of three types of iris flowers. [You can read more about iris data here.](https://archive.ics.uci.edu/ml/datasets/iris) Let's send some data to the model. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-04-27T16:05:03.253143Z", + "iopub.status.busy": "2023-04-27T16:05:03.252208Z", + "iopub.status.idle": "2023-04-27T16:05:03.304174Z", + "shell.execute_reply": "2023-04-27T16:05:03.303066Z", + "shell.execute_reply.started": "2023-04-27T16:05:03.253106Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "res = !curl -v seldon-deployment-example-sklearn-iris-predictor.bryanpaget.svc.cluster.local:8000/api/v0.1/predictions \\\n", + " -H 'Content-Type: application/json' \\\n", + " -d '{\"data\": {\"ndarray\": [[5.964, 1.006, 2.081, 1.031]]}}'" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results\n", + "\n", + "These are the results in raw JSON." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-04-27T16:05:03.594132Z", + "iopub.status.busy": "2023-04-27T16:05:03.593539Z", + "iopub.status.idle": "2023-04-27T16:05:03.600395Z", + "shell.execute_reply": "2023-04-27T16:05:03.599596Z", + "shell.execute_reply.started": "2023-04-27T16:05:03.594090Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "parsed = json.loads(res[-1])\n", + "print(json.dumps(parsed, indent=4, sort_keys=True))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This plotting function reveals a bar chart with the classifier's inference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-04-27T16:05:04.188096Z", + "iopub.status.busy": "2023-04-27T16:05:04.187614Z", + "iopub.status.idle": "2023-04-27T16:05:04.541870Z", + "shell.execute_reply": "2023-04-27T16:05:04.540936Z", + "shell.execute_reply.started": "2023-04-27T16:05:04.188045Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def display_iris_classifier_results(res):\n", + " \"\"\"Takes the classification results from the iris classifier and plots them in a bar chart for easy viewing of class probabilities.\n", + "\n", + " Args:\n", + " res (string): results from iris model served by Seldon.\n", + " \"\"\"\n", + " results = res[-1]\n", + " results = json.loads(results)\n", + " names = results[\"data\"][\"names\"]\n", + " predictions = results[\"data\"][\"ndarray\"][0]\n", + " model = results[\"meta\"][\"requestPath\"]\n", + " model = model.popitem()\n", + " model, version = model[0], model[1]\n", + "\n", + " df = pd.DataFrame({\"Class\": names, \"Probability\": predictions})\n", + "\n", + " df.plot(\n", + " kind=\"bar\",\n", + " title=model.replace(\"-\", \" \").title(),\n", + " xlabel=\"Classification\",\n", + " ylabel=\"Probability\",\n", + " legend=False\n", + " )\n", + "\n", + "display_iris_classifier_results(res)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Clean Up\n", + "\n", + "Clean up by removing the deployment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-04-27T16:05:04.750111Z", + "iopub.status.busy": "2023-04-27T16:05:04.749559Z", + "iopub.status.idle": "2023-04-27T16:05:07.723921Z", + "shell.execute_reply": "2023-04-27T16:05:07.722248Z", + "shell.execute_reply.started": "2023-04-27T16:05:04.750073Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!kubectl delete -f sklearn_iris_jsondata_deployment.yaml" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/fr/Aide.md b/docs/fr/Aide.md index 072603594..83030b38f 100644 --- a/docs/fr/Aide.md +++ b/docs/fr/Aide.md @@ -4,20 +4,20 @@ Joignez-vous à nous sur le [canal Slack de l'espace de travail en analytique avancée](https://statcan-aaw.slack.com)! Vous trouverez de nombreux utilisateurs de la plateforme qui pourront peut-être répondre à vos questions. Quelques ingénieurs sont aussi habituellement -présents. Vous pouvez poser des questions et soumettre vos commentaires. +présents. Vous pouvez poser des questions et soumettre vos commentaires, dans la langue de votre choix. -- [Slack (fr)](https://statcan-eaa.slack.com) +- [Slack (fr)](https://statcan-aaw.slack.com) Nous publierons également des avis de mise à jour et d'interruption sur le canal Slack. -# Vidéos tutoriels + # GitHub diff --git a/docs/fr/images/Kubeflow.PNG b/docs/fr/images/Kubeflow.PNG index 1ccb7665823bc27885a8ef821de6be4a3edfa5b3..1ecb693b628be745b00f1cdd87303804a7b6cc26 100644 GIT binary patch literal 76115 zcmYg%c{tSH`~DO~o26A`>8(=9R+b{Pk`l!%wy`9XWvpXrWGPC;@GdRN_Lgij#?CO7 z5k*oYy=*f>lI?||G?o#5XJ-0*uiqbCb6uH^bDr}&_jBL(b9!*e!gSMm+4TqnVw2gq zGgb)1y0-{~NTv8%@Hd8xQf~xe)s5??PhYz3>WV8 z=1%o)eOsw*{$KZtrk}dM?eVDduP?7}S8M+ec^-Gi>&=_^cjU;Jl6;B$J4*AbzJG^b zkN&e)V%;8b>FSOFR*3rC@;U<3|4TpN@#`a_+h!_rkojjeIH~D-o@`9_WKB8l-`e?+ z>NK@x8doN-61swE;gZ74r3P-XCO3)WzE4n35>EIjj@>A+Q@IhhrN*lA)U@0H!T4@Z2~0u|f3w5RL{w>aLW@>5KgbwP0abHdR-x%QK& zlj~hF-pqL%dVXyFnMhX?YhS3PabfMJUO9#1KW-#El?z;UOdgtTd$EpTY}FX>l78V2 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les chercheurs familiarisés avec les outils open source et le codage. Développé par des data scientists pour des data scientists, ETAA fournit un environnement flexible qui permet aux praticiens avancés de faire leur travail en toute simplicité. + **[Espace de travail d'analyse avancée](https://www.statcan.gc.ca/services-analyse-donnees/etaa)** est une plateforme open source conçue pour les data scientists, les intendants des données, les analystes et les chercheurs familiarisés avec les outils open source et le codage. Développé par des data scientists pour des data scientists, ETAA fournit un environnement flexible qui permet aux praticiens avancés de faire leur travail en toute simplicité. L'ETAA est une solution complète pour la science des données et l'analyse des données. Avec l'ETAA, vous pouvez personnaliser les déploiements de serveurs de bloc-notes en fonction de vos besoins spécifiques en science des données. Nous avons un petit nombre d'images Docker personnalisées réalisées par notre équipe. @@ -25,7 +25,7 @@ Quelle que soit l'étape à laquelle vous vous trouvez dans votre parcours en sc ## Premiers pas avec l'ETAA
-![image](https://user-images.githubusercontent.com/8212170/158243976-0ee25082-f3dc-4724-b8c3-1430c7f2a461.png) +![Icone EAA](https://user-images.githubusercontent.com/8212170/158243976-0ee25082-f3dc-4724-b8c3-1430c7f2a461.png)
### Le portail ETAA @@ -35,7 +35,7 @@ La page d'accueil du portail ETAA est disponible uniquement pour les utilisateur !!! info annoter "Page d'accueil du portail ETAA" - [**Page d'accueil du portail pour les employés de Statistique Canada**](https://www.statcan.gc.ca/services-analyse-donnees/etaa) - - [**Page d'accueil du portail pour les utilisateurs externes**](https://analytics-platform.statcan.gc.ca/covid19) + - [**Page d'accueil du portail pour les utilisateurs externes**](https://www.statcan.gc.ca/services-analyse-donnees/apercu) ### Compte Kubeflow @@ -51,7 +51,7 @@ La page d'accueil du portail ETAA est disponible uniquement pour les utilisateur !!! note ""
[![Kubeflow est le cœur de l'ETAA !](./images/Kubeflow.PNG)](https://kubeflow.aaw.cloud.statcan.ca/) -

**[👉 Cliquez ici pour configurer votre compte Kubeflow ! 👈](https://kubeflow.aaw.cloud.statcan.ca/)**

+

**[👉 Cliquez ici pour configurer votre compte Kubeflow! 👈](https://kubeflow.aaw.cloud.statcan.ca/)**

**[Kubeflow](1-Experiments/Kubeflow/)** est une plate-forme open source puissante et flexible qui permet une exploitation dynamique du calcul dans le cloud, les utilisateurs ayant la possibilité de contrôler les ressources de calcul, de mémoire et de stockage utilisées. @@ -70,7 +70,7 @@ La prise en main de l'espace de travail d'analyse avancée (ETAA) est simple et ### Slack
-[![Posez des questions relatives à la plate-forme sur Slack !](images/Slackaaw.PNG)](https://statcan-aaw.slack.com/) +[![Posez des questions relatives à la plate-forme sur Slack !](images/SlackAAW.PNG)](https://statcan-aaw.slack.com/)
- **[Cliquez ici pour vous connecter à notre espace de travail d'assistance Slack](https://statcan-aaw.slack.com/)** @@ -90,7 +90,7 @@ Cliquez sur le lien, puis choisissez "Créer un compte" dans le coin supérieur !!! note ""
- ![Utilisez votre courriel @statcan.gc.ca!](images/Slackaaw2.png) + ![Utilisez votre courriel @statcan.gc.ca!](images/SlackAAW2.png)

Utilisez votre adresse de courriel @statcan.gc.ca afin que vous soyez automatiquement approuvé.

@@ -118,7 +118,7 @@ Voici une liste d'outils que nous proposons : - Cahiers Jupyter - 🐧 Bureaux virtuels Linux pour des outils supplémentaires (🧫 OpenM++, 🌏 QGIS etc.) -Le partage de code, de disques et d'espaces de travail (ex : deux personnes partageant la même machine virtuelle) est décrit plus en détail dans la section [Collaboration](4-Collaboration/Overview.md). Le partage de données via des buckets est décrit plus en détail dans **[Azure Blob Storage](./5-Storage/AzureBlobStorage.md)** +Le partage de code, de disques et d'espaces de travail (ex : deux personnes partageant la même machine virtuelle) est décrit plus en détail dans la section [Collaboration](4-Collaboration/Aperçu.md). Le partage de données via des buckets est décrit plus en détail dans **[Azure Blob Storage](./5-Stockage/AzureBlobStorage.md)** section. ### 💡 Aide diff --git a/docs/fr/message-de-bienvenue.md b/docs/fr/message-de-bienvenue.md index 6aa4b20ee..74a74cc9d 100644 --- a/docs/fr/message-de-bienvenue.md +++ b/docs/fr/message-de-bienvenue.md @@ -1,4 +1,5 @@ -![image](https://user-images.githubusercontent.com/8212170/158243976-0ee25082-f3dc-4724-b8c3-1430c7f2a461.png) +![# 🧙🔮 Bienvenue à l’Espace d'analyse avancé (EAA) +](https://user-images.githubusercontent.com/8212170/158243976-0ee25082-f3dc-4724-b8c3-1430c7f2a461.png) # 🧙🔮 Bienvenue à l’Espace d'analyse avancé (EAA) @@ -18,8 +19,8 @@ EAA est basé sur le projet Kubeflow qui est une solution complète open source ### 🛎️ Services EAA - 🌀 Page d'accueil du portail EEA - - **Interne seulement** [https://www.statcan.gc.ca/data-analytics-service/aaw](https://www.statcan.gc.ca/data-analytics-service/aaw) - - **Interne/externe** [https://analytics-platform.statcan.gc.ca/covid19](https://analytics-platform.statcan.gc.ca/covid19) + - **Interne seulement** [https://www.statcan.gc.ca/services-analyse-donnees/etaa](https://www.statcan.gc.ca/services-analyse-donnees/etaa) + - **Interne/externe** [https://www.statcan.gc.ca/services-analyse-donnees/etaa](https://www.statcan.gc.ca/services-analyse-donnees/etaa) - 🤖 Tableau de bord Kubeflow - [https://kubeflow.aaw.cloud.statcan.ca/](https://kubeflow.aaw.cloud.statcan.ca/) diff --git a/mkdocs-fr.yml b/mkdocs-fr.yml index bde7ba981..06a893573 100644 --- a/mkdocs-fr.yml +++ b/mkdocs-fr.yml @@ -85,4 +85,16 @@ nav: - Aperçu: 5-Stockage/Aperçu.md - MinIo: 5-Stockage/AzureBlobStorage.md - Disque: 5-Stockage/Disque.md + - Gitlab: + - Gitlab: 6-Gitlab/Gitlab.md + - MLOps: + - Overview: 7-MLOps/Overview.md + - Machine Learning Models: 7-MLOps/Machine-Learning.md + - Training ML Models: 7-MLOps/Machine-Learning-Training-Pipelines.md + - Storing ML Models: 7-MLOps/Machine-Learning-Model-Cloud-Storage.md + - Serving ML Models: + - Introduction: 7-MLOps/Machine-Learning-Model-Serving.md + - Example Notebooks: + - Seldon Iris Classifier: 7-MLOps/sklearn_iris_jsondata.ipynb + - Integrate with External PaaS: 7-MLOps/PaaS-Integration.md - Aide/Contactez-nous: Aide.md From 7eddb01bcddf3a8b6925ed149172949e4b528cd6 Mon Sep 17 00:00:00 2001 From: Wendy V Gaultier Date: Thu, 26 Oct 2023 18:51:21 +0000 Subject: [PATCH 02/15] Experiment section --- docs/en/1-Experiments/Kubeflow.md | 2 +- docs/en/1-Experiments/RStudio.md | 7 +- docs/en/1-Experiments/Remote-Desktop.md | 53 ++++----- docs/en/1-Experiments/Selecting-an-Image.md | 49 ++------ docs/fr/1-Experiences/Apercu.md | 25 ++++ docs/fr/1-Experiences/Bureau-virtuel.md | 103 +++++++++++----- docs/fr/1-Experiences/Jupyter.md | 44 +++---- docs/fr/1-Experiences/Kubeflow.md | 112 ++++++++++++------ docs/fr/1-Experiences/RStudio.md | 40 +++++-- .../1-Experiences/Selectionner-une-Image.md | 64 ---------- .../1-Experiences/Selectionner-une-image.md | 42 +++++++ ...ureBlobStorage.md => StockageBlobAzure.md} | 0 docs/fr/images/KubeflowVideo.PNG | Bin 0 -> 184083 bytes docs/fr/images/RStudio.PNG | Bin 0 -> 37869 bytes docs/fr/images/RStudioOption.PNG | Bin 0 -> 49173 bytes docs/fr/images/RemoteDesktop.PNG | Bin 0 -> 48442 bytes docs/fr/images/VirtualDesktop.jpg | Bin 0 -> 54566 bytes docs/fr/images/jupyter_in_action.png | Bin 970826 -> 23773 bytes docs/fr/images/jupyter_in_action_old.png | Bin 0 -> 970826 bytes docs/fr/images/kubeflow_gpu_selection.PNG | Bin 0 -> 11707 bytes docs/fr/images/misc-settings.PNG | Bin 0 -> 10448 bytes docs/fr/images/rd-clipboard-copied-result.png | Bin 0 -> 70105 bytes docs/fr/images/rd-clipboard-copy-from-rd.png | Bin 0 -> 37283 bytes docs/fr/images/rd-clipboard-send-to-rd.png | Bin 0 -> 70823 bytes docs/fr/images/rd_desktop.opti.jpg | Bin 0 -> 14318 bytes docs/fr/images/workspace-volume.PNG | Bin 0 -> 27394 bytes mkdocs-fr.yml | 1 + 27 files changed, 310 insertions(+), 232 deletions(-) create mode 100644 docs/fr/1-Experiences/Apercu.md delete mode 100644 docs/fr/1-Experiences/Selectionner-une-Image.md create mode 100644 docs/fr/1-Experiences/Selectionner-une-image.md rename docs/fr/5-Stockage/{AzureBlobStorage.md => StockageBlobAzure.md} (100%) create mode 100644 docs/fr/images/KubeflowVideo.PNG create mode 100644 docs/fr/images/RStudio.PNG create mode 100644 docs/fr/images/RStudioOption.PNG create mode 100644 docs/fr/images/RemoteDesktop.PNG create mode 100644 docs/fr/images/VirtualDesktop.jpg create mode 100644 docs/fr/images/jupyter_in_action_old.png create mode 100644 docs/fr/images/kubeflow_gpu_selection.PNG create mode 100644 docs/fr/images/misc-settings.PNG create mode 100644 docs/fr/images/rd-clipboard-copied-result.png create mode 100644 docs/fr/images/rd-clipboard-copy-from-rd.png create mode 100644 docs/fr/images/rd-clipboard-send-to-rd.png create mode 100644 docs/fr/images/rd_desktop.opti.jpg create mode 100644 docs/fr/images/workspace-volume.PNG diff --git a/docs/en/1-Experiments/Kubeflow.md b/docs/en/1-Experiments/Kubeflow.md index 4b609dcb3..fa628f333 100644 --- a/docs/en/1-Experiments/Kubeflow.md +++ b/docs/en/1-Experiments/Kubeflow.md @@ -191,7 +191,7 @@ The following can be customized here: !!! success "Your server is running" If all goes well, your server should be running!!! You will now have the - option to connect, and [try out Jupyter!](/daaas/en/1-Experiments/Jupyter) + option to connect, and [try out Jupyter!](../Jupyter) # Once you've got the basics ... diff --git a/docs/en/1-Experiments/RStudio.md b/docs/en/1-Experiments/RStudio.md index 46b8f8aca..c1359dce5 100644 --- a/docs/en/1-Experiments/RStudio.md +++ b/docs/en/1-Experiments/RStudio.md @@ -1,8 +1,6 @@ # Overview -RStudio is an integrated development environment (IDE) for R. It includes a -console, editor, and tools for plotting, history, debugging and workspace -management. +RStudio is an integrated development environment (IDE) for R. It includes a console, editor, and tools for plotting, history, debugging and workspace management. # Video Tutorial @@ -10,8 +8,7 @@ management. # Setup -You can use the `rstudio` image to get an RStudio environment! When you create -your notebook, choose RStudio from the list of available images. +You can use the `rstudio` image to get an RStudio environment! When you create your notebook, choose RStudio from the list of available images. ![RStudio menu](../images/RStudioOption.PNG) ![RStudio](../images/rstudio_visual.png) diff --git a/docs/en/1-Experiments/Remote-Desktop.md b/docs/en/1-Experiments/Remote-Desktop.md index 52ff57808..8639e9ce0 100644 --- a/docs/en/1-Experiments/Remote-Desktop.md +++ b/docs/en/1-Experiments/Remote-Desktop.md @@ -17,7 +17,9 @@ quick access to supporting tools. The operating system is [**Ubuntu**](https://ubuntu.com/about) **22.04** with the [**XFCE**](https://www.xfce.org/about) desktop environment. +
![Remote Desktop](../images/rd_desktop.png) +
## Geomatics @@ -31,64 +33,55 @@ _pip_, _conda_, _npm_ and _yarn_ are available to install various packages. ## Accessing the Remote Desktop -To launch the Remote Desktop or any of its supporting tools, create a Notebook -Server in [Kubeflow](./Kubeflow.md) and select the remote desktop option, which is the Ubuntu image. +To launch the Remote Desktop or any of its supporting tools, create a Notebook Server in [Kubeflow](./Kubeflow.md) and select the remote desktop option, which is the Ubuntu image. ![Remote Desktop](../images/RemoteDesktop.PNG) -Once it has been created, click `Connect` to be redirected to the Remote -Desktop. +Once it has been created, click `Connect` to be redirected to the Remote Desktop. -_Remote Desktop_ brings you to the Desktop GUI through a noVNC session. Click on -the > on the left side of the screen to expand a panel with options such as -fullscreen and clipboard access. +_Remote Desktop_ brings you to the Desktop GUI through a noVNC session. Click on the > on the left side of the screen to expand a panel with options such as fullscreen and clipboard access. +
![NoVNC Panel](../images/rd_novnc_panel.png) +
## Accessing the Clipboard -This is done via the second button from the top of the panel on the left. -It brings up a text box which we can modify to change the contents of the clipboard -or copy stuff from the clipboard of the remote desktop. +This is done via the second button from the top of the panel on the left. It brings up a text box which we can modify to change the contents of the clipboard or copy stuff from the clipboard of the remote desktop. -For example, suppose we want to execute the command `head -c 20 /dev/urandom | md5sum` -and copy-paste the result into a text file on our computer used to connect to the -remote desktop. +For example, suppose we want to execute the command `head -c 20 /dev/urandom | md5sum` and copy-paste the result into a text file on our computer used to connect to the remote desktop. -We first open the clipboard from the panel on the left and paste in that command into -the text box: +We first open the clipboard from the panel on the left and paste in that command into the text box: +
![Clipboard Paste Command from Computer](../images/rd-clipboard-send-to-rd.png) +
-To close the clipboard window over the remote desktop, simply click the clipboard -button again. +To close the clipboard window over the remote desktop, simply click the clipboard button again. -We then right click on a terminal window to paste in that command and press enter to -execute the command. At that point we select the MD5 result, right click, and click -copy: +We then right click on a terminal window to paste in that command and press enter to execute the command. At that point we select the MD5 result, right click, and click copy: +
![Copy to Clipboard from Remote Desktop](../images/rd-clipboard-copy-from-rd.png) +
-If we open the clipboard from the panel on the left again, it will now have the new -contents: +If we open the clipboard from the panel on the left again, it will now have the new contents: +
![Copy to Clipboard from Remote Desktop](../images/rd-clipboard-copy-from-rd.png) +
-The clipboard window will even update in-place if we leave it open the whole time -and we simply select new material on the remote desktop and press copy again. We can -simply copy what we have in that text box and paste it into any other software running -on the computer used to connect. +The clipboard window will even update in-place if we leave it open the whole time and we simply select new material on the remote desktop and press copy again. We can simply copy what we have in that text box and paste it into any other software running on the computer used to connect. ## In-browser Tools ### VS Code -Visual Studio Code is a lightweight but powerful source code editor. It comes -with built-in support for JavaScript, TypeScript and Node.js and has a rich -ecosystem of extensions for several languages (such as C++, C#, Java, Python, -PHP, Go). +Visual Studio Code is a lightweight but powerful source code editor. It comes with built-in support for JavaScript, TypeScript and Node.js and has a rich ecosystem of extensions for several languages (such as C++, C#, Java, Python, PHP, Go). +
![VS Code](../images/rd_vs_code.png) +
## Footnotes diff --git a/docs/en/1-Experiments/Selecting-an-Image.md b/docs/en/1-Experiments/Selecting-an-Image.md index 8157806ab..71ece9d8c 100644 --- a/docs/en/1-Experiments/Selecting-an-Image.md +++ b/docs/en/1-Experiments/Selecting-an-Image.md @@ -1,8 +1,6 @@ # Selecting an Image for your Notebook Server -Depending on your project or use case of the Notebook Server, some images may be -more suitable than others. The following will go through the main features of -each to help you pick the most appropriate image for you. +Depending on your project or use case of the Notebook Server, some images may be more suitable than others. The following will go through the main features of each to help you pick the most appropriate image for you. When selecting an image, you have 3 main options: @@ -12,54 +10,33 @@ When selecting an image, you have 3 main options: ## Jupyter Notebooks -[Jupyter Notebooks](https://jupyter.org/) are used to create and share -interactive documents that contain a mix of live code, visualizations, and text. -These can be written in `Python`, `Julia`, or `R`. +[Jupyter Notebooks](https://jupyter.org/) are used to create and share interactive documents that contain a mix of live code, visualizations, and text. These can be written in `Python`, `Julia`, or `R`. +
![Jupyter Notebooks](../images/jupyter_in_action.png) +
??? info "Common uses include:" data transformation, numerical simulation, statistical modelling, machine learning and more. -The jupyter notebooks are great launchpads for analytics including machine -learning. The `jupyterlab-cpu` image gives a good core experience for python, -including common packages such as `numpy`, `pandas` and `scikit-learn`. If -you're interested specifically in using **_TensorFlow_** or **_PyTorch_**, we -also have `jupyterlab-tensorflow` and `jupyterlab-pytorch` which come with those -tools pre-installed. - -For the `jupyterlab-pytorch` image, the PyTorch packages (torch, torchvision, -and torchaudio) are installed in the `torch` conda environment. You must -activate this environment to use PyTorch. - -For the `jupyterlab-cpu`, `jupyterlab-tensorflow`, and `jupyterlab-pytorch` -images, in the default shell the `conda activate` command may not work. This is -due to the environment not being initialized properly. In this case run `bash`, -you should see the AAW logo and a few instructions appear. After this -`conda activate` should work properly. If you see the AAW logo on startup it -means the environment is correctly initialized and `conda activate` should work -properly. A fix for this bug is in the works, once this is fixed this paragraph -will be removed. - -Each image comes pre-loaded with VS Code in the browser if you prefer a full IDE -experience. +The jupyter notebooks are great launchpads for analytics including machine learning. The `jupyterlab-cpu` image gives a good core experience for python, including common packages such as `numpy`, `pandas` and `scikit-learn`. If you're interested specifically in using **_TensorFlow_** or **_PyTorch_**, we also have `jupyterlab-tensorflow` and `jupyterlab-pytorch` which come with those tools pre-installed. + +For the `jupyterlab-pytorch` image, the PyTorch packages (torch, torchvision, and torchaudio) are installed in the `torch` conda environment. You must activate this environment to use PyTorch. + +For the `jupyterlab-cpu`, `jupyterlab-tensorflow`, and `jupyterlab-pytorch` images, in the default shell the `conda activate` command may not work. This is due to the environment not being initialized properly. In this case run `bash`, you should see the AAW logo and a few instructions appear. After this `conda activate` should work properly. If you see the AAW logo on startup it means the environment is correctly initialized and `conda activate` should work properly. A fix for this bug is in the works, once this is fixed this paragraph will be removed. + +Each image comes pre-loaded with VS Code in the browser if you prefer a full IDE experience. ## RStudio -**[RStudio](../RStudio/)** gives you an integrated development environment -specifically for `R`. If you're coding in `R`, this is typically the Notebook -Server to use. Use the `rstudio` image to get an RStudio environment. +**[RStudio](../RStudio/)** gives you an integrated development environment specifically for `R`. If you're coding in `R`, this is typically the Notebook Server to use. Use the `rstudio` image to get an RStudio environment. ![RStudio](../images/rstudio_visual.png) ## Remote-Desktop -For a full Ubuntu desktop experience, use the remote desktop image. It comes -pre-loaded with Python, R and Geomatics tooling, but are delivered in a typical -desktop experience that also comes with Firefox, VS Code, and open office tools. -The operating system is **[Ubuntu](https://ubuntu.com/about)** 22.04 with the -**[XFCE](https://www.xfce.org/about)** desktop environment. +For a full Ubuntu desktop experience, use the remote desktop image. It comes pre-loaded with Python, R and Geomatics tooling, but are delivered in a typical desktop experience that also comes with Firefox, VS Code, and open office tools. The operating system is **[Ubuntu](https://ubuntu.com/about)** 22.04 with the **[XFCE](https://www.xfce.org/about)** desktop environment. ![Screenshot of the Virtual Desktop](../images/rd_desktop.png) diff --git a/docs/fr/1-Experiences/Apercu.md b/docs/fr/1-Experiences/Apercu.md new file mode 100644 index 000000000..0469032d2 --- /dev/null +++ b/docs/fr/1-Experiences/Apercu.md @@ -0,0 +1,25 @@ +## Expérimentation de la science des données + +_Traitez les données à l'aide de R, Python ou Julia avec Kubeflow, une plateforme d'apprentissage automatique qui fournit une infrastructure simple, unifiée et évolutive pour les charges de travail d'apprentissage automatique._ + +Avec Kubeflow, vous pouvez traiter les données de manière évolutive et efficace en utilisant le langage de programmation de votre choix. Une fois Kubeflow configuré, utilisez Jupyter Notebooks pour créer et partager des documents contenant du code en direct, des équations ou des visualisations. + +Vous pouvez également exécuter Ubuntu en tant que bureau virtuel avec Kubeflow, vous donnant accès à un environnement de développement puissant qui peut être personnalisé selon vos besoins. Avec R Shiny, un framework d'application Web pour R, vous pouvez facilement créer et publier des tableaux de bord statiques et interactifs pour communiquer les résultats de votre analyse aux parties prenantes. + +Kubeflow fournit également une intégration avec des plates-formes externes en tant que service, telles que Google Cloud Platform (GCP) et Amazon Web Services (AWS), vous permettant de déplacer facilement des données et des charges de travail entre différents services cloud. De plus, grâce aux fonctionnalités de collaboration de Kubeflow, vous pouvez travailler sur vos projets avec votre équipe en temps réel, en partageant votre analyse, votre code et vos résultats de manière transparente. + +L'expérimentation en science des données fait référence au processus de conception, de réalisation et d'analyse d'expériences afin de tester des hypothèses et d'obtenir des informations à partir des données. Ce processus implique généralement plusieurs étapes : + +1. Formuler une hypothèse : Avant de mener une expérience, il est important d’avoir une idée claire de ce que vous essayez de tester ou d’apprendre. Cela peut impliquer de formuler une hypothèse sur une relation entre des variables ou d’essayer de répondre à une question de recherche spécifique. + +2. Conception de l'expérience : Une fois que vous avez une hypothèse, vous devez concevoir une expérience qui vous permettra de la tester. Cela peut impliquer de sélectionner un échantillon de données, de choisir les variables à manipuler ou à mesurer et de décider des conditions expérimentales. + +3. Collecte et nettoyage des données : Une fois l'expérience conçue, vous devez collecter les données nécessaires pour tester votre hypothèse. Cela peut impliquer de collecter des données à partir de sources existantes ou de mener vos propres expériences. Une fois les données collectées, vous devez les nettoyer pour supprimer toute erreur ou anomalie. + +4. Analyser les données : Une fois les données propres, vous pouvez commencer à les analyser pour tester votre hypothèse. Cela peut impliquer l'exécution de tests statistiques ou d'algorithmes d'apprentissage automatique, la visualisation des données pour identifier des modèles ou des tendances, ou l'utilisation d'autres techniques analytiques pour obtenir des informations. + +5. Tirer des conclusions : sur la base des résultats de votre analyse, vous pouvez tirer des conclusions quant à savoir si votre hypothèse est étayée ou non. Vous pourrez peut-être également identifier des domaines nécessitant des recherches ou des expérimentations plus approfondies. + +L'analyse des données est un élément clé de l'expérimentation en science des données et implique l'utilisation de diverses techniques et outils pour donner un sens à de grandes quantités de données. Cela peut impliquer une analyse exploratoire des données, dans laquelle vous utilisez des visualisations et des statistiques récapitulatives pour acquérir une première compréhension des données, ou des techniques plus avancées telles que l'apprentissage automatique ou la modélisation statistique. L'analyse des données peut être utilisée pour répondre à un large éventail de questions, depuis de simples questions descriptives sur les données jusqu'à des questions prédictives ou prescriptives plus complexes. + +En résumé, l’expérimentation et l’analyse des données sont des éléments importants du domaine plus large de la science des données et impliquent l’utilisation de données pour tester des hypothèses, obtenir des informations et prendre des décisions éclairées. diff --git a/docs/fr/1-Experiences/Bureau-virtuel.md b/docs/fr/1-Experiences/Bureau-virtuel.md index 6e78926be..0e36714e2 100644 --- a/docs/fr/1-Experiences/Bureau-virtuel.md +++ b/docs/fr/1-Experiences/Bureau-virtuel.md @@ -1,51 +1,90 @@ -# Bureau virtuel +# Aperçu -![Bureau virtuel](../images/rd_desktop.png) +### Bureau virtuel Ubuntu -# Qu'est-ce qu'un Bureau virtuel? +_Vous pouvez exécuter un [**Bureau Ubuntu**](https://ubuntu.com/desktop) complet, avec des applications typiques, directement dans votre navigateur, en utilisant [**Kubeflow!**](https://kubeflow. aaw.cloud.statcan.ca/)_ -Le bureau à distance offre une expérience de bureau Ubuntu avec une interface -graphique dans le navigateur, ainsi qu'un accès rapide aux outils de support. Le -système d'exploitation est [**Ubuntu**](https://ubuntu.com/about) **18.04** avec -l'environnement de bureau [**XFCE**](https://www.xfce.org/about). +
+[![Bureau Virtuel](../images/VirtualDesktop.jpg)](https://novnc.com/info.html) +
-## Versions +Le bureau virtuel Ubuntu est un outil puissant pour les scientifiques de données et les ingénieurs en apprentissage automatique qui doivent exécuter des charges de travail gourmandes en ressources dans le nuage informatique. Ubuntu est une distribution Linux populaire largement utilisée dans les communautés de science des données et d'apprentissage automatique en raison de sa forte prise en charge des outils open source tels que R et Python. Avec Ubuntu Virtual Desktop, vous pouvez rapidement démarrer une machine virtuelle avec Ubuntu préinstallé et y accéder depuis n'importe où avec une connexion Internet. Cela signifie que vous pouvez effectuer des tâches d'analyse de données et d'apprentissage automatique depuis votre ordinateur portable, votre tablette ou votre téléphone sans avoir à vous soucier des limitations matérielles. -Deux versions du Bureau virtuel sont disponibles. _R_ comprend R et RStudio. -Geomatics étend _R_ avec QGIS et diverses bibliothèques de support. Vous pouvez -personnaliser votre espace de travail en fonction de vos besoins et de vos -préférences. +## Qu'est-ce que le Bureau à distance ? + +Le bureau à distance offre une expérience de bureau Ubuntu avec interface graphique dans le navigateur ainsi que l'accès rapide aux outils de support. Le système d'exploitation est +[**Ubuntu**](https://ubuntu.com/about) **22.04** avec le +[**XFCE**](https://www.xfce.org/about) pour environnement de bureau. + +
+![Bureau à distance](../images/rd_desktop.png) +
+ +## Géomatique + +Notre version de bureau à distance est construite sur une image R Geospatiale. ## Personnalisation -_pip_, _conda_, _npm_ et _yarn_ sont disponibles pour installer divers paquets. +_pip_, _conda_, _npm_ et _yarn_ sont disponibles pour installer divers packages. + +# Installation -# Accéder au Bureau virtuel +## Accéder au bureau à distance -Pour lancer le Bureau virtuel ou l'un de ses outils de support, créez un serveur -bloc-notes dans [Kubeflow](./Kubeflow.md) et sélectionnez l'une des versions -disponibles dans la liste des images. Ensuite, cliquez sur `Connecter` pour -accéder au Bureau virtuel. +Pour lancer le Bureau à distance ou l'un de ses outils de support, créez un serveur bloc-notes dans [Kubeflow](./Kubeflow.md) et sélectionnez l'option de bureau à distance, qui est l'image Ubuntu. -Un _Bureau virtuel_ vous permet d'accéder à l'interface graphique du bureau via -une session noVNC. Cliquez sur le '<' sur le côté gauche de l'écran pour ouvrir -un panneau avec des options telles qu'un plein écran et l'accès au -presse-papiers. +
+![Bureau à distance](../images/RemoteDesktop.PNG) +
+Une fois créé, cliquez sur « Connecter » pour être redirigé vers le serveur de bureau à distance + +_Remote Desktop_ vous amène à l'interface graphique du bureau via une session noVNC. Cliquer sur le > sur le côté gauche de l'écran pour développer un panneau avec des options telles que le plein écran et presse-papiers. + +
![Panneau NoVNC](../images/rd_novnc_panel.png) +
+ +## Accéder au Presse-papiers + +Cela se fait via le deuxième bouton en partant du haut du panneau à gauche. Il fait apparaître une zone de texte que nous pouvons modifier pour changer le contenu du presse-papiers ou copiez des éléments du presse-papiers du bureau distant. + +Par exemple, supposons que nous souhaitions exécuter la commande `head -c 20 /dev/urandom | somme md5` et copiez-collez le résultat dans un fichier texte sur notre ordinateur utilisé pour se connecter au bureau à distance. + +Nous ouvrons d’abord le presse-papiers à partir du panneau de gauche et collons cette commande dans la zone de texte : + +
+![Commande Coller le Presse-papiers depuis l'ordinateur](../images/rd-clipboard-send-to-rd.png) +
+ +Pour fermer la fenêtre du presse-papiers sur le bureau distant, cliquez simplement sur le bouton presse-papiers à nouveau. + +Nous faisons ensuite un clic droit sur une fenêtre de terminal pour coller cette commande et appuyons sur Entrée pour exécuter la commande. À ce stade, nous sélectionnons le résultat MD5, faisons un clic droit et cliquons copie: + +
+![Copier dans le Presse-papiers depuis le Bureau à distance](../images/rd-clipboard-copy-from-rd.png) +
+ +Si nous ouvrons à nouveau le presse-papiers depuis le panneau de gauche, il aura désormais le nouveau contenu: + +
+![Copier dans le Presse-papiers depuis le Bureau à distance](../images/rd-clipboard-copy-from-rd.png) +
+ +La fenêtre du presse-papiers sera même mise à jour sur place si nous la laissons ouverte tout le temps et nous sélectionnons simplement le nouveau matériel sur le bureau distant et appuyons à nouveau sur copier. Nous pouvons simplement copiez ce que nous avons dans cette zone de texte et collez-le dans n'importe quel autre logiciel en cours d'exécution sur l'ordinateur utilisé pour se connecter. -# Outils dans le Navigateur +## Outils dans le navigateur -## VS Code +### VSCode -Visual Studio Code est un éditeur de code source léger mais puissant. Il est -livré avec un support intégré pour JavaScript, TypeScript et Node.js et possède -un riche écosystème d'extensions pour plusieurs langages (tels que C++, C#, -Java, Python, PHP, Go). +Visual Studio Code est un éditeur de code source léger mais puissant. Il inclut la prise en charge intégrée de JavaScript, TypeScript et Node.js et dispose d'un riche écosystème d'extensions pour plusieurs langages (tels que C++, C#, Java, Python,PHP, Go). -![VS Code](../images/rd_vs_code.png) +
+![Code VS](../images/rd_vs_code.png) +
-# Notes de bas de page +## Notes de bas de page -Bureau virtuel est basé sur -[ml-tooling/ml-workspace](https://github.com/ml-tooling/ml-workspace). +Le Bureau à distance est basé sur +[ml-tooling/ml-workspace](https://github.com/ml-tooling/ml-workspace). \ No newline at end of file diff --git a/docs/fr/1-Experiences/Jupyter.md b/docs/fr/1-Experiences/Jupyter.md index 4ab6a7620..aa34d1832 100644 --- a/docs/fr/1-Experiences/Jupyter.md +++ b/docs/fr/1-Experiences/Jupyter.md @@ -2,17 +2,11 @@ ## Jupyter: Expérience conviviale de R et Python -Jupyter vous permet d'obtenir des **bloc-notes** pour écrire votre code et faire -des visualisations. Vous pouvez rapidement itérer, visualiser et partager vos -analyses. Puisque Jupyter est exécuté sur un serveur (que vous avez mis en place -dans la dernière section), il vous permet d'effectuer de très grandes analyses -sur un matériel centralisé! Ajoutez autant de puissance qu'il vous faut! Et -puisque c'est dans le nuage, vous pouvez aussi le partager avec vos collègues. +Jupyter vous propose des **bloc-notes** pour écrire votre code et réaliser des visualisations. Vous pouvez rapidement itérer, visualiser et partager vos analyses. Parce qu'il fonctionne sur un serveur (que vous avez configuré dans la section Kubeflow), vous pouvez effectuer de très grandes analyses sur du matériel centralisé, en ajoutant autant de puissance que nécessaire ! Et comme il est sur le cloud, vous pouvez également le partager avec vos collègues. ### Explorez vos données -Jupyter offre un certain nombre de fonctionnalités (et nous pouvons en ajouter -d'autres) +Jupyter offre un certain nombre de fonctionnalités (et nous pouvons en ajouter d'autres) - Éléments visuels intégrés dans votre bloc-notes - Volume de données pour le stockage de vos données @@ -20,6 +14,14 @@ d'autres) ![gadgets logiciels interactifs](../images/jupyter_visual.png) +### Explorez vos données avec une API + +
+[![Explorez vos données](../images/ExplorerDonnees.PNG)](../../2-Publication/Datasette) +
+ +Utilisez **[Datasette](../../2-Publication/Datasette)** , une interface de programmation (API) JSON instantanée pour vos bases de données SQLite. Exécutez des requêtes SQL de manière plus interactive ! + ### Environnement de développement dans le navigateur Créez pour explorer, et aussi pour écrire du code @@ -35,7 +37,7 @@ Créez pour explorer, et aussi pour écrire du code # Installation -## Commencez par les exemples +## Commencez avec les exemples Lorsque vous avez démarré votre serveur, il a été chargé de modèles de bloc-notes. Parmi les bons blocs-notes pour commencer, il y a @@ -67,32 +69,26 @@ pip install --user your_package_name de nouvelles trousses.** -??? warning "Assurez-vous de redémarrer le noyau Jupyter après l'installation d'un nouveau logiciel" - Si vous installez un logiciel dans un terminal, mais que votre noyau Jupyter - était déjà en cours d'exécution, il ne sera pas mis à jour. +??? tip "Assurez-vous de redémarrer le noyau Jupyter après l'installation d'un nouveau logiciel" + Si vous installez un logiciel dans un terminal, mais que votre noyau Jupyter était déjà en cours d'exécution, il ne sera pas mis à jour. -??? warning "Y a-t-il quelque chose que vous ne pouvez pas installer?" +??? tip "Y a-t-il quelque chose que vous ne pouvez pas installer?" Si vous avez besoin d'installer quelque chose, communiquez avec nous - ou [ouvrir une question GitHub](https://github.com/StatCan/aaw-kubeflow-containers). - Nous pouvons l'ajouter au logiciel par défaut. + ou [ouvrir une question GitHub](https://github.com/StatCan/aaw-kubeflow-containers). Nous pouvons l'ajouter au logiciel par défaut. # Une fois que vous avez les bases ... ## Entrer et sortir des données de Jupyter Vous pouvez télécharger et charger des données vers ou depuis JupyterHub -directement dans le menu. Il y a un bouton de chargement en haut, et vous pouvez -cliquer avec le bouton droit de la souris sur la plupart des fichiers ou -dossiers pour les télécharger. +directement dans le menu. Il y a un bouton de chargement en haut, et vous pouvez cliquer avec le bouton droit de la souris sur la plupart des fichiers ou dossiers pour les télécharger. ### Stockage partagé en compartiment -Il y a aussi un dossier `minio` monté dans votre répertoire personnel qui -contient les fichiers dans [MinIO](../Stockage.md). +Il existe également un dossier `buckets` monté dans votre répertoire personnel, qui contient les fichiers dans [Stockage Blob Azure](../../5-Stockage/StockageBlobAzure). -**Consultez la [section sur le stockage](../index.md#storage) rubrique pour plus -de détails** +**Reportez-vous à la section [Stockage](../../5-Stockage/Aperçu) pour plus de détails.** ## L'analyse des données @@ -104,4 +100,8 @@ L'analyse des données est le processus d'examen et d'interprétation de grandes _Traiter les données à l'aide de R, Python ou Julia dans JupyterLab_ +
+[![R, Python, ou Julia dans JupyterLab](../images/jupyter_in_action.png)](../1-Experiences/Jupyter.md) +
+ Le traitement des données à l'aide de R, Python ou Julia est simplifié grâce à l'espace de travail d'analyse avancée. Que vous débutiez dans l'analyse de données ou que vous soyez un data scientist expérimenté, notre plateforme prend en charge une gamme de langages de programmation pour répondre à vos besoins. Vous pouvez installer et exécuter des packages pour R ou Python pour effectuer des tâches de traitement de données telles que le nettoyage, la transformation et la modélisation des données. Si vous préférez Julia, notre plateforme offre également un support pour ce langage de programmation. diff --git a/docs/fr/1-Experiences/Kubeflow.md b/docs/fr/1-Experiences/Kubeflow.md index 8c68de0d6..e102048be 100644 --- a/docs/fr/1-Experiences/Kubeflow.md +++ b/docs/fr/1-Experiences/Kubeflow.md @@ -12,24 +12,22 @@ votre équipe. **Commençons sans plus tarder!** -# Créer un serveur - -## Se connecter à Kubeflow - + + !!! note "" Cette vidéo n'est pas à jour, certaines choses pourraient avoir changé depuis. [![Click here for the video](../images/KubeflowVideo.PNG)](https://www.youtube.com/watch?v=xaI6ExYdxc4&list=PL1zlA2D7AHugkDdiyeUHWOKGKUd3MB_nD&index=1 "Espace de travail d'analyse avancée - Kubeflow") +--> # Installation ## Connectez-vous à Kubeflow -!!! avertissement "Connectez-vous au portail Azure à l'aide de vos identifiants cloud" +!!! warning "Connectez-vous au portail Azure à l'aide de vos identifiants cloud" Vous devez vous connecter au portail Azure ** en utilisant vos informations d'identification StatCan **.`first.lastname@cloud.statcan.ca` ou ** en utilisant vos informations d'identification StatCan ** `first.lastname@statcan.gc.ca`. Vous pouvez le faire en utilisant [Portail Azure](https://portal.azure.com). ![Azure Portal: Choisir l'adresse `@cloud.statcan.ca` ](../images/azure-login.png) @@ -48,13 +46,20 @@ votre équipe. :** le nom de votre serveur doit être en lettres minuscules avec des tirets. **Pas d'espaces, et non souligne.** -- Vous devrez choisir une image. Vérifiez le nom des images et choisissez-en une - qui correspond à ce que tu veux faire. (Vous ne savez pas lequel choisir ? - Vérifiez vos options [ici](./Selecting-an-Image.md).) +- Vous devrez spécifier un espace de noms. Par défaut, vous disposerez d'un espace de noms par défaut pour votre compte, mais pour les projets, vous devrez peut-être sélectionner l'espace de noms créé spécifiquement pour ce projet. Sinon, le serveur de notebooks que vous créez risque de ne pas disposer des droits d'accès aux ressources requises pour le projet. -![Choisissez une image](../images/select-image-screenshot.PNG) +## Image -- Si vous souhaitez utiliser un GPU, vérifiez si l'image indique `cpu` ou `gpu`. +Vous devrez choisir une image. Il existe JupyterLab, RStudio, Ubuntu distant +images de bureau et SAS disponibles. L'image SAS est disponible uniquement pour StatCan +employés (en raison des limitations de licence), les autres sont disponibles pour tout le monde. +Sélectionnez le menu déroulant pour sélectionner des options supplémentaires parmi celles-ci (par exemple +instance, images CPU, PyTorch et TensorFlow pour JupyterLab). + +Vérifiez le nom des images et choisissez-en une qui correspond à ce que vous souhaitez faire. Je ne sais pas +lequel choisir ? Découvrez vos options [ici](../Selectionner-une-image). + +![Choisir une image](../images/select-image-screenshot.PNG) ### Image personnalisée @@ -86,34 +91,74 @@ Cela vous permet de répondre à vos besoins de calcul tout en minimisant les coûts. Pour un serveur portable GPU, vous obtiendrez toujours le serveur complet (6 cœurs CPU, 96 Gio de mémoire accessible et 1 GPU). -À l'avenir, il se peut que des machines plus grandes soient disponibles, vous -pourriez donc avoir des restrictions plus souples. +Dans les options avancées, vous pouvez sélectionner une limite supérieure au nombre de cœurs de processeur et de RAM demandés. Le montant demandé est le montant garanti disponible pour votre serveur notebook et vous paierez toujours au moins ce montant. Si la limite est supérieure à la quantité demandée, si des cœurs de RAM et de processeur supplémentaires sont disponibles sur ce serveur partagé dans le cluster, votre serveur de notebook peut les utiliser selon vos besoins. Un cas d’utilisation est celui des tâches qui ne nécessitent généralement qu’un seul cœur de processeur mais qui peuvent bénéficier du multithreading pour accélérer certaines opérations. En demandant un cœur de processeur mais une limite plus élevée, vous pouvez payer beaucoup moins pour le serveur d'ordinateurs portables tout en lui permettant d'utiliser des cœurs de processeur de rechange inutilisés si nécessaire pour accélérer les calculs. - -!!! note "Bogue de création de nœud lent." - En raison d'un bug avec le pare-feu, la création d'un nouveau nœud peut être très - lente dans certains cas (jusqu'à quelques heures). Un correctif pour ce problème est en cours. +![Choisir CPU et RAM](../images/cpu-ram.PNG) + + +## GPUs + +Si vous voulez un serveur GPU, sélectionnez « 1 » comme nombre de GPU et « NVIDIA » comme GPU +fournisseur (le bouton de création sera grisé jusqu'à ce que le fournisseur de GPU soit sélectionné si +vous avez un GPU spécifié). Les serveurs multi-GPU sont actuellement pris en charge sur l'AAW +système uniquement sur une base spéciale sur demande, veuillez contacter les mainteneurs d'AAW si +vous souhaitez un serveur multi-GPU. + +![Configuration GPU](../images/kubeflow_gpu_selection.PNG) + +Comme mentionné précédemment, si vous sélectionnez un serveur GPU, vous obtiendrez automatiquement 6 CPU +cœurs et 112 Go de mémoire. !!! note "Utilisez les machines GPU de manière responsable" - Il y a moins de machines GPU que de machines CPU, alors utilisez-les de manière responsable. + Les machines GPU sont nettement plus chères que les machines CPU, + alors utilisez-les de manière responsable. -![Choisir CPU et RAM](../images/cpu-ram.PNG) +## Volume d'espace de travail -## Stockage de vos données +Vous aurez besoin d'un volume d'espace de travail, sur lequel le dossier personnel sera monté. Là +différentes options de configuration sont disponibles : --Vous aurez envie de créer un volume de données ! Vous pourrez enregistrer votre -travail ici, et si vous éteignez votre serveur, vous pourrez simplement remonter -vos anciennes données en entrant le nom de votre ancien disque. **Il est -important que vous vous souveniez du nom du volume.** +- Vous pouvez soit réutiliser un volume d'espace de travail existant auparavant, soit en créer un nouveau. -![Créer un volume de données](../images/kubeflow_volumes.png) +- Vous pouvez spécifier la taille du volume de l'espace de travail, de 4 Gio à 32 Gio. + +![Créer un volume d'espace de travail](../images/workspace-volume.PNG) !!! conseil "Vérifiez les anciens volumes en regardant l'option Existant" - Lorsque vous créez votre serveur vous avez la possibilité de réutiliser un ancien volume - ou en créer un nouveau. Vous souhaitez probablement réutiliser votre ancien volume. + Lorsque vous créez votre serveur vous avez la possibilité de réutiliser un ancien volume + ou en créer un nouveau. Vous souhaitez probablement réutiliser votre ancien volume. + +## Volumes de données + +Vous pouvez également créer des volumes de données pouvant être utilisés pour stocker des données supplémentaires. Plusieurs +des volumes de données peuvent être créés. Cliquez sur le bouton Ajouter un nouveau volume pour créer un nouveau volume et +préciser sa configuration. Cliquez sur le bouton Attacher un volume existant pour monter un volume existant. +volume de données vers le serveur de notebook. Il existe les paramètres de configuration suivants pour +volumes de données : + +- **Nom** : Nom du volume. + +- **Taille en GiB** : De 4 Gio à 512 Gio. + +- **Chemin de montage** : Chemin où le volume de données est accessible sur le serveur notebook, par + par défaut `/home/jovyan/vol-1`, `/home/jovyan/vol-2`, etc. (compteur incrémentiel par données + volume monté). +Lors du montage d'un volume de données existant, l'option de nom devient une liste déroulante du +volumes de données existants. Uniquement un volume non actuellement monté sur un serveur de notebook existant +peut être utilisé. L'option du chemin de montage reste configurable par l'utilisateur avec les mêmes valeurs par défaut que +créer un nouveau volume. + +L'icône de la poubelle à droite peut être utilisée pour supprimer un fichier existant ou créé accidentellement. +volume de données. + +![Créer un volume de données](../images/kubeflow_volumes.png) + +## Configuration + +Si des configurations sont disponibles, elles sont présentées ici sous forme de cases à cocher. Actuellement, aucune configuration n'existe. ## Paramètres divers @@ -125,6 +170,8 @@ Les éléments suivants peuvent être personnalisés ici : cela nécessite une mémoire partagée. - **Langue du système** : Vous pouvez spécifier ici l'anglais ou le français. +![Paramètres divers](../images/misc-settings.PNG) + ## Et... Créer!!! - Si vous êtes satisfait des paramètres, vous pouvez maintenant créer le serveur @@ -132,16 +179,13 @@ Les éléments suivants peuvent être personnalisés ici : ressources que vous avez demandées. (GPU prendre plus de temps.) -!!! Succès "Votre serveur est en cours d'exécution" +!!! success "Votre serveur est en cours d'exécution" Si tout se passe bien, votre serveur devrait fonctionner !!! Vous aurez maintenant le - possibilité de se connecter, et [essayer Jupyter!](/daaas/en/1-Experiments/Jupyter) + possibilité de se connecter, et [essayer Jupyter!](../Jupyter) # Une fois que vous avez les bases... ## Partagez votre espace de travail -Dans Kubeflow, chaque utilisateur dispose d'un **espace de noms** qui contient -son travail (son serveurs de blocs-note, pipelines, disques, etc.). Votre espace -de nom vous appartient, mais peut être partagé si vous souhaitez collaborer avec -d'autres. **Pour plus de détails sur collaboration sur la plateforme, voir -[Collaboration](../4-Collaboration/Overview.md).** +Dans Kubeflow, chaque utilisateur dispose d'un **espace de noms** qui contient son travail (son serveurs de blocs-note, pipelines, disques, etc.). Votre espace de nom vous appartient, mais peut être partagé si vous souhaitez collaborer avec d'autres. **Pour plus de détails sur collaboration sur la plateforme, voir +[Collaboration](../4-Collaboration/Aperçu.md).** diff --git a/docs/fr/1-Experiences/RStudio.md b/docs/fr/1-Experiences/RStudio.md index 7f14b3df1..07896b8b9 100644 --- a/docs/fr/1-Experiences/RStudio.md +++ b/docs/fr/1-Experiences/RStudio.md @@ -1,15 +1,39 @@ -# RStudio +# Aperçu -Vous pouvez utiliser l'image `rstudio` pour obtenir un environnement RStudio! +RStudio est un environnement de développement intégré (IDE) pour R. Il comprend un console, éditeur et outils de traçage, d'historique, de débogage et d'espace de travail gestion. + +# Didacticiel vidéo + +[![Cliquez ici pour voir la vidéo](../images/KubeflowVideo.PNG)](https://www.youtube.com/watch?v=Xrk1kN9Lr_4&list=PL1zlA2D7AHugkDdiyeUHWOKGKUd3MB_nD&index=3 "Advanced Analytics Workspace - R-Studio Basics") + +# Installation + +Vous pouvez utiliser l'image `rstudio` pour obtenir un environnement RStudio ! Lorsque vous créez votre bloc-notes, choisissez RStudio dans la liste des images disponibles. + +![Menu RStudio](../images/RStudioOption.PNG) ![RStudio](../images/rstudio_visual.png) -Vous pouvez installer les paquets `R` ou `python` avec `conda` ou -`install.packages()`. +Vous pouvez installer les packages `R` ou `python` avec `conda` ou `install.packages()`. + +# Une fois que vous avez les bases... + +## R-Brillant + +Vous pouvez aussi utiliser « Shiny » ! Shiny est un package R open source qui fournit un web +cadre pour créer des applications Web à l'aide de R. Shiny vous aide à transformer votre +analyses en applications Web interactives. + +![RStudio avec R-Shiny](../images/rstudio_rshiny_visual.png) + +### R Studio + +_Traiter les données à l'aide de R ou Python dans R Studio_ -# R-Shiny +
+[![R ou Python dans R Studio](../images/RStudio.PNG)](../1-Experiences/RStudio.md) +
-Vous pouvez aussi utiliser `Shiny`! Et le tableau de bord apparaîtra dans une -nouvelle fenêtre. +R Studio est un puissant environnement de développement intégré (IDE) qui prend en charge les langages de programmation R et Python, ce qui en fait un choix idéal pour les analystes de données et les scientifiques. Avec R Studio, vous pouvez effectuer un large éventail de tâches de traitement de données, du nettoyage et de la transformation des données à l'analyse statistique et à l'apprentissage automatique. Le logiciel fournit une interface conviviale et une variété d'outils et de bibliothèques qui simplifient les tâches complexes d'analyse de données. De plus, R Studio facilite le partage de votre travail avec d'autres en créant des rapports et des visualisations dynamiques et interactifs qui peuvent être publiés en ligne ou partagés sous forme de documents autonomes. Dans l'ensemble, R Studio est un outil polyvalent et puissant, essentiel pour toute personne travaillant avec des données en R ou Python. -![RStudio with R-Shiny](../images/rstudio_rshiny_visual.png) +**[R Studio](../1-Experiences/RStudio.md)** vous offre un environnement de développement intégré pour R et Python. Utilisez l'image r-studio-cpu pour obtenir un environnement R Studio. \ No newline at end of file diff --git a/docs/fr/1-Experiences/Selectionner-une-Image.md b/docs/fr/1-Experiences/Selectionner-une-Image.md deleted file mode 100644 index 706961483..000000000 --- a/docs/fr/1-Experiences/Selectionner-une-Image.md +++ /dev/null @@ -1,64 +0,0 @@ -# Sélectionner une Image pour votre Serveur bloc-notes - -Selon votre projet ou votre utilisation souhaitée du serveur bloc-notes, -certaines images seront plus appropriées que d'autres. - -Ce qui suit passera en revue quelques caractéristiques de chaque image pour vous -aider à choisir. - -Lors de la sélection, vous avez 3 options principales: - -- Bloc-notes Jupyter (CPU, TensorFlow, PyTorch) -- RStudio -- Bureau virtuel (r, Geomatics) - -## Jupyter - -Les [Bloc-notes Jupyter](https://jupyter.org/) sont utilisés pour créer et -partager des documents interactifs qui contiennent un mélange de code en direct, -de visualisations et de texte. Ceux-ci peuvent être écrits en `Python`,` Julia` -ou `R`. - -![Bloc-notes Jupyter](../images/jupyter_in_action.png) - - -??? info "La plupart des utilisations comprennent:" - la transformation de données, la simulation numérique, la modélisation statistique, l'apprentissage automatique et autres. - -Ceux-ci s'agit d'un excellent outil pour l'analyse, y compris l'apprentissage -machine. L'image `jupyterlab-cpu` fournit une bonne expérience de base pour -`python`, y compris des paquets tels que `numpy`, `pandas` et `scikit-learn`. Si -vous êtes intéressé spécifiquement par **_TensorFlow_** ou **_PyTorch_**, nous -avons également les images `jupyterlab-tensorflow` et` jupyterlab-pytorch` qui -viennent avec ces outils pré-installés. - -Pour l'image `jupyterlab-pytorch`, les packages PyTorch (torch, torchvision et torchaudio) sont installés dans l'environnement conda `torch`. Vous devez activer cet environnement pour utiliser PyTorch. - -Pour les images `jupyterlab-cpu`, `jupyterlab-tensorflow` et `jupyterlab-pytorch`, dans le shell par défaut, la commande `conda activate` peut ne pas fonctionner. Cela est dû au fait que l'environnement n'est pas correctement initialisé. Dans ce cas, exécutez « bash », vous devriez voir le logo AAW et quelques instructions apparaissent. Après cela, "conda activate" devrait fonctionner correctement. Si vous voyez le logo AAW au démarrage, cela signifie que l'environnement est correctement initialisé et que « conda activate » devrait fonctionner correctement. Un correctif pour ce bogue est en préparation, une fois cela corrigé, ce paragraphe sera supprimé. - -Chaque image est préchargée avec VS Code dans le navigateur si vous préférez une -expérience IDE complète. - -### RStudio - -**[RStudio](RStudio/)** vous offre un environnement de développement intégré -spécifiquement pour `R`. Si vous codez en `R`, il s'agit généralement du serveur -bloc-notes utiliser. Utilisez l'image `rstudio` pour obtenir un environnement -RStudio. - -![RStudio](../images/rstudio_visual.png) - -### Bureau virtuel - -Pour une expérience Ubuntu complète, deux versions du Bureau virtuel sont -disponible. Ceux-ci sont préchargés avec Python et R, mais sont livrés dans un -expérience typique qui est également fournie avec _Firefox_, _VS Code_ et les -outils _Open Office_. Le système d'exploitation est -**[Ubuntu](https://ubuntu.com/about)** 18.04 avec l'environnement de bureau -**[XFCE](https://www.xfce.org/about)**. - -`remote-desktop-r` inclut R et RStudio mais si vous avez besoin d'outils de -géomatique pour R, choisissez la version `remote-desktop-geomatics` de cette -image. - -![Capture d'écran d'un bureau virtuel](../images/rd_desktop.png) diff --git a/docs/fr/1-Experiences/Selectionner-une-image.md b/docs/fr/1-Experiences/Selectionner-une-image.md new file mode 100644 index 000000000..d73b27198 --- /dev/null +++ b/docs/fr/1-Experiences/Selectionner-une-image.md @@ -0,0 +1,42 @@ +# Sélection d'une image pour votre serveur Notebook + +En fonction de votre projet ou du cas d'utilisation du Notebook Server, certaines images peuvent être plus adapté que d'autres. Ce qui suit passera en revue les principales caractéristiques de chacun pour vous aider à choisir l’image la plus appropriée pour vous. + +Lors de la sélection d'une image, vous disposez de 3 options principales : + +- Carnet Jupyter (CPU, TensorFlow, PyTorch) +- RStudio +- Bureau à distance + +## Bloc-notes Jupyter + +Les [bloc-notes Jupyter](https://jupyter.org/) sont utilisés pour créer et partager documents interactifs contenant un mélange de code en direct, de visualisations et de texte. Ceux-ci peuvent être écrits en `Python`, `Julia` ou `R`. + +
+![Bloc-notes Jupyter](../images/jupyter_in_action.png) +
+ + +??? info "Les utilisations courantes incluent :" + transformation de données, simulation numérique, statistique + modélisation, apprentissage automatique et bien plus encore. + +Les notebooks Jupyter sont d'excellents tremplins pour l'analyse, y compris les apprentissage automatique. L'image `jupyterlab-cpu` donne une bonne expérience de base pour python, y compris des packages courants tels que `numpy`, `pandas` et `scikit-learn`. Si vous souhaitez spécifiquement utiliser **_TensorFlow_** ou **_PyTorch_**, nous ont également `jupyterlab-tensorflow` et `jupyterlab-pytorch` qui viennent avec ceux outils préinstallés. + +Pour l'image `jupyterlab-pytorch`, les packages PyTorch (torch, torchvision, et torchaudio) sont installés dans l'environnement conda `torch`. Vous devez activez cet environnement pour utiliser PyTorch. + +Pour `jupyterlab-cpu`, `jupyterlab-tensorflow` et `jupyterlab-pytorch` images, dans le shell par défaut, la commande `conda activate` peut ne pas fonctionner. C'est en raison de l'environnement qui n'est pas initialisé correctement. Dans ce cas, lancez `bash`, vous devriez voir le logo AAW et quelques instructions apparaître. Après ça `conda activate` devrait fonctionner correctement. Si vous voyez le logo AAW au démarrage signifie que l'environnement est correctement initialisé et que `conda activate` devrait fonctionner correctement. Un correctif pour ce bug est en préparation, une fois celui-ci corrigé, ce paragraphe sera supprimé. + +Chaque image est préchargée avec VS Code dans le navigateur si vous préférez une expérience IDE complète. + +## RStudio + +**[RStudio](../RStudio/)** vous offre un environnement de développement intégré spécifiquement pour `R`. Si vous codez en `R`, il s'agit généralement du Serveur Bloc-notesà utiliser. Utilisez l'image `rstudio` pour obtenir un environnement RStudio. + +![RStudio](../images/rstudio_visual.png) + +## Bureau à distance + +Pour une expérience de bureau Ubuntu complète, utilisez l'image du bureau à distance. Elle est préchargée avec les outils Python, R et Geomatics, mais dans une expérience de bureau typique qui comprend également Firefox, VS Code et les outils Open Office. Le système d'exploitation est **[Ubuntu](https://ubuntu.com/about)** 22.04 avec l'environnement de bureau **[XFCE](https://www.xfce.org/about)**. + +![Capture d'écran du bureau virtuel](../images/rd_desktop.png) \ No newline at end of file diff --git a/docs/fr/5-Stockage/AzureBlobStorage.md b/docs/fr/5-Stockage/StockageBlobAzure.md similarity index 100% rename from docs/fr/5-Stockage/AzureBlobStorage.md rename to docs/fr/5-Stockage/StockageBlobAzure.md diff --git a/docs/fr/images/KubeflowVideo.PNG b/docs/fr/images/KubeflowVideo.PNG new file mode 100644 index 0000000000000000000000000000000000000000..d4224d27c91b4c540d29921975b7558e9064ca92 GIT binary patch literal 184083 zcmYhj1z1&G7cDH^-JQ}U-EaVuA(H3hD?n7=FJ;4Ss5v{H*ese-n@aHL4*dr^Md&!3it%&q9!fz zrs_B8KJWt80;C9f^QI;i;+?*pg^ zC|Ect&XEL=aX8AziP8zj3f}YD?wyFirK^^f@{Gdx(df}>U1z1&!O-=eX^ruwY~=pl zUa62zgs&g&Pzu+n|L-NfkTp4f>d551@GlP3Z4*HdChTw@2Wrp%`KUwbx$OGi*F7Ag zRnRuZuRn(HvsM5P$NzmB@NeA+2bC)Ge{V&t^W5hGIvOPB{qS{E{-t 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+++ b/mkdocs-fr.yml @@ -63,6 +63,7 @@ extra: nav: - Démarrage: index.md - Analyse: + - Aperçu: 1-Experiences/Apercu.md - Kubeflow: 1-Experiences/Kubeflow.md - Jupyter: 1-Experiences/Jupyter.md - RStudio: 1-Experiences/RStudio.md From 012c784180de11b67338d04ee1b122924286a4c0 Mon Sep 17 00:00:00 2001 From: Wendy V Gaultier Date: Fri, 27 Oct 2023 13:47:29 +0000 Subject: [PATCH 03/15] Publication folder --- docs/en/2-Publishing/Custom.md | 12 ++++------ docs/en/2-Publishing/Datasette.md | 25 ++++++--------------- docs/en/2-Publishing/PowerBI.md | 17 +++++--------- docs/en/2-Publishing/R-Shiny.md | 4 +++- docs/fr/2-Publication/Dash.md | 2 +- docs/fr/2-Publication/Datasette.md | 30 ++++++++++++------------- docs/fr/2-Publication/Overview.md | 12 +++++----- docs/fr/2-Publication/PowerBI.md | 33 ++++++++++------------------ docs/fr/2-Publication/R-Shiny.md | 28 ++++++++++++----------- docs/fr/2-Publication/Sur-mesure.md | 21 +++++++----------- docs/fr/images/InstallDatasette.PNG | Bin 0 -> 4397 bytes docs/fr/images/Publier.png | Bin 4495 -> 4157 bytes 12 files changed, 76 insertions(+), 108 deletions(-) create mode 100644 docs/fr/images/InstallDatasette.PNG diff --git a/docs/en/2-Publishing/Custom.md b/docs/en/2-Publishing/Custom.md index 660f932ad..f53282876 100644 --- a/docs/en/2-Publishing/Custom.md +++ b/docs/en/2-Publishing/Custom.md @@ -2,8 +2,7 @@ ## Custom Web Apps -We can deploy anything as long as it's open source and we can put it in a Docker -container. For instance, Node.js apps, Flask or Dash apps. Etc. +We can deploy anything as long as it's open source and we can put it in a Docker container. For instance, Node.js apps, Flask or Dash apps. Etc. ![Example of a Node.js App](../images/readme/covid_ui.png) @@ -11,13 +10,10 @@ container. For instance, Node.js apps, Flask or Dash apps. Etc. !!! info "See the source code for this app" We just push these kinds of applications through GitHub into the server. -# Setup +## Setup -## How to get your app hosted +### How to get your app hosted -If you already have a web app in a git repository then, as soon as it's -containerized, we can fork the Git repository into the StatCan GitHub repository -and point a URL to it. To update it, you'll just interact with the StatCan -GitHub repository with Pull Requests. +If you already have a web app in a git repository then, as soon as it's containerized, we can fork the Git repository into the StatCan GitHub repository and point a URL to it. To update it, you'll just interact with the StatCan GitHub repository with Pull Requests. **Contact us if you have questions.** diff --git a/docs/en/2-Publishing/Datasette.md b/docs/en/2-Publishing/Datasette.md index 56724c164..1d8091d68 100644 --- a/docs/en/2-Publishing/Datasette.md +++ b/docs/en/2-Publishing/Datasette.md @@ -1,23 +1,17 @@ # Overview -Datasette is an instant JSON API for your SQLite databases allowing you to -explore the DB and run SQL queries in a more interactive way. +Datasette is an instant JSON API for your SQLite databases allowing you to explore the DB and run SQL queries in a more interactive way. You can find a list of example datasettes [here](https://github.com/simonw/datasette/wiki/Datasettes). !!! faq "The Datasette Ecosystem" - There are all sorts of tools for converting data to and from sqlite - [here](https://docs.datasette.io/en/stable/ecosystem.html). For example, - you can load shapefiles into sqlite, or create [Vega](https://vega.github.io/vega/) - plots from a sqlite database. SQLite works well with `R`, `Python`, and many other tools. + There are all sorts of tools for converting data to and from sqlite [here](https://docs.datasette.io/en/stable/ecosystem.html). For example, you can load shapefiles into sqlite, or create [Vega](https://vega.github.io/vega/) plots from a sqlite database. SQLite works well with `R`, `Python`, and many other tools. ## Example Datasette -Below are some screenshots from the -[global-power-plants](https://global-power-plants.datasettes.com) Datasette, you -can preview and explore the data in the browser, either with clicks or SQL +Below are some screenshots from the [global-power-plants](https://global-power-plants.datasettes.com) Datasette, you can preview and explore the data in the browser, either with clicks or SQL queries. ![Preview Data](../images/datasette-preview.png) @@ -35,19 +29,15 @@ You can even explore maps within the tool! ## Installing Datasette -In your Jupyter Notebook, open a terminal window and run the command -`pip3 install datasette`. +In your Jupyter Notebook, open a terminal window and run the command `pip3 install datasette`. +

![Install Datasette](../images/InstallDatasette.PNG)
## Starting Datasette -To view your own database in your Jupyter Notebook, create a file called -start.sh in your project directory and copy the below code into it. Make the -file executable using `chmod +x start.sh`. Run the file with `./start.sh`. -Access the web server using the **base URL** with the port number you are using -in the below file. +To view your own database in your Jupyter Notebook, create a file called start.sh in your project directory and copy the below code into it. Make the file executable using `chmod +x start.sh`. Run the file with `./start.sh`. Access the web server using the **base URL** with the port number you are using in the below file. **start.sh** @@ -78,8 +68,7 @@ datasette $DATABASE --cors --config max_returned_rows:100000 --config sql_time_l you will not be able to simply access it from `http://localhost:5000/` as normally suggested in the output upon running the web-app. - To access the web server you will need to use the base URL. In your notebook - terminal, run: + To access the web server you will need to use the base URL. In your notebook terminal, run: ```python echo https://kubeflow.covid.cloud.statcan.ca${JUPYTER_SERVER_URL:19}proxy/5000/ diff --git a/docs/en/2-Publishing/PowerBI.md b/docs/en/2-Publishing/PowerBI.md index f805b89ff..c0ed1cfba 100644 --- a/docs/en/2-Publishing/PowerBI.md +++ b/docs/en/2-Publishing/PowerBI.md @@ -2,8 +2,7 @@ ## Loading data into Power BI -We do not offer a Power BI server, but you can pull your data into Power BI from -our Storage system, and use the data as a `pandas` data frame. +We do not offer a Power BI server, but you can pull your data into Power BI from our Storage system, and use the data as a `pandas` data frame. ![Power BI Dashboard](../images/powerbi_dashboard.png) @@ -17,22 +16,16 @@ our Storage system, and use the data as a `pandas` data frame. ## Set up Power BI -Open up your Power BI system, and open up this -[Power BI quick start](https://raw.githubusercontent.com/StatCan/aaw-contrib-jupyter-notebooks/master/querySQL/power_bi_quickstart.py) -in your favourite text editor. +Open up your Power BI system, and open up this [Power BI quick start](https://raw.githubusercontent.com/StatCan/aaw-contrib-jupyter-notebooks/master/querySQL/power_bi_quickstart.py) in your favourite text editor. -You'll have to make sure that `pandas`, `boto3`, and `numpy` are installed, and -that you're using the right Conda virtual environment (if applicable). +You'll have to make sure that `pandas`, `boto3`, and `numpy` are installed, and that you're using the right Conda virtual environment (if applicable). ![Install the dependencies](../images/powerbi_cmd_prompt.png) -You'll then need to make sure that Power BI is using the correct Python -environment. This is modified from the options menu, and the exact path is -specified in the quick start guide. +You'll then need to make sure that Power BI is using the correct Python environment. This is modified from the options menu, and the exact path is specified in the quick start guide. ## Edit your python script -Then, edit your Python script to use your MinIO `ACCESS_KEY` and `SECRET_KEY`, -and then click "Get Data" and copy it in as a Python Script. +Then, edit your Python script to use your MinIO `ACCESS_KEY` and `SECRET_KEY`, and then click "Get Data" and copy it in as a Python Script. ![Run your Python Script](../images/powerbi_python.png) diff --git a/docs/en/2-Publishing/R-Shiny.md b/docs/en/2-Publishing/R-Shiny.md index 52c1382b6..6673a62bd 100644 --- a/docs/en/2-Publishing/R-Shiny.md +++ b/docs/en/2-Publishing/R-Shiny.md @@ -12,7 +12,9 @@ R-Shiny is an R package that makes it easy to build interactive web apps in R. _Publish Professional Quality Graphics_ +
[![InteractiveDashboard](../images/InteractiveDashboard.PNG)](../R-Shiny/) +
R Shiny is an open source web application framework that allows data scientists and analysts to create interactive, web-based dashboards and data visualizations using the R programming language. One of the main advantages of R Shiny is that it offers a straightforward way to create high-quality, interactive dashboards without the need for extensive web development expertise. With R Shiny, data scientists can leverage their R coding skills to create dynamic, data-driven web applications that can be shared easily with stakeholders. @@ -22,7 +24,7 @@ Another advantage of R Shiny is that it supports a variety of data visualization R Shiny is also highly extensible and can be integrated with other open source tools and platforms to build end-to-end data science workflows. With its powerful and flexible features, R Shiny is a popular choice for building data visualization dashboards for a wide range of applications, from scientific research to business analytics. Overall, R Shiny offers a powerful, customizable, and cost-effective solution for creating interactive dashboards and data visualizations. -Use **[R-Shiny](/2-Publishing/R-Shiny/)** to build interactive web apps straight from R. You can deploy your R Shiny dashboard by submitting a pull request to our [R-Dashboards GitHub repository](https://github.com/StatCan/R-dashboards). +Use **[R-Shiny](../R-Shiny/)** to build interactive web apps straight from R. You can deploy your R Shiny dashboard by submitting a pull request to our [R-Dashboards GitHub repository](https://github.com/StatCan/R-dashboards). # R Shiny UI Editor diff --git a/docs/fr/2-Publication/Dash.md b/docs/fr/2-Publication/Dash.md index 80b552e94..5e75ab716 100644 --- a/docs/fr/2-Publication/Dash.md +++ b/docs/fr/2-Publication/Dash.md @@ -31,7 +31,7 @@ Ceci est un exemple de mise en page avec figure et curseur de _Publier avec des logiciels fabriqués au Canada._ -**[Plotly Dash](/2-Publishing/Dash/)** est une bibliothèque Python populaire qui vous permet de créer facilement des visualisations et des tableaux de bord Web interactifs. Développé par la société montréalaise Plotly, Dash a acquis la réputation d'être un outil puissant et flexible pour créer des graphiques de science des données personnalisés. Avec Dash, vous pouvez tout créer, des simples graphiques linéaires aux tableaux de bord complexes de plusieurs pages avec des widgets et des commandes interactifs. Parce qu'il repose sur des technologies open source telles que Flask, React et Plotly.js, Dash est hautement personnalisable et peut être facilement intégré à d'autres outils et workflows de science des données. Que vous soyez data scientist, analyste ou développeur, Dash peut vous aider à créer des visualisations attrayantes et informatives qui donnent vie à vos données. +**[Plotly Dash](../Dash/)** est une bibliothèque Python populaire qui vous permet de créer facilement des visualisations et des tableaux de bord Web interactifs. Développé par la société montréalaise Plotly, Dash a acquis la réputation d'être un outil puissant et flexible pour créer des graphiques de science des données personnalisés. Avec Dash, vous pouvez tout créer, des simples graphiques linéaires aux tableaux de bord complexes de plusieurs pages avec des widgets et des commandes interactifs. Parce qu'il repose sur des technologies open source telles que Flask, React et Plotly.js, Dash est hautement personnalisable et peut être facilement intégré à d'autres outils et workflows de science des données. Que vous soyez data scientist, analyste ou développeur, Dash peut vous aider à créer des visualisations attrayantes et informatives qui donnent vie à vos données. # Commencer diff --git a/docs/fr/2-Publication/Datasette.md b/docs/fr/2-Publication/Datasette.md index 51eb9e539..ec8472866 100644 --- a/docs/fr/2-Publication/Datasette.md +++ b/docs/fr/2-Publication/Datasette.md @@ -1,8 +1,6 @@ # Aperçu -Datasette est une API JSON instantanée pour vos bases de données SQLite qui -permet d'explorer la BD et d'exécuter des requêtes SQL de manière plus -interactive. +Datasette est une API JSON instantanée pour vos bases de données SQLite qui permet d'explorer la BD et d'exécuter des requêtes SQL de manière plus interactive. Vous pouvez trouver une liste d'exemples de datasettes [ici](https://github.com/simonw/datasette/wiki/Datasettes). @@ -13,30 +11,32 @@ Vous pouvez trouver une liste d'exemples de datasettes ## Exemple Datasette -Voici quelques captures d'écran du Datasette -[global-power-plants](https://global-power-plants.datasettes.com), vous pouvez -prévisualiser et explorer les données dans le navigateur, que ce soit par des +Voici quelques captures d'écran du Datasette [global-power-plants](https://global-power-plants.datasettes.com), vous pouvez prévisualiser et explorer les données dans le navigateur, que ce soit par des clics ou des requêtes SQL. ![Prévisualisation des données](../images/datasette-preview.png) Vous pouvez même explorer des cartes au sein de l'outil! -[Exécuter des requêtes SQL](../images/datasette-sql.png) +![Exécuter des requêtes SQL](../images/datasette-sql.png) -# Didacticiel vidéo + # Commencer -## Installation des Datasettes +## Installation de l'ensemble de données -Pour visualiser votre base de données dans votre bloc-notes Jupyter, créez le -fichier _bash_ suivant dans votre répertoire de projet, rendre le fichier -exécutable en utilisant `chmod +x start.sh` puis exécutez-le avec la commande -`./start.sh`. Accédez au serveur web en utilisant le **URL de base** avec le -numéro de port que vous utilisez dans le fichier ci-dessous. +Dans votre Jupyter Notebook, ouvrez une fenêtre de terminal et exécutez la commande `pip3 install datasette`. + +
+ ![Installer l'ensemble de données](../images/InstallDatasette.PNG) +
+ +## Démarrage de l'ensemble de données + +Pour afficher votre propre base de données dans votre bloc-notes Jupyter, créez un fichier appelé `start.sh` dans le répertoire de votre projet et copiez-y le code ci-dessous. Rendez le fichier exécutable en utilisant `chmod +x start.sh`. Exécutez le fichier avec `./start.sh`. Accédez au serveur Web à l'aide de l'**URL de base** avec le numéro de port que vous utilisez dans le fichier ci-dessous. **start.sh** diff --git a/docs/fr/2-Publication/Overview.md b/docs/fr/2-Publication/Overview.md index d1219a3e7..6b964b6b8 100644 --- a/docs/fr/2-Publication/Overview.md +++ b/docs/fr/2-Publication/Overview.md @@ -1,13 +1,13 @@ ## Publication statistique -_De beaux graphismes sont importants dans la publication statistique car ils rendent les données plus accessibles et compréhensibles pour un public plus large._ +_De beaux graphiques sont importants dans la publication statistique car ils rendent les données plus accessibles et compréhensibles pour un public plus large._ -La publication est un aspect essentiel de la science des données et des statistiques. Il permet aux chercheurs de partager leurs découvertes avec la communauté scientifique au sens large, permettant à d'autres de s'appuyer sur leurs travaux et de faire avancer le domaine. En partageant ouvertement leurs données et leurs méthodes, les chercheurs peuvent recevoir des commentaires sur leurs travaux et s'assurer que leurs conclusions sont exactes et fiables. +La publication est un aspect essentiel de la science des données et des statistiques. Il permet aux chercheurs de partager leurs découvertes avec la communauté scientifique au sens large, permettant ainsi à d’autres de s’appuyer sur leurs travaux et de faire avancer le domaine. En partageant ouvertement leurs données et leurs méthodes, les chercheurs peuvent recevoir des commentaires sur leurs travaux et garantir que leurs résultats sont précis et fiables. -La publication permet aux chercheurs d'établir leur réputation et d'obtenir la reconnaissance de leurs travaux, ce qui peut aider à obtenir des financements et de futures opportunités de recherche. En outre, la publication des résultats de la recherche peut avoir des implications importantes pour les politiques publiques et la prise de décision, car les décideurs s'appuient souvent sur des preuves scientifiques pour prendre des décisions éclairées. Dans l'ensemble, la publication fait partie intégrante du processus scientifique et joue un rôle essentiel dans l'avancement des connaissances et la résolution de problèmes du monde réel. +La publication permet aux chercheurs d’établir leur réputation et d’obtenir la reconnaissance de leurs travaux, ce qui peut contribuer à obtenir des financements et de futures opportunités de recherche. En outre, la publication des résultats de la recherche peut avoir des implications importantes sur les politiques publiques et la prise de décision, dans la mesure où les décideurs s’appuient souvent sur des preuves scientifiques pour prendre des décisions éclairées. Dans l’ensemble, la publication fait partie intégrante du processus scientifique et joue un rôle essentiel dans l’avancement des connaissances et la résolution de problèmes du monde réel. -La publication statistique consiste à communiquer des informations statistiques à un public plus large à l'aide de diverses formes de médias, tels que des graphiques et des infographies. Avoir de beaux graphiques est important dans la publication statistique car cela rend les données plus accessibles et compréhensibles pour un public plus large. Des visualisations bien conçues peuvent aider à communiquer des concepts et des modèles statistiques complexes de manière claire et convaincante, permettant au public de saisir rapidement les principales idées et conclusions. +La publication statistique consiste à communiquer des informations statistiques à un public plus large en utilisant diverses formes de médias, tels que des tableaux, des graphiques et des infographies. Avoir de beaux graphiques est important dans la publication statistique car cela rend les données plus accessibles et compréhensibles pour un public plus large. Des visualisations bien conçues peuvent aider à communiquer des concepts et des modèles statistiques complexes de manière claire et convaincante, permettant au public de saisir rapidement les principales informations et conclusions. -De beaux graphismes peuvent améliorer l'impact global des publications statistiques, les rendant plus attrayantes et mémorables. Cela peut être particulièrement important lors de la communication d'informations importantes aux décideurs, aux acteurs ou au grand public, où la capacité à communiquer clairement et efficacement des informations basées sur les données peut être essentielle pour réussir. +De superbes graphiques peuvent améliorer l’impact global des publications statistiques, les rendant plus attrayantes et mémorables. Cela peut être particulièrement important lors de la communication d'informations importantes aux décideurs, aux parties prenantes ou au grand public, où la capacité de communiquer clairement et efficacement des informations basées sur les données peut être essentielle pour réussir. -En résumé, la science des données et la publication de statistiques sont essentielles pour transformer des données complexes en informations significatives, et avoir de beaux graphiques est un aspect crucial pour communiquer efficacement ces informations à un public plus large. +En résumé, la science des données et la publication statistique sont essentielles pour transformer des données complexes en informations significatives, et disposer de superbes graphiques est un aspect crucial pour communiquer efficacement ces informations à un public plus large. \ No newline at end of file diff --git a/docs/fr/2-Publication/PowerBI.md b/docs/fr/2-Publication/PowerBI.md index 82c101f9c..add2a9da9 100644 --- a/docs/fr/2-Publication/PowerBI.md +++ b/docs/fr/2-Publication/PowerBI.md @@ -1,39 +1,30 @@ -# Chargement des données dans Power BI +# Aperçu -Nous ne proposons pas de serveur Power BI, mais vous pouvez extraire vos données -dans Power BI à partir de notre système de stockage et les utiliser comme une -trame de données `pandas`. +## Chargement des données dans Power BI + +Nous ne proposons pas de serveur Power BI, mais vous pouvez extraire vos données dans Power BI à partir de notre système de stockage et les utiliser comme une trame de données `pandas`. ![Tableau de bord sur Power BI](../images/powerbi_dashboard.png) -# Installation +## Installation -## Ce dont vous aurez besoin +### Ce dont vous aurez besoin 1. Un ordinateur avec Power BI et Python 3.6 -2. Vos `ACCESS_KEY` et `SECRET_KEY` MinIO (voir [Stockage](../Stockage.md)) - -## Connectez-vous +2. Vos `ACCESS_KEY` et `SECRET_KEY` MinIO (voir [Stockage](../5-Stockage/Aperçu.md)) -### Configurez Power BI +## Configurez Power BI -Ouvrez votre système Power BI et ouvrez -[démarrage rapide de Power BI](https://raw.githubusercontent.com/StatCan/aaw-contrib-jupyter-notebooks/master/querySQL/power_bi_quickstart.py) -dans votre éditeur de texte préféré. +Ouvrez votre système Power BI et ouvrez [démarrage rapide de Power BI](https://raw.githubusercontent.com/StatCan/aaw-contrib-jupyter-notebooks/master/querySQL/power_bi_quickstart.py) dans votre éditeur de texte préféré. -Assurez-vous que `pandas`, `boto3` et `numpy` sont installés et que vous -utilisez le bon environnement virtuel Conda (le cas échéant). +Assurez-vous que `pandas`, `boto3` et `numpy` sont installés et que vous utilisez le bon environnement virtuel Conda (le cas échéant). ![Installez les dépendances](../images/powerbi_cmd_prompt.png) -Ensuite, assurez-vous que Power BI utilise le bon environnement Python. Vous -pouvez modifier cet élément à partir du menu des options. Le chemin d'accès -exact est indiqué dans le guide de démarrage rapide. +Ensuite, assurez-vous que Power BI utilise le bon environnement Python. Vous pouvez modifier cet élément à partir du menu des options. Le chemin d'accès exact est indiqué dans le guide de démarrage rapide. ### Modifiez votre script Python -Ensuite, modifiez votre script Python pour utiliser vos `ACCESS_KEY` et -`SECRET_KEY` MinIO, puis cliquez sur « Obtenir des données » et copiez-le en -tant que script Python. +Ensuite, modifiez votre script Python pour utiliser vos `ACCESS_KEY` et `SECRET_KEY` MinIO, puis cliquez sur « Obtenir des données » et copiez-le en tant que script Python. ![Exécutez votre script Python.](../images/powerbi_python.png) diff --git a/docs/fr/2-Publication/R-Shiny.md b/docs/fr/2-Publication/R-Shiny.md index a5e728fee..a58c67dda 100644 --- a/docs/fr/2-Publication/R-Shiny.md +++ b/docs/fr/2-Publication/R-Shiny.md @@ -6,15 +6,17 @@ R-Shiny est un package R qui facilite la création d'applications Web interactiv !!! info "Hébergement d'applications R Shiny" Nous ne prenons actuellement pas en charge l'hébergement d'applications R Shiny, mais vous pouvez les créer. Nous souhaitons activer l'hébergement de l'application R Shiny à l'avenir. -![Page d'accueil brillante](../images/readme/shiny_ui.png) +![Page d'accueil de Shiny](../images/readme/shiny_ui.png) -## R brillant +## R Shiny _Publier des graphismes de qualité professionnelle_ -[![InteractiveDashboard](../images/InteractiveDashboard.PNG)](/2-Publishing/R-Shiny/) +
+[![InteractiveDashboard](../images/Publier.png)](../R-Shiny/) +
-R Shiny est un framework d'application Web open source qui permet aux data scientists et aux analystes de créer des tableaux de bord et des visualisations de données interactifs basés sur le Web à l'aide du langage de programmation R. L'un des principaux avantages de R Shiny est qu'il offre un moyen simple de créer des tableaux de bord interactifs de haute qualité sans avoir besoin d'une expertise approfondie en développement Web. Avec R Shiny, les data scientists peuvent tirer parti de leurs compétences en codage R pour créer des applications Web dynamiques, basées sur les données, qui peuvent être facilement partagées avec les acteurs. +R Shiny est un framework d'application Web open source qui permet aux scientifiques de données et aux analystes de créer des tableaux de bord et des visualisations de données interactifs basés sur le Web à l'aide du langage de programmation R. L'un des principaux avantages de R Shiny est qu'il offre un moyen simple de créer des tableaux de bord interactifs de haute qualité sans avoir besoin d'une expertise approfondie en développement Web. Avec R Shiny, les scientifiques de données peuvent tirer parti de leurs compétences en codage R pour créer des applications Web dynamiques, basées sur les données, qui peuvent être facilement partagées avec les acteurs. Un autre avantage de R Shiny est qu'il prend en charge une variété de visualisations de données qui peuvent être facilement personnalisées pour répondre aux besoins du projet. Les utilisateurs peuvent créer une large gamme de diagrammes et de graphiques, allant de simples diagrammes à barres et nuages de points à des cartes thermiques et des graphiques de réseau plus complexes. De plus, R Shiny prend en charge une variété de widgets interactifs qui permettent aux utilisateurs de manipuler et d'explorer des données en temps réel. @@ -22,9 +24,9 @@ Un autre avantage de R Shiny est qu'il prend en charge une variété de visualis R Shiny est également hautement extensible et peut être intégré à d'autres outils et plates-formes open source pour créer des workflows de science des données de bout en bout. Avec ses fonctionnalités puissantes et flexibles, R Shiny est un choix populaire pour créer des tableaux de bord de visualisation de données pour un large éventail d'applications, de la recherche scientifique à l'analyse commerciale. Dans l'ensemble, R Shiny offre une solution puissante, personnalisable et rentable pour créer des tableaux de bord interactifs et des visualisations de données. -Utilisez **[R-Shiny](/2-Publishing/R-Shiny/)** pour créer des applications Web interactives directement à partir de R. Vous pouvez déployer votre tableau de bord R Shiny en soumettant une demande d'extraction à notre [référentiel R-Dashboards GitHub ](https://github.com/StatCan/R-dashboards). +Utilisez **[R-Shiny](../R-Shiny/)** pour créer des applications Web interactives directement à partir de R. Vous pouvez déployer votre tableau de bord R Shiny en soumettant une demande d'extraction à notre [Référentiel R-Dashboards GitHub ](https://github.com/StatCan/R-dashboards). -# R Éditeur d'interface utilisateur brillant +# Éditeur d'interface utilisateur R Shiny Le script Rscript suivant installe les packages requis pour exécuter "shinyuieditor" sur l'ETAA. Il commence par installer les packages R nécessaires et utilise `conda` pour installer `yarn`. @@ -75,27 +77,27 @@ shinyuieditor::launch_editor(app_loc = "./") La première chose que vous verrez est le sélecteur de modèle. Il existe trois options au moment d'écrire ces lignes (`shinyuieditor` est actuellement en alpha). -![image](https://user-images.githubusercontent.com/8212170/229583104-9404ad01-26cd-4260-bce6-6fe32ffab7d8.png) +![Modèle d'éditeur d'interface utilisateur Shiny](https://user-images.githubusercontent.com/8212170/229583104-9404ad01-26cd-4260-bce6-6fe32ffab7d8.png) ### Mode fichier unique ou multi Je recommande le **mode multi-fichiers**, cela mettra le code back-end dans un fichier appelé `server.R` et le front-end dans un fichier appelé `ui.R`. -![image](https://user-images.githubusercontent.com/8212170/229584803-452bcdb9-4aa6-4902-805e-845d0b939016.png) +![Générer en mode multi-fichiers](https://user-images.githubusercontent.com/8212170/229584803-452bcdb9-4aa6-4902-805e-845d0b939016.png) ### Concevez votre application Vous pouvez concevoir votre application avec du code ou l'interface utilisateur graphique. Essayez de concevoir la mise en page avec l'interface graphique et de concevoir les tracés avec du code. -![image](https://user-images.githubusercontent.com/8212170/229589867-19bf334c-4789-4228-99ec-44583b119e29.png) +![Exemple de conception d'application](https://user-images.githubusercontent.com/8212170/229589867-19bf334c-4789-4228-99ec-44583b119e29.png) Toutes les modifications que vous apportez dans `shinyuieditor` apparaîtront immédiatement dans le code. -![image](https://user-images.githubusercontent.com/8212170/229637808-38dc0ed3-902a-44db-bfa0-193ef25af6ca.png) +![Exemple de texte de panneau](https://user-images.githubusercontent.com/8212170/229637808-38dc0ed3-902a-44db-bfa0-193ef25af6ca.png) Toute modification que vous apportez au code apparaîtra immédiatement dans le `shinyuieditor`. -![image](https://user-images.githubusercontent.com/8212170/229637972-b4a263f5-27f0-4160-8b43-9250ace72999.png) +![ShinyUiEditor](https://user-images.githubusercontent.com/8212170/229637972-b4a263f5-27f0-4160-8b43-9250ace72999.png) ## Publication sur l'ETAA @@ -108,8 +110,8 @@ Si vous avez besoin d'installer des bibliothèques R supplémentaires, envoyez v ![Exemple de tableau de bord](../images/example_shiny_dashboard.png) -!!! exemple "Voir le tableau de bord ci-dessus ici" - Le tableau de bord ci-dessus est dans GitHub. Jetez un œil à [la source](https://github.com/StatCan/R-dashboards/tree/master/bus-dashboard) et [voir le tableau de bord en direct](https://shiny.covid.cloud. statcan.ca/bus-dashboard). +!!! example "Voir le tableau de bord ci-dessus ici" + Le tableau de bord ci-dessus est dans GitHub. Jetez un œil à [la source](https://github.com/StatCan/R-dashboards/tree/master/bus-dashboard). ## Une fois que vous avez les bases... diff --git a/docs/fr/2-Publication/Sur-mesure.md b/docs/fr/2-Publication/Sur-mesure.md index ccb4af005..7557823b4 100644 --- a/docs/fr/2-Publication/Sur-mesure.md +++ b/docs/fr/2-Publication/Sur-mesure.md @@ -1,23 +1,18 @@ -# Applications Web personnalisées +# Aperçu -Nous pouvons tout déployer, dans la mesure où il s'agit de logiciel libre, et -nous pouvons le mettre dans un conteneur Docker (p. ex. applications Node.js, -Flask, Dash). +## Applications Web personnalisées + +Nous pouvons tout déployer, dans la mesure où il s'agit de logiciel libre, et nous pouvons le mettre dans un conteneur Docker (p. ex. applications Node.js, Flask, Dash). ![Exemple d'une application Node.js](../images/readme/covid_ui.png) !!! info "Voir le code de source de cette application" - Nous intégrons ces types d'applications au serveur au moyen de GitHub. La - source de l'application ci-dessus est ici : - [`StatCan/covid19`](https://github.com/StatCan/covid19). + Nous intégrons ces types d'applications au serveur au moyen de GitHub. -# Comment faire héberger votre application +## Installation +### Comment faire héberger votre application -Si vous avez déjà une application Web dans un répertoire Git, dès qu'elle est -placée dans un conteneur Docker, nous pouvons intégrer le répertoire Git dans le -répertoire GitHub de StatCan et pointer une URL vers elle. Pour la mettre à -jour, il vous suffit d'interagir avec le répertoire GitHub de StatCan au moyen -de demandes d'extraction. +Si vous avez déjà une application Web dans un répertoire Git, dès qu'elle est placée dans un conteneur Docker, nous pouvons intégrer le répertoire Git dans le répertoire GitHub de StatCan et pointer une URL vers elle. 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docs/fr/3-Pipelines/Overview.md | 17 - docs/fr/3-Pipelines/PaaS-Integration.md | 52 -- docs/fr/3-Pipelines/PaaS.md | 12 - docs/fr/3-Pipelines/Serving.md | 18 - .../3-Pipelines/sklearn_iris_jsondata.ipynb | 524 ----------- docs/fr/images/argo-workflows.jpg | Bin 0 -> 60271 bytes docs/fr/images/argo.png | Bin 0 -> 87565 bytes mkdocs-fr.yml | 4 +- 16 files changed, 386 insertions(+), 2192 deletions(-) delete mode 100644 docs/en/3-Pipelines/PaaS.md create mode 100644 docs/fr/3-Pipelines/Argo.md delete mode 100644 docs/fr/3-Pipelines/Kubeflow-Pipelines.md delete mode 100644 docs/fr/3-Pipelines/Machine-Learning-Model-Cloud-Storage.md delete mode 100644 docs/fr/3-Pipelines/Machine-Learning-Model-Serving.md delete mode 100644 docs/fr/3-Pipelines/Machine-Learning-Training-Pipelines.md delete mode 100644 docs/fr/3-Pipelines/Machine-Learning.md delete mode 100644 docs/fr/3-Pipelines/Overview.md delete mode 100644 docs/fr/3-Pipelines/PaaS-Integration.md delete mode 100644 docs/fr/3-Pipelines/PaaS.md delete mode 100644 docs/fr/3-Pipelines/Serving.md delete mode 100644 docs/fr/3-Pipelines/sklearn_iris_jsondata.ipynb create mode 100644 docs/fr/images/argo-workflows.jpg create mode 100644 docs/fr/images/argo.png diff --git a/docs/en/3-Pipelines/Argo.md b/docs/en/3-Pipelines/Argo.md index 45ab86835..59fa75824 100644 --- a/docs/en/3-Pipelines/Argo.md +++ b/docs/en/3-Pipelines/Argo.md @@ -7,7 +7,7 @@ Full documentation can be found [here](https://argoproj.github.io/argo-workflows/walk-through/). -Argo Workflows allows you to +Argo Workflows have these advantages: - Workflow tasks can be defined as scripts (ex. python) or be containerized (ex. docker). - Complex workflows can be modeled using Directed Acyclic graphs (DAGs) to capture dependency chains. @@ -158,7 +158,7 @@ Use the following `Dockerfile` as a starting point for your `R` and `Python` pro #### 3. Write your workflow in YAML -YAML is Yet Another Markup Language and you'll need to write down the steps of your training pipeline in an Argo Workflows YAML file. This file should include reference to the Dockerfile you created in [Step 1](#2-write-a-dockerfile-to-run-your-code), as well as any input data and output data you'll be working with. +YAML is Yet Another Markup Language and you'll need to write down the steps of your training pipeline in an Argo Workflows YAML file. This file should include reference to the Dockerfile you created in [Step 2](#2-write-a-dockerfile-to-run-your-code), as well as any input data and output data you'll be working with. Here is an example YAML file for a simple machine learning pipeline that trains a logistic regression model on the iris dataset. The only real difference between the `Python` and `R` versions is the command `command: ["python", "train.py"]` vs `command: ["Rscript", "train.R"]` and the models are stored in different formats, `pkl` for `python` and `rds` for `R`. diff --git a/docs/en/3-Pipelines/PaaS.md b/docs/en/3-Pipelines/PaaS.md deleted file mode 100644 index 663e84195..000000000 --- a/docs/en/3-Pipelines/PaaS.md +++ /dev/null @@ -1,18 +0,0 @@ -# Overview - -## Integrate with Platforms like Databricks and AzureML - -The AAW platform is built around the idea of integrations, and so we can -integrate with many _Platform as a Service_ (PaaS) offerings, such as -[Azure ML](https://azure.microsoft.com/en-us/services/machine-learning/) and -[Databricks](https://azure.microsoft.com/en-ca/services/databricks/). - -See some examples on our -["MLOps" github Repo](https://github.com/StatCan/aaw-kubeflow-mlops). - -![PaaS](../images/PaaS.png) - -# Setup - -If you need help integrating with a platform as a service offering, we're happy -to help! diff --git a/docs/fr/3-Pipelines/Argo.md b/docs/fr/3-Pipelines/Argo.md new file mode 100644 index 000000000..f33060ea3 --- /dev/null +++ b/docs/fr/3-Pipelines/Argo.md @@ -0,0 +1,383 @@ + +## Flux de travail Argo + +![Logo calmar Argo Workflows](../images/argo.png) + +**[Flux de travail Argo](https://argoproj.github.io/argo-workflows/)** est un moteur de flux de travail open source natif de conteneur pour orchestrer des tâches parallèles sur Kubernetes. Les flux de travails Argo sont implémentés en tant que Kubernetes CRD (Custom Resource Definition). Il est particulièrement adapté aux flux de travail de science des données et aux flux de travail d’apprentissage automatique. + +La documentation complète peut être trouvée [ici](https://argoproj.github.io/argo-workflows/walk-through/). + +Les flux de travails Argo ont les avantages suivants: + +- Les tâches de workflow peuvent être définies sous forme de scripts (ex. Python) ou être conteneurisées (ex. Docker). +- Des flux de travail complexes peuvent être modélisés à l'aide de graphes acycliques dirigés (DAG) pour capturer les chaînes de dépendance. +- Les tâches indépendantes peuvent être exécutées en parallèle avec une granularité jusqu'au niveau de mise en œuvre, réduisant ainsi les charges de tâches chronophages. +- Agnositique de la plateforme Kubernetes, ce qui signifie que votre travail est très portable. + +Avec les flux de travails Argo, vous pouvez facilement créer des flux de travails qui intègrent des tâches telles que des constructions et des déploiements automatisés, le prétraitement des données, la formation de modèles et le déploiement de modèles, le tout dans un environnement Cloud Native Kubernetes. + + +!!! info "" +
+ [![Diagramme de flux de travail Argo](../images/argo-workflows.jpg)](https://argoproj.github.io/argo-workflows/) +

Flux de travail Argo

+
+ +Vous trouverez ci-dessous un exemple de cas d'utilisation de flux de travail Argo, dans lequel nous formons un modèle d'apprentissage automatique à l'aide des flux de travail Argo sur AAW. + +#### 1. Écrivez un script pour entraîner votre modèle + +Voici un exemple de script qui entraîne un modèle de régression logistique sur l'ensemble de données iris. N'oubliez pas de consulter le code de chaque langue ci-dessous. + +=== "Python" + + ``` python title="train.py" linenums="1" + #!/usr/bin/env python + + # Importer les bibliothèques nécessaires + import argparse + import pandas as pd + from sklearn.datasets import load_iris + from sklearn.linear_model import LogisticRegression + from sklearn.model_selection import train_test_split + from sklearn.metrics import accuracy_score + import joblib + + # Analyser les arguments d'entrée + parser = argparse.ArgumentParser(description="Train logistic regression model on iris dataset.") + parser.add_argument("--input", default="iris.csv", help="Path to input dataset file.") + parser.add_argument("--output", default="model.pkl", help="Path to output model file.") + args = parser.parse_args() + + # Charger l'ensemble de données d'iris + data = load_iris() + X, y = data.data, data.target + + # Divisez les données en ensembles d'entraînement et de test + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + + # Entraîner un modèle de régression logistique + clf = LogisticRegression(random_state=42) + clf.fit(X_train, y_train) + + # Évaluer le modèle sur l'ensemble de test + y_pred = clf.predict(X_test) + accuracy = accuracy_score(y_test, y_pred) + print("Accuracy:", accuracy) + + # Enregistrer le modèle dans un fichier + joblib.dump(clf, args.output) + ``` + +=== "R" + + ``` r title="train.R" linenums="1" + #!/usr/bin/env Rscript + + # Importer les bibliothèques nécessaires + library(caret) + + # Copier les arguments d'entrée + args <- commandArgs(trailingOnly = TRUE) + input_file <- ifelse(length(args) > 0, args[1], "iris.csv") + output_file <- ifelse(length(args) > 1, args[2], "model.rds") + + # Charger l'ensemble de données d'iris + data(iris) + X <- iris[, 1:4] + y <- iris[, 5] + + # Divisez les données en ensembles d'entraînement et de test + set.seed(42) + train_index <- createDataPartition(y, p = 0.8, list = FALSE) + X_train <- X[train_index, ] + y_train <- y[train_index] + X_test <- X[-train_index, ] + y_test <- y[-train_index] + + # Entraîner un modèle de régression logistique + clf <- train(x = X_train, y = y_train, method = "glm") + + # Évaluer le modèle sur l'ensemble de test + y_pred <- predict(clf, newdata = X_test) + accuracy <- confusionMatrix(y_pred, y_test)$overall["Accuracy"] + print(paste0("Accuracy: ", accuracy)) + + # Enregistrer le modèle dans un fichier + saveRDS(clf, output_file) + ``` +#### 2. Écrivez un Dockerfile pour exécuter votre code + +Vous aurez besoin d'un Dockerfile qui inclut toutes les dépendances nécessaires pour entraîner votre modèle d'apprentissage automatique. Cela pourrait inclure: + +- des paquets comme + - `scikit-learn`, `pandas` ou `numpy` si vous utilisez `Python` + - `caret`, `janitor` et `tidyverse` si vous utilisez `R` +- vos propres bibliothèques ou scripts personnalisés +- le code de votre modèle d'apprentissage automatique sous la forme d'un script [comme dans l'exemple ci-dessus](#1-ecrivez-un-script-pour-entrainer-votre-modele). + +Utilisez le `Dockerfile` suivant comme point de départ pour vos projets `R` et `Python`. + +=== "Python" + + ``` docker title="Dockerfile" linenums="1" + FROM python:3.8-slim-buster + + # Installez toutes les dépendances nécessaires + RUN pip install --no-cache-dir scikit-learn pandas numpy + + # Définir le répertoire de travail + WORKDIR /app + + # Copier le code dans le conteneur + COPY train.py . + + # Définir le point d'entrée + ENTRYPOINT ["python", "train.py"] + ``` + +=== "R" + ``` docker title="Dockerfile" linenums="1" + FROM rocker/r-base:latest + + RUN apt-get update && apt-get install -y --no-install-recommends \ + libssl-dev \ + libcurl4-openssl-dev \ + libxml2-dev \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + + RUN R -e 'install.packages(c("caret", "janitor", "tidyverse"))' + + COPY train.R /app/train.R + + WORKDIR /app + + ENTRYPOINT ["Rscript", "train.R"] + ``` + +#### 3. Écrivez votre flux de travail en YAML + +YAML est encore un autre langage de balisage et vous devrez écrire les étapes de votre pipeline de formation dans un fichier YAML Argo Workflows. Ce fichier doit inclure une référence au Dockerfile que vous avez créé à l'[Étape 1](#2-ecrivez-un-dockerfile-pour-executer-votre-code), ainsi que toutes les données d'entrée et de sortie avec lesquelles vous travaillerez. + +Voici un exemple de fichier YAML pour un pipeline d'apprentissage automatique simple qui entraîne un modèle de régression logistique sur l'ensemble de données iris. La seule vraie différence entre les versions `Python` et `R` est la commande `command: ["python", "train.py"]` vs `command: ["Rscript", "train.R"]` et le les modèles sont stockés dans différents formats, `pkl` pour `python` et `rds` pour `R`. + +Le fichier YAML définit une seule étape appelée `train` qui exécute un script appelé `train.py` ou `train.R` dans l'image Docker `machine-learning:v1`. Le script prend un fichier d'ensemble de données d'entrée, spécifié par un paramètre appelé `dataset`, et génère un fichier de modèle entraîné vers un artefact de sortie appelé `model.pkl` ou `model.rds` selon le langage utilisé. + +=== "Python" + ``` yaml title="workflow.yaml" linenums="1" + apiVersion: argoproj.io/v1alpha1 + kind: Workflow + metadata: + generateName: ml-pipeline- + spec: + entrypoint: train + templates: + - name: train + container: + image: machine-learning:v1 + command: ["python", "train.py"] + args: ["--input", "{{inputs.parameters.dataset}}", "--output", "{{outputs.artifacts.model}}"] + inputs: + parameters: + - name: dataset + default: "iris.csv" + outputs: + artifacts: + - name: model + path: /output/model.pkl + ``` +=== "R" + ``` yaml title="workflow.yaml" linenums="1" + apiVersion: argoproj.io/v1alpha1 + kind: Workflow + metadata: + generateName: ml-pipeline- + spec: + entrypoint: train + templates: + - name: train + container: + image: machine-learning:v1 + command: ["Rscript", "train.R"] + args: ["--input", "{{inputs.parameters.dataset}}", "--output", "{{outputs.artifacts.model}}"] + inputs: + parameters: + - name: dataset + default: "iris.csv" + outputs: + artifacts: + - name: model + path: /output/model.rds + ``` + +#### 4. Soumettez le flux de travail à l'aide de l'entrée de ligne de commande(CLI) du flux de travail Argo + +Pour exécuter le flux de travail ci-dessus, vous devrez d'abord envoyer le Dockerfile vers notre registre de conteneurs, puis soumettre le fichier YAML à l'aide de la commande `argo submit`. Une fois le pipeline terminé, vous pouvez récupérer le fichier de modèle entraîné en téléchargeant l'artefact de sortie à partir de la commande `argo logs`. + +``` bash title="Terminal" +$ argo submit workflow.yaml # soumettre une spécification de flux de travail à Kubernetes +``` + +Il est également possible de soumettre des workflows argo à partir des workflows à l'aide de SDK et d'appels API (c'est juste un autre service Web !). Voir cette [section](#exemples-utilisant-des-sdk-bases-sur-flux-de-travail-argo). + +#### 5. Surveillez le pipeline à l'aide de la CLI du flux de travail Argo + +Pendant l'exécution du pipeline, vous pouvez surveiller sa progression à l'aide de la CLI Argo Workflows. Cela vous montrera quelles étapes se sont terminées avec succès et lesquelles sont toujours en cours. Vous trouverez ci-dessous quelques commandes utiles. Pour plus d'informations sur la CLI Argo Workflows, veuillez consulter [la documentation officielle de la CLI Argo Workflows](https://argoproj.github.io/argo-workflows/walk-through/argo-cli/) . + +``` bash title="Terminal" +$ argo list # lister les flux de travail actuels +$ argo get workflow-xxx # obtenir des informations sur un flux de travail spécifique +$ argo logs workflow-xxx # imprimer les journaux d'un flux de travail +$ argo delete workflow-xxx # suprimer un flux de travail + +``` + +#### 6. Récupérer le modèle entraîné + +Une fois la pipeline terminé, vous pouvez récupérer les données de sortie à l'aide de la commande argo logs ou en affichant les artefacts de sortie à l'aide de la CLI, c'est-à-dire accéder au répertoire que vous avez spécifié dans votre script et localiser le fichier `model.pkl` ou `model.rds`. L'extrait de code suivant, extrait du [script de formation ci-dessus](#1-ecrivez-un-script-pour-entrainer-votre-modele), indique au langage de programmation respectif où enregistrer le modèle entraîné. + +=== "Python" + + ``` python title="Saving Output Data" linenums="1" + #!/usr/bin/env python + + parser.add_argument("--output", default="model.pkl", help="Path to output model file.") + + # Enregistrer le modèle dans un fichier + joblib.dump(clf, args.output) + ``` + +=== "R" + + ``` r title="Saving Output Data" linenums="1" + #!/usr/bin/env Rscript + + output_file <- ifelse(length(args) > 1, args[2], "model.rds") + + # Enregistrer le modèle dans un fichier + saveRDS(clf, output_file) + ``` + +### Exemples utilisant des SDK basés sur flux de travail Argo + +Argo prend en charge les [bibliothèques client](https://argoproj.github.io/argo-workflows/client-libraries/), générées automatiquement et gérées par la communauté, qui incluent les SDK Java et Python. + +Si vous préférez utiliser un cadres de niveau supérieur, alors `Couler` et `Hera` sont des alternatives bien adaptées. Ces cadress rendent la création et la gestion de flux de travail complexes plus accessibles à un public plus large. + +#### Hera + +Hera vise à simplifier le processus de création et de soumission de flux de travail en éliminant de nombreux détails techniques via une interface de programmation d'application simple. Il utilise également un ensemble cohérent de terminologie et de concepts qui s'alignent sur les flux de travail Argo, permettant aux utilisateurs d'apprendre et d'utiliser plus facilement les deux outils ensemble. + +#### Couler + +Couler fournit une interface de programmation d'applications simple et unifiée pour définir des flux de travail à l'aide d'un style de programmation impératif. Il construit également automatiquement des graphiques acycliques dirigés (DAG) pour les flux de travail, ce qui peut contribuer à simplifier le processus de création et de gestion de ceux-ci. + +=== "Couler" + ``` py title="couler.py" linenums="1" + #!/usr/bin/env python + + # Préparez votre système + !pip config --user set global.index-url https://jfrog.aaw.cloud.statcan.ca/artifactory/api/pypi/pypi-remote/simple + !python3 -m pip install git+https://github.com/couler-proj/couler --ignore-installed + + # Définir une variable globale pour plus de commodité + NAMESPACE = "" + + # Importer les packages nécessaires + import json + import random + + import couler.argo as couler + from couler.argo_submitter import ArgoSubmitter + + + # Définir les étapes (fonctions) utilisées dans le workflow + def random_code(): + import random + res = "heads" if random.randint(0, 1) == 0 else "tails" + print(res) + + + def flip_coin(): + return couler.run_script( + image="k8scc01covidacr.azurecr.io/ubuntu", + source=random_code + ) + + + def heads(): + return couler.run_container( + image="k8scc01covidacr.azurecr.io/ubuntu", + command=["sh", "-c", 'echo "it was heads"'] + ) + + + def tails(): + return couler.run_container( + image="k8scc01covidacr.azurecr.io/ubuntu", + command=["sh", "-c", 'echo "it was tails"'] + ) + + + result = flip_coin() + + couler.when(couler.equal(result, "heads"), lambda: heads()) + couler.when(couler.equal(result, "tails"), lambda: tails()) + + submitter = ArgoSubmitter(namespace="NAMESPACE") + result = couler.run(submitter=submitter) + + print(json.dumps(result, indent=2)) + ``` +=== "Hera" + ``` py title="hera.py" linenums="1" + #!/usr/bin/env python + + # Préparez votre système + !pip config --user set global.index-url https://jfrog.aaw.cloud.statcan.ca/artifactory/api/pypi/pypi-remote/simple + !pip install hera-workflows + + # Importer les packages nécessaires + import hera + from hera import Task, Workflow + + # Configurer Hera + hera.global_config.GlobalConfig.token = "" + hera.global_config.GlobalConfig.host = "https://argo-workflows.aaw-dev.cloud.statcan.ca:443" + hera.global_config.GlobalConfig.namespace = "" + hera.global_config.GlobalConfig.service_account_name = "" + + + # Définir les étapes (fonctions) utilisées dans le workflow + def random_code(): + res = "heads" if random.randint(0, 1) == 0 else "tails" + print(res) + + + def heads(): + print("it was heads") + + + def tails(): + print("it was tails") + + # Définir le flux de travail + with Workflow("coin-flip") as w: + r = Task("r", random_code) + h = Task("h", heads) + t = Task("t", tails) + + h.on_other_result(r, "heads") + t.on_other_result(r, "tails") + + # Exécuter le flux de travail + w.create() + ``` + +### Ressources supplémentaires pour les flux de travail Argo + +Des exemples de flux de travail Argo peuvent être trouvés dans les référentiels Github suivants : + +- [Documentation des workflows Argo](https://argoproj.github.io/argo-workflows/) +- [Référence Argo CLI](https://argoproj.github.io/argo-workflows/walk-through/argo-cli/) diff --git a/docs/fr/3-Pipelines/Kubeflow-Pipelines.md b/docs/fr/3-Pipelines/Kubeflow-Pipelines.md deleted file mode 100644 index 31534fa06..000000000 --- a/docs/fr/3-Pipelines/Kubeflow-Pipelines.md +++ /dev/null @@ -1,256 +0,0 @@ -# Vue d'ensemble - -Kubeflow Pipelines est une plateforme de création de flux de production -d'apprentissage automatique pouvant être déployés dans un environnement -Kubernetes. Il permet de créer des pipelines qui encapsulent les flux de -production analytiques (transformation de données, modèles de formation, -construction d'éléments visuels, etc.). Ces pipelines peuvent être mis en -commun, réutilisés et programmés. Ils sont créés de façon à être exécutés avec -les calculs fournis par Kubernetes. - -Dans le contexte de l'espace de travail en analytique avancée, vous pouvez -interagir avec les pipelines Kubeflow par l'entremise : - -- de [l'interface utilisateur](../1-Experiences/Kubeflow.md) de Kubeflow, où, à - partir du menu Pipelines, vous pouvez télécharger des pipelines, visualiser - les pipelines que vous possédez et leurs résultats, etc. - -- de la trousse [SDK](https://www.kubeflow.org/docs/pipelines/sdk/sdk-overview/) - en Python de Kubeflow Pipelines, accessible dans les serveurs de bloc-notes - Jupyter, où vous pouvez définir vos composants et pipelines, les soumettre - pour les exécuter immédiatement, ou même les enregistrer pour plus tard. - - -??? exemple "Des exemples supplémentaires dans les bloc-notes" - Des exemples plus exhaustifs de pipelines produits expressément pour cette - plateforme sont accessibles dans - [GitHub](https://github.com/StatCan/aaw-contrib-jupyter-notebooks) (et dans chaque - serveur de bloc-notes à `/jupyter-notebooks`). Vous pouvez également - consulter des sources publiques. - -Voyez -[le documentation officiel de Kubeflow](https://www.kubeflow.org/docs/pipelines/overview/pipelines-overview/) -pour obtenir une explication générale détaillée de Kubeflow. - -![Un pipeline Kubeflow](../images/kf-pipeline_with_result.png) - -# Qu'est-ce qu'un pipeline et comment fonctionne-t-il? - -Dans Kubeflow Pipelines, un pipeline comprend un ou plusieurs composants de -pipeline enchaînés pour former un flux de production. Les composants sont comme -des fonctions que le pipeline connecte ensemble. - -Le _pipeline_ décrit l'ensemble du flux de production de ce que vous souhaitez -accomplir, tandis que les _composants de pipeline_ décrivent chacune des étapes -distinctes de ce processus (comme le fait d'extraire des colonnes d'un stock de -données, de transformer des données ou d'entraîner un modèle). Chaque -**composant** doit être **modulaire** et idéalement **réutilisable**. - -Essentiellement, chaque _composant_ a : - -- une application autonome, présentée sous forme d'image de menu fixe - (https://docs.docker.com/get-started/), pour effectuer le travail proprement - dit .le code dans l'image de menu fixe peut être une séquence de commandes en - langage naturel, un script Python ou tout autre code pouvant être exécuté à - partir d'un terminal Linux -- une description de la manière dont Kubeflow Pipelines exécute le code - (l'emplacement de l'image, les arguments de la ligne de commande qu'il - accepte, les résultats qu'il produit), sous forme de fichier YAML. - -Un _pipeline_ définit ensuite la logique de connexion des composants, par -exemple : - -1. Exécuter `ComposantA` -2. transmettre le résultat du `ComposantA` au `ComposantB` et au `ComposantC` -3. ... - - -!!! example "Exemple d'un pipeline" - Voici un exemple : - - #!/bin/python3 - dsl.pipeline(name="Estimer Pi", - description="Estimer Pi au moyen d'un modèle Map-Reduce") - def compute_pi(): - # Créer un "exemple" d'opération pour chaque valeur de départ - # transmise au pipeline - seeds = (1,2,3,4,5,6,7,8,9,10) - sample_ops = [sample_op(seed) for seed in seeds] - - # Obtenir les résultats avant de les transmettre à deux pipelines - # distincts. Les résultats sont extraits des fichiers - # `output_file.json` et sont disponibles à partir des instances - # `sample_op` par l'entremise de l'attribut `.outputs`. - outputs = [s.outputs['output'] for s in sample_ops] - - _generate_plot_op = generate_plot_op(outputs) - _average_op = average_op(outputs) - - Vous pouvez trouver le pipeline complet dans - [l'exemple `map-reduce-pipeline`](https://github.com/StatCan/aaw-contrib-jupyter-notebooks). - -# Définir et exécuter votre premier pipeline - -Bien que les _pipelines_ et les _composants_ soient définis par des fichiers -YAML, la trousse -[SDK](https://www.kubeflow.org/docs/pipelines/sdk/sdk-overview/) en Python vous -permet de les définir à partir du code Python. Voici un exemple de définition -d'un -[pipeline simple](https://github.com/StatCan/aaw-contrib-jupyter-notebooks/blob/master/kfp-basics/average_with_docker_components.ipynb) -en utilisant la trousse SDK en Python. - -L'objectif de notre pipeline est de calculer, au moyen de cinq nombres, les -valeurs suivantes : - -1. la moyenne des trois premiers nombres; -2. la moyenne des deux derniers nombres; -3. la moyenne des résultats de (1) et de (2). - -Pour ce faire, nous définissons un _pipeline_ qui utilise notre _composant_ -moyen pour effectuer les calculs. - -Le composant moyen est défini par une image de menu fixe au moyen d'un script -Python qui : - -- accepte un ou plusieurs nombres comme arguments de ligne de commande -- renvoie la moyenne de ces nombres, enregistrée dans le fichier `out.txt` dans - son conteneur. - -Pour indiquer à Kubeflow Pipelines comment utiliser cette image, nous -définissons notre _composant_ moyen par l'entremise d'un `ContainerOp`, qui -indique à Kubeflow l'interface API de notre image. L'instance `ContainerOp` -définit l'emplacement de l'image du menu fixe, la façon de lui transmettre des -arguments et les résultats à extraire du conteneur. Pour utiliser réellement ces -`ContainerOp` dans notre pipeline, nous créons des fonctions de fabrique comme -`average_op` (car nous voudrons probablement plus d'un _composant_ moyen). - -```python -from kfp import dsl - -def average_op(\*numbers): - """ Fabrique de ContainerOp moyen - - Accepte un nombre arbitraire de nombres d'entrée, en renvoyant un ContainerOp - qui transmet ces nombres à l'image du menu fixe sous-jacent pour faire la - moyenne - - Renvoie le résultat recueilli à partir du fichier ./out.txt à l'intérieur du - conteneur - - """ - - # Validation d'entrée - - if len(numbers) < 1: - raise ValueError("Doit préciser au moins un nombre à partir duquel calculer la moyenne") - - return dsl.ContainerOp( - name="averge", # Élément affiché dans la visionneuse de pipeline - image="k8scc01covidacr.azurecr.io/kfp-components/average:v1", # L'image - exécutée par Kubeflow Pipelines pour faire le travail arguments=numbers, - #transmet chaque nombre comme un argument de ligne de commande (chaîne) distinct - # Le script à l'intérieur du conteneur enregistre le résultat (sous forme de chaîne de caractères) dans le fichier out.txt, que - # Kubeflow Pipelines lit pour nous et récupère sous forme de chaîne. - file_outputs={'data': './out.txt'}, - ) -``` - -Nous définissons notre pipeline comme une fonction Python qui utilise les -fabriques de `ComponentOp` ci-dessus, décorées par l'élément décoratif -`@dsl.pipeline`. Notre pipeline utilise notre composant _moyen_ en lui -transmettant des nombres. Puis, nous utilisons les résultats _moyens_ en les -transmettant à des fonctions plus tard par l'accès à `avg\_\*.output`. - -```python -@dsl.pipeline( - name="nom de mon pipeline" -) -def my_pipeline(a, b, c, d, e): - """ - Calcul de moyenne de pipeline, qui accepte cinq nombres et effectue quelques calculs de moyenne sur ceux-ci - """ - - # Calculer les moyennes pour deux groupes - - avg_1 = average_op(a, b, c) - avg_2 = average_op(d, e) - - # Utiliser les résultats de \_1 et de \_2 pour calculer une moyenne globale - - average_result_overall = average_op(avg_1.output, avg_2.output) -``` - -Enfin, nous enregistrons une définition YAML de notre pipeline pour la -transmettre plus tard à Kubeflow Pipelines. Ce fichier YAML décrit à Kubeflow -Pipelines exactement comment exécuter notre pipeline. Décompressez-le et voyez -par vous-même! - -```python -from kfp import compiler -pipeline_yaml = 'pipeline.yaml.zip' -compiler.Compiler().compile( - my_pipeline, - pipeline_yaml -) print(f"Définition de pipeline exportée vers {pipeline_yaml}") -``` - - -??? avertissement "Kubeflow Pipelines est une bête paresseuse". - Il est utile de garder à l'esprit le calcul qui se produit lorsque vous - exécutez ce code Python par rapport à ce qui se passe lorsque vous soumettez - le pipeline à Kubeflow Pipelines. Bien que tout semble se produire - instantanément, essayez d'ajouter `print(avg_1.output)` au pipeline - ci-dessus et voyez ce qui se passe lorsque vous compilez votre pipeline. La - trousse SDK en Python que nous utilisons sert à créer des pipelines, et non - à les exécuter, de sorte que les résultats des composants ne seront jamais - disponibles lorsque vous exécuterez ce code Python. Ce point est abordé plus - en détail plus loin, dans la section _Comprendre l'ordre des calculs_. - -Pour exécuter notre pipeline, nous définissons une expérience : - -```python -experiment_name = "calcul de moyenne de pipeline" - -import kfp -client = kfp.Client() -exp = client.create_experiment(name=experiment_name) - -pl_params = { 'a': 5, 'b': 5, 'c': 8, 'd': 10, 'e': 18, } -``` - -Voici ce qui peut être observé dans -[l'interface utilisateur](../1-Experiences/Kubeflow.md) de Kubeflow Pipelines : - -![Expérience Kubeflow Pipelines](../images/kfp_experiment.png) - -Ensuite, nous exécutons une instance de notre pipeline en utilisant les -arguments souhaités : - -```python -import time - -run = client.run_pipeline( - exp.id, # Exécuter dans l'expérience ci-dessus - experiment_name + '-' + time.strftime("%Y%m%d-%H%M%S"), # Donner un nom et une - heure système à notre tâche unique pipeline_yaml, # Transmettre le .yaml.zip que - nous avons créé ci-dessus. Il définit le pipeline params=pl_params # Transmettre - les paramètres en fonction desquels nous souhaitons exécuter le pipeline -) -``` - -Voici ce que l'on peut également voir dans l'interface utilisateur : - -![Expérience Kubeflow Pipelines](../images/kfp_experiment.png) - -Plus tard, lorsque nous souhaiterons réutiliser le pipeline, nous pourrons -transmettre différents arguments et tout recommencer (et même le réutiliser à -partir de l'interface utilisateur de Kubeflow). Pour mieux comprendre cet -exemple, ouvrez-le dans Kubeflow et essayez-le vous-même. - -# Composants légers - -En construction, malheureusement! - -# Comprendre l'ordre des calculs - -En construction, malheureusement! diff --git a/docs/fr/3-Pipelines/Machine-Learning-Model-Cloud-Storage.md b/docs/fr/3-Pipelines/Machine-Learning-Model-Cloud-Storage.md deleted file mode 100644 index 3a271b7d4..000000000 --- a/docs/fr/3-Pipelines/Machine-Learning-Model-Cloud-Storage.md +++ /dev/null @@ -1,61 +0,0 @@ -# Données B protégées par Statcan - - -!!! info "Protégé B" - L'ETAA est certifié pour l'hébergement de données protégé B ! - -Afin de télécharger des données protégées B sur l'ETAA, les utilisateurs devront demander l'accès via l'Unité de Succès à la Clientèle (USC). Les utilisateurs d'ETAA devront également fournir un espace de noms, obtenir un sponsor et obtenir l'approbation d'OPMIC. Une fois le processus approuvé, notre équipe infrastructure de données F.A.I.R créera alors un dossier sur Net A qui à son tour donnera accès aux utilisateurs via le répertoire actif. Les données pourront alors être transférées de Net A vers le cloud ETAA. - -Le stockage des modèles d'apprentissage automatique dans un environnement de stockage cloud protégé est essentiel pour garantir la sécurité et la confidentialité des données sensibles. L'espace de travail d'analyse avancée (ETAA) fournit un environnement de stockage cloud sécuritaire et robuste qui peut être utilisé pour stocker des modèles d'apprentissage automatique et d'autres actifs de données. - -La plate-forme ETAA fournit un environnement de stockage cloud protégé conçu pour répondre aux exigences les plus strictes en matière de sécurité et de confidentialité des données. L'environnement de stockage est protégé par un cryptage et des contrôles d'accès conformes aux normes de l'industrie, ce qui garantit que seul le personnel autorisé peut accéder aux données sensibles. Cela protège contre les accès non autorisés, les violations de données et autres menaces de sécurité. - -En plus de ses fonctions de sécurité robustes, l'environnement de stockage en cloud ETAA est également hautement évolutif et flexible. Cela signifie que les data scientists et les ingénieurs en apprentissage automatique peuvent facilement faire évoluer leurs besoins de stockage à mesure que leurs ensembles de données et la taille de leurs modèles augmentent. Cela leur permet de stocker et de gérer de gros volumes de données et de modèles sans avoir à se soucier des limitations de stockage ou des goulots d'étranglement des performances. - -Le stockage des modèles d'apprentissage automatique dans un environnement de stockage cloud protégé sur l'espace de travail d'analyse avancée fournit une solution sécurisée, évolutive et flexible pour la gestion et la protection des données sensibles. En tirant parti des capacités de stockage dans le cloud fournies par la plate-forme ETAA, les data scientists et les ingénieurs en apprentissage automatique peuvent se concentrer sur la création et le déploiement de leurs modèles en toute confiance, sachant que leurs données sont protégées et sécurisées. - -## Stockage en ligne - - -!!! info "Avantages du stockage en cloud" - Le stockage en cloud offre plusieurs avantages pour la science des données et l'apprentissage automatique, notamment en termes d'évolutivité, d'accessibilité et de rentabilité. - -Premièrement, le stockage dans le cloud permet aux data scientists de stocker et de traiter de grandes quantités de données sans avoir à se soucier des limites du stockage local. Ceci est particulièrement important dans le contexte de l'apprentissage automatique, où de grands ensembles de données sont nécessaires pour la formation et le test des modèles. Le stockage dans le cloud permet aux data scientists d'augmenter leur capacité de stockage selon les besoins, sans avoir à investir dans du matériel coûteux. - -Deuxièmement, le stockage en cloud permet aux data scientists d'accéder aux données de n'importe où, en utilisant n'importe quel appareil doté d'une connexion Internet. Cela permet la collaboration entre des équipes géographiquement dispersées et permet aux data scientists de travailler à distance. De plus, le stockage dans le cloud facilite le partage de données avec d'autres parties prenantes, telles que des partenaires commerciaux ou des clients. Enfin, le stockage dans le cloud est généralement plus rentable que le stockage sur site, en particulier pour les petites entreprises ou celles dont les ressources informatiques sont limitées. - -Dans l'ensemble, le stockage en cloud est une solution fiable et pratique pour stocker et gérer vos données. Que vous ayez besoin de stocker de grandes quantités de données ou seulement quelques fichiers, le stockage en cloud facilite la gestion de vos besoins de stockage sans les tracas des solutions de stockage traditionnelles. - -La plateforme ETAA propose plusieurs types de stockage : - -- Disques (également appelés Volumes sur l'écran de création de Kubeflow Notebook Server) -- Buckets ("Blob" ou stockage S3, fournis via MinIO) -- Lacs de données (à venir) - -Selon votre cas d'utilisation, le disque ou le bucket peut être le plus approprié. Notre [aperçu du stockage](../5-Storage/Overview.md) vous aidera à les comparer. - -### Disques - -[![Disques](../images/Disks.PNG)](Storage.md/) - -**[Disks](../5-Storage/Disks.md)** sont ajoutés à votre serveur notebook en ajoutant des volumes de données. - -### Seaux - -MinIO est un système de stockage d'objets compatible S3-API qui fournit une alternative open source aux services de stockage cloud propriétaires. Bien que nous utilisions actuellement MinIO comme solution de stockage dans le cloud, nous prévoyons de le remplacer par s3-proxy dans un proche avenir. S3-proxy est un serveur proxy inverse léger et open source qui vous permet d'accéder à des services de stockage compatibles avec Amazon S3 avec vos applications existantes. En passant à s3-proxy, nous pourrons améliorer les performances, la sécurité et l'évolutivité de notre stockage dans le cloud, tout en maintenant la compatibilité avec l'API S3. - -[![MinIO](../images/Buckets.PNG)](AzureBlobStorage.md/) - -**[MinIO](../5-Storage/AzureBlobStorage.md)** est un magasin d'objets évolutif cloud natif. Nous l'utilisons pour les buckets (stockage blob ou S3). - -### Lacs de données (à venir) - -Un lac de données est un référentiel central qui vous permet de stocker toutes vos données structurées et non structurées à n'importe quelle échelle. C'est un moyen rentable de stocker et de gérer tous les types de données, des données brutes aux données traitées, et c'est un outil essentiel pour les data scientists. - -L'un des principaux avantages d'un lac de données est sa flexibilité. Il permet de stocker tous types de données sans avoir besoin de définir un schéma au préalable, ce qui est particulièrement utile lorsqu'il s'agit de données non structurées. Cette flexibilité permet aux data scientists d'explorer, d'expérimenter et d'extraire facilement des informations à partir de leurs données sans être contraints par les limites d'une base de données relationnelle traditionnelle. - -Les lacs de données permettent également aux data scientists de centraliser leurs données, ce qui facilite la gestion et l'analyse de gros volumes de données provenant de diverses sources. Avec un lac de données, les data scientists peuvent facilement ingérer et stocker des données provenant de diverses sources, telles que des bases de données, le stockage dans le cloud et des API tierces. De plus, les lacs de données fournissent souvent des fonctionnalités pour la gouvernance des données, la gestion des métadonnées et le contrôle d'accès, ce qui permet de garantir que les données sont de haute qualité et conformes aux réglementations en vigueur. - -De plus, les lacs de données basés sur le cloud offrent des solutions de stockage évolutives et économiques qui peuvent être facilement étendues en un clic. À mesure que les besoins de stockage de données d'un scientifique des données augmentent, il peut ajouter une capacité de stockage supplémentaire à son lac de données avec un minimum d'effort, sans se soucier de l'infrastructure sous-jacente ou de la maintenance. - -Dans l'ensemble, les lacs de données sont un outil essentiel pour les data scientists, car ils offrent la flexibilité, l'évolutivité et la facilité d'utilisation nécessaires pour stocker et gérer de gros volumes de données, permettant aux data scientists de se concentrer sur l'extraction d'informations et de valeur à partir des données. diff --git a/docs/fr/3-Pipelines/Machine-Learning-Model-Serving.md b/docs/fr/3-Pipelines/Machine-Learning-Model-Serving.md deleted file mode 100644 index 237deb56f..000000000 --- a/docs/fr/3-Pipelines/Machine-Learning-Model-Serving.md +++ /dev/null @@ -1,39 +0,0 @@ -# Introduction au modèle de service - -Dans le contexte des gouvernements, servir des modèles d'apprentissage automatique signifie rendre les modèles formés disponibles pour être utilisés par d'autres applications et systèmes. Cela peut inclure la réalisation de prédictions ou de classifications basées sur les données d'entrée, ou la fourniture d'informations et de recommandations basées sur les résultats de l'analyse des données. - -Servir des modèles d'apprentissage automatique dans un contexte gouvernemental soulève des questions importantes liées à la confidentialité des données. Les agences gouvernementales sont souvent responsables de la collecte et de la gestion des données personnelles sensibles, telles que les dossiers médicaux, les données financières et les casiers judiciaires. Lors de la diffusion de modèles d'apprentissage automatique, il est essentiel de s'assurer que ces données sont protégées et que leur accès est strictement contrôlé. - -Pour répondre à ces préoccupations, les agences gouvernementales doivent mettre en œuvre des mesures solides de confidentialité et de sécurité des données lorsqu'elles servent des modèles d'apprentissage automatique. Cela pourrait inclure le chiffrement des données au repos et en transit, la mise en œuvre de contrôles d'accès et d'authentification des utilisateurs, et la surveillance régulière des violations de données et des vulnérabilités. - -En plus de la confidentialité et de la sécurité des données, il est également important de prendre en compte les implications éthiques de servir des modèles d'apprentissage automatique dans un contexte gouvernemental. Les modèles d'apprentissage automatique peuvent être biaisés ou discriminatoires, entraînant un traitement injuste de certains groupes de personnes. Pour atténuer ces risques, les agences gouvernementales doivent soigneusement évaluer et surveiller leurs modèles d'apprentissage automatique, et prendre des mesures pour lutter contre les préjugés ou la discrimination qui pourraient survenir. - -Dans l'ensemble, servir des modèles d'apprentissage automatique dans un contexte gouvernemental nécessite un examen attentif de la confidentialité des données, de la sécurité et des préoccupations éthiques. En mettant en œuvre des mesures solides pour protéger les données personnelles et prévenir les préjugés, les agences gouvernementales peuvent tirer parti de la puissance de l'apprentissage automatique pour prendre de meilleures décisions et améliorer les résultats pour les citoyens tout en maintenant la confiance et la transparence. - -## Pourquoi servir avec nous ? - -Servir des modèles d'apprentissage automatique avec l'espace de travail d'analyse avancée (l'ETAA) présente plusieurs avantages. Premièrement, l'ETAA est une plate-forme d'analyse de données open source qui donne accès à une variété d'outils d'analyse avancés, notamment Python, R et SAS. Cela facilite le déploiement de modèles d'apprentissage automatique et leur intégration dans les flux de travail existants. - -Deuxièmement, l'ETAA prend en charge plusieurs frameworks MLOps, notamment Couler, Seldon et Argo Workflows. Ces frameworks fournissent une gamme de fonctionnalités, notamment la gestion des versions de modèle, le service de modèle et la surveillance de modèle, qui simplifient le processus de déploiement et de gestion des modèles d'apprentissage automatique en production. - -Troisièmement, l'ETAA fournit une plate-forme sécurisée et évolutive pour servir les modèles d'apprentissage automatique avec le statut protégé B. Les modèles peuvent être servis à l'aide d'environnements conteneurisés, tels que Docker, qui offrent un niveau élevé d'isolement et de sécurité. L'ETAA fournit également un accès aux ressources de cloud computing, permettant aux utilisateurs d'augmenter leur puissance de calcul selon les besoins pour gérer des volumes élevés de demandes. - -Enfin, l'ETAA est une plateforme collaborative qui permet aux utilisateurs de partager du code et des données avec d'autres chercheurs et analystes. Cela favorise une communauté d'utilisateurs qui peuvent apprendre du travail des autres et collaborer sur des projets qui nécessitent des capacités d'analyse avancées. - -En résumé, servir des modèles d'apprentissage automatique avec l'espace de travail d'analyse avancée donne accès à des outils d'analyse avancés, à plusieurs cadres MLOps, à une plate-forme protégé B sécurisée et évolutive et à une communauté collaborative d'utilisateurs, ce qui en fait une plate-forme idéale pour les scientifiques et les analystes de données qui veulent pour déployer et gérer des modèles d'apprentissage automatique en production. - -## Noyau de Seldon - -Seldon Core est une plate-forme open source pour le déploiement, la mise à l'échelle et la surveillance des modèles d'apprentissage automatique sur Kubernetes. Il fournit un moyen simple et efficace de déployer des modèles d'apprentissage automatique en tant que microservices dans un environnement de production. - -Servir des modèles d'apprentissage automatique à l'aide de Seldon Core implique les étapes suivantes : - -1. Conditionnement du modèle : la première étape consiste à conditionner le modèle d'apprentissage automatique formé dans une image de conteneur avec toutes les dépendances requises. Seldon Core prend en charge divers frameworks d'apprentissage automatique, notamment TensorFlow, PyTorch et Scikit-learn. - -2. Déploiement du modèle : une fois l'image du conteneur créée, l'étape suivante consiste à déployer le modèle sur Kubernetes à l'aide de Seldon Core. Cela implique de définir le fichier de configuration de déploiement, qui spécifie les ressources requises pour le déploiement, telles que le nombre de répliques et les ressources de calcul. - -3. Service de modèle : une fois le modèle déployé, Seldon Core expose un point de terminaison d'API REST qui peut être utilisé pour effectuer des prédictions. Les clients peuvent envoyer des requêtes au point de terminaison avec des données d'entrée, et le modèle renverra la sortie correspondante. Seldon Core prend également en charge divers modèles de déploiement, tels que le déploiement Canary et les tests A/B, pour permettre une expérimentation et des tests faciles de différents modèles. - -4. Surveillance des modèles : Seldon Core fournit diverses fonctionnalités de surveillance pour suivre les performances des modèles déployés. Cela inclut la surveillance en temps réel des métriques du modèle, telles que la latence et le débit, ainsi que la journalisation des données de demande et de réponse à des fins de débogage. - -Seldon Core facilite la mise à disposition de modèles d'apprentissage automatique à grande échelle, avec une prise en charge de la haute disponibilité, de l'évolutivité et de la tolérance aux pannes. Il fournit également une intégration avec divers outils natifs de Kubernetes, tels qu'Istio et Prometheus, pour permettre une surveillance et une observabilité avancées. diff --git a/docs/fr/3-Pipelines/Machine-Learning-Training-Pipelines.md b/docs/fr/3-Pipelines/Machine-Learning-Training-Pipelines.md deleted file mode 100644 index c15095c6e..000000000 --- a/docs/fr/3-Pipelines/Machine-Learning-Training-Pipelines.md +++ /dev/null @@ -1,821 +0,0 @@ -# Former des modèles d'apprentissage automatique sur l'ETAA - -
-![MLOps](../images/mlops.jpg) -
- - -!!! info - La formation de modèles d'apprentissage automatique implique l'utilisation d'algorithmes pour apprendre des modèles et des relations dans les données. Ce processus implique l'identification de caractéristiques ou de variables pertinentes pour le problème en question et l'utilisation de ces caractéristiques pour faire des prédictions ou des classifications. - -## Pourquoi s'entraîner avec nous ? - -_L'entraînement des modèles d'apprentissage automatique sur l'espace de travail d'analyse avancée (ETAA) présente plusieurs avantages._ - -1. **Open Source :** L'ETAA est une *plate-forme de données open source hébergée par Statistique Canada* qui fournit un accès sécurisé (protégé B) à une variété de sources de données, y compris des données de recensement, des enquêtes et des dossiers administratifs. Ces données peuvent être utilisées pour former des modèles d'apprentissage automatique et générer des informations qui peuvent éclairer les décisions politiques et améliorer les processus métier. - -2. **Polyvalent :** L'ETAA est *conçu pour gérer des ensembles de données volumineux et complexes*. Il donne accès à une gamme d'outils d'analyse avancés, dans le langage de votre choix, y compris *Python, R et SAS*, qui peuvent être utilisés pour prétraiter les données, former des modèles d'apprentissage automatique et générer des visualisations. *Parce que l'ETAA exploite les technologies cloud, *les utilisateurs peuvent augmenter leur puissance de calcul selon leurs besoins*. -* -3. **Sécurisé :** L'ETAA est une *plate-forme sécurisée (protégé B) qui respecte les normes les plus élevées en matière de confidentialité et de sécurité des données*. Les données peuvent être stockées et traitées sur la plateforme sans risque d'accès non autorisé ou de violation de données. - -## MLOps et pipelines de données - - -!!! info "Optimiser les workflows de données" - Les MLOps et les pipelines de données sont des outils importants utilisés dans le domaine de la science des données pour gérer et optimiser les workflows de données. - -### MLOps - -MLOps fait référence à l'ensemble des pratiques et des outils utilisés pour gérer l'ensemble du cycle de vie d'un modèle d'apprentissage automatique. Cela comprend tout, du développement et de la formation du modèle à son déploiement en production et à sa maintenance dans le temps. MLOps garantit que les modèles d'apprentissage automatique sont fiables, précis et évolutifs, et qu'ils peuvent être mis à jour et améliorés selon les besoins. - -### Pipelines de données - -Les pipelines de données sont une série d'étapes qui permettent de déplacer des données d'un système ou d'une application à un autre. Cela comprend la collecte, le nettoyage, la transformation et le stockage des données, ainsi que leur récupération en cas de besoin. Les pipelines de données sont importants pour garantir que les données sont exactes, fiables et accessibles à ceux qui en ont besoin. - - -!!! info "Automatisation et fiabilité" - Les MLOps et les pipelines de données aident les organisations à gérer le processus complexe consistant à travailler avec de grandes quantités de données et à développer des modèles d'apprentissage automatique. En automatisant ces processus et en s'assurant que les données sont exactes et fiables, les organisations peuvent économiser du temps et des ressources tout en prenant de meilleures décisions basées sur des informations basées sur les données. - -### Pourquoi des MLOps conteneurisés ? - -Les avantages de l'utilisation d'une approche conteneurisée pour la formation de modèles d'apprentissage automatique avec Argo Workflows incluent : - -1. **Reproductibilité :** La conteneurisation du modèle de l'apprentissage automatique et de ses dépendances garantit que l'environnement reste cohérent d'une exécution à l'autre, ce qui facilite la reproduction des résultats. - -2. **Évolutivité :** Argo Workflows peut orchestrer des tâches parallèles et des flux de travail complexes, ce qui facilite l'évolution du processus de formation selon les besoins. - -3. **Portabilité :** Les conteneurs peuvent être exécutés sur n'importe quelle plate-forme prenant en charge la conteneurisation, ce qui facilite le déplacement du processus de formation vers différents environnements ou fournisseurs de cloud. - -4. **Collaboration :** En transférant le conteneur vers un registre de conteneurs, les autres utilisateurs peuvent facilement télécharger et utiliser le conteneur à leurs propres fins, ce qui facilite la collaboration sur des projets de l'apprentissage automatique. - -Les flux de travail Argo et la conteneurisation offrent une approche puissante et flexible pour la formation de modèles d'apprentissage automatique. En tirant parti de ces outils, les data scientists et les ingénieurs en apprentissage automatique peuvent créer, déployer et gérer des workflows d'apprentissage automatique avec facilité et reproductibilité. - -## Comment former des modèles - -Il existe plusieurs façons de former des modèles d'apprentissage automatique et ce n'est pas à nous de dire à qui que ce soit comment le faire. Cela étant dit, nous avons fourni ci-dessous quelques guides sur la façon de former des modèles d'apprentissage automatique à l'aide des outils disponibles sur l'ETAA. Le premier tutoriel concerne la formation d'un modèle simple directement dans un notebook JupyterLab. Le deuxième tutoriel suppose que l'utilisateur est plus avancé et souhaite définir un pipeline MLOps pour former des modèles à l'aide d'Argo Workflows. - -### Créer un serveur de bloc-notes sur l'ETAA - - -!!! info "Serveurs portables" - Que vous envisagiez de travailler dans JupyterLab, R Studio ou quelque chose de plus avancé avec Argo Workflows, vous aurez besoin du serveur de bloc-notes approprié. [Suivez les instructions trouvées ici pour commencer.](https://docs.google.com/presentation/d/12yTDlbMCmbg0ccdea2h0vwhs5YTa_GHm_3DieG5A-k8/edit?usp=sharing) - -### Utilisation de JupyterLab - -![JupyterLab](../images/jupyterlab.jpg) - - -!!! info "JupyterLab est populaire" - La formation de modèles d'apprentissage automatique avec JupyterLab est une approche populaire parmi les data scientists et les ingénieurs en apprentissage automatique. - -Vous trouverez ici les étapes nécessaires pour former un modèle d'apprentissage automatique avec JupyterLab sur l'ETAA. Parce que nous sommes un environnement multilingue, nous avons fait de notre mieux pour fournir des exemples de code dans nos langages les plus populaires, `Python`, `R` et `SAS`. - -#### 1. Importez les bibliothèques requises - -Une fois qu'une session JupyterLab est en cours d'exécution, vous devez importer les bibliothèques requises pour votre modèle d'apprentissage automatique. Cela peut inclure des bibliothèques telles que `NumPy`, `Pandas`, `Scikit-learn`, `Tensorflow` ou `PyTorch`. Si vous utilisez `R`, vous aurez besoin de `tidyverse`, `caret` et `janitor`. - -=== "Python" - ``` py title="libraries.py" linenums="1" - #!/usr/bin/env python - - # tensorflow et keras pour la construction et la formation de modèles d'apprentissage en profondeur - import tensorflow as tf - from tensorflow import keras - - # numpy pour les calculs numériques - import numpy as np - - # pandas pour la manipulation et l'analyse des données - import pandas as pd - - # matplotlib pour la visualisation des données - import matplotlib.pyplot as plt - ``` -=== "R" - ``` r title="libraries.R" linenums="1" - #!/usr/bin/env Rscript - - # tidyverse pour des outils impressionnants d'analyse de données et de munging - library(tidyverse) - - # concierge pour nettoyer vos données - library(janitor) - - # caret pour un apprentissage automatique facile - library(caret) - ``` -=== "SASPy" - ``` py title="libraries.py" linenums="1" - #!/usr/bin/env python - - # la seule bibliothèque dont vous aurez besoin pour accéder à SAS depuis Python - import saspy - ``` -=== "SAS" - ``` sas title="libraries.sas" linenums="1" - - ``` - - -!!! note "À propos du code" - Les exemples de code que vous voyez dans ce document et tout au long de la documentation sont à titre indicatif pour vous aider à démarrer vos projets. Selon la tâche ou le projet spécifique, d'autres bibliothèques et étapes peuvent être nécessaires. - -#### 2. Charger et prétraiter les données - -Ensuite, vous devez charger et prétraiter les données que vous utiliserez pour entraîner votre modèle d'apprentissage automatique. Cela peut inclure le nettoyage des données, l'extraction de caractéristiques et la normalisation. Les étapes de prétraitement exactes que vous devrez effectuer dépendront de l'ensemble de données spécifique avec lequel vous travaillez, des exigences de votre modèle d'apprentissage automatique et du travail à effectuer. - -=== "Python" - ``` py title="load_data.py" linenums="1" - #!/usr/bin/env python - - # Importer les paquets nécessaires - import pandas as pd - from sklearn.preprocessing import StandardScaler - - # Charger des données à partir d'un fichier CSV - data = pd.read_csv('data.csv') - - # Nettoyage des données ! Beaucoup plus peut être fait, c'est un principe de base - data = data.dropna() # Drop rows with missing values - data = data.drop_duplicates() # Drop duplicate rows - - # Extraction de caractéristiques - X = data[['feature1', 'feature2', 'feature3']] # Sélectionnez les fonctionnalités pertinentes - - # Normalisation - scaler = StandardScaler() # Crée un objet scaler - X_norm = scaler.fit_transform(X) # Normaliser les valeurs des caractéristiques - ``` -=== "R" - ``` r title="load_data.R" linenums="1" - #!/usr/bin/env Rscript - - # Importer les paquets nécessaires - library(tidyverse) - library(janitor) - - # Charger des données à partir d'un fichier CSV - data <- read_csv("data.csv") - - # Nettoyer les données à l'aide d'un concierge - data_cleaned <- data %>% - # Supprimer les espaces blancs de début/fin dans les noms de colonne - clean_names() %>% - # Supprimer les lignes avec des valeurs manquantes - remove_empty() %>% - # Convertir la colonne de date au format Date - mutate(date = as.Date(date, format = "%m/%d/%Y")) %>% - # Supprimer les lignes en double - distinct() %>% - # Réorganiser les colonnes - select(date, column2, column1, column3) - ``` -=== "SASPy" - ``` py title="load_data.py" linenums="1" - #!/usr/bin/env python - - # Importer les paquets nécessaires - import saspy - - # Démarrez une session SAS et vérifiez les informations de configuration - sas = saspy.SASsession(cfgname='default') - - # Charger des données à partir d'un fichier CSV - data = sas.read_csv("./data.csv") - ``` -=== "SAS" - ``` sas title="load_data.sas" linenums="1" - /* Lecture d'un fichier délimité par des virgules avec une extension .csv */ - /* */ - /* Puisque la valeur DBMS= est CSV, vous n'avez pas besoin d'utiliser le DELIMITER= */ - /* déclaration.Par défaut, il est supposé que les noms de variables sont sur la première */ - /* ligne, l'instruction GETNAMES= n'est donc pas requise. */ - /* */ - /* Créer un fichier de test délimité par des virgules à lire en utilisant PROC IMPORT ci-dessous. */ - - /* Charger les données d'un fichier CSV */ - proc import - datafile='data.csv' - out=data - dbms=csv - replace; - run; - - /* Afficher les données */ - proc print ; - courir; - ``` - -#### 3. Divisez les données en ensembles d'entraînement et de test - - Une fois les données prétraitées, vous devez les diviser en ensembles d'apprentissage et de test. L'ensemble de formation sera utilisé pour former le modèle d'apprentissage automatique, tandis que l'ensemble de test sera utilisé pour évaluer ses performances. - -=== "Python" - ``` py title="train_test.py" linenums="1" - #!/usr/bin/env python - - # Importer les paquets nécessaires - import pandas as pd - from sklearn.model_selection import train_test_split - - # Diviser les données en ensembles d'entraînement et de test - X_train, X_test, y_train, y_test = train_test_split(X_norm, - data['target'], test_size=0.2, random_state=42) - ``` -=== "R" - ``` r title="train_test.R" linenums="1" - #!/usr/bin/env Rscript - - # Importer les paquets nécessaires - library(caret) - - # Charger le jeu de données - data <- read.csv("my-dataset.csv") - - # Définir la graine pour la reproductibilité - set.seed(123) - - # Diviser l'ensemble de données en train et tester à l'aide de la fonction createDataPartition de caret - train_index <- createDataPartition(data$target_variable, p = 0.7, list = FALSE) - train_data <- data[train_index, ] - test_data <- data[-train_index, ] - ``` -=== "SASPy" - ``` py title="train_test.py" linenums="1" - #!/usr/bin/env python - - ``` -=== "SAS" - ``` sas title="train_test.sas" linenums="1" - - ``` - - -!!! note - Nous divisons les données en ensembles d'apprentissage et de test à l'aide de la fonction "train_test_split" de "scikit-learn", qui divise de manière aléatoire les données en deux ensembles en fonction de la taille de test spécifiée et de la graine aléatoire. - - -#### 4. Définir et entraîner le modèle d'apprentissage automatique - -Avec la division des données, vous pouvez désormais définir et entraîner votre modèle d'apprentissage automatique à l'aide de l'ensemble d'entraînement. Cela pourrait impliquer la sélection de l'algorithme approprié, le réglage des hyperparamètres et la validation croisée. - -=== "Python" - ``` py title="train.py" linenums="1" - #!/usr/bin/env python - - # Importer les paquets nécessaires - from sklearn.ensemble import RandomForestClassifier - from sklearn.model_selection import train_test_split - - # Charger les ensembles de données - data = pd.read_csv("my-dataset.csv") - - # Diviser l'ensemble de données en train et tester - X_train, X_test, y_train, y_test = train_test_split(data.iloc[:, :-1], data.iloc[:, -1], test_size=0.3, random_state=123) - - # Former le modèle - model = RandomForestClassifier(n_estimators=100, random_state=123) - model.fit(X_train, y_train) - - # Imprimer le score de précision sur les données de test - print("Accuracy on test set: {:.3f}".format(model.score(X_test, y_test))) - ``` -=== "R" - ``` r title="train.R" linenums="1" - #!/usr/bin/env Rscript - - # Importer les paquets nécessaires - library(caret) - - # Charger l'ensembles de données - data <- read.csv("my-dataset.csv") - - # Définir la graine pour la reproductibilité - set.seed(123) - - # Diviser l'ensemble de données en train et tester à l'aide de la fonction createDataPartition de caret - train_index <- createDataPartition(data$target_variable, p = 0.7, list = FALSE) - train_data <- data[train_index, ] - test_data <- data[-train_index, ] - - # Définir le contrôle d'entraînement - train_control <- trainControl(method = "cv", number = 5) - - # Entraînez le modèle à l'aide de la fonction d'entraînement de caret, (method = "rf" est pour la forêt aléatoire) - model <- train(target_variable ~ ., data = train_data, method = "rf", trControl = train_control) - - # Imprimez l'objet modèle pour afficher les résultats - print(model) - ``` -=== "SASPy" - ``` py title="train.py" linenums="1" - #!/usr/bin/env python - - # Importer les paquets nécessaires - import saspy - import pandas as pd - - # Établir une connexion à une session SAS - sas = saspy.SASsession() - - # Charger l'ensembles de données - data = pd.read_csv("my-dataset.csv") - - # Télécharger l'ensemble de données sur SAS - sas_df = sas.df2sd(data, "mydata") - - # Diviser l'ensemble de données en train et tester - train, test = sas.surveyselect(data=sas_df, - method="SRS", - seed=123, - samprate=0.7, - outall=True, - strata="target_variable", - partind=True) - - # Former le modèle en utilisant la procédure HPFOREST - model = sas.hpforest(data=train, - target="target_variable", - input="input_variable_1-input_variable_n", - partition="rolevar", - rolevars={"test": "0", "train": "1"}, - nominals=["input_variable_1-input_variable_n"], - forestopts={"ntree": 100, "seed": 123}) - - # Noter le modèle sur les données de test - predictions = model.predict(newdata=test, out=pred_out) - - # Calculer le score de précision sur les données de test - accuracy = sas.freq(data=predictions, tables="target_variable*p_target_variable", nocum=True, nocol=True) - - # Imprimer le score de précision - print("Accuracy on test set: {:.3f}".format(accuracy.Frequency.iloc[0, 1] / accuracy.Frequency.iloc[:, 1].sum())) - - # Se déconnecter de la session SAS - sas.disconnect() - ``` -=== "SAS" - ``` sas title="train.sas" linenums="1" - /* Charger le jeu de données */ - proc import datafile="my-dataset.csv" out=mydata dbms=csv replace; - run; - - /* Divise le jeu de données en train et test */ - proc surveyselect data=mydata method=srs seed=123 out=selected outall - samprate=0.7; - strata target_variable; - run; - - /* Former le modèle */ - proc hpforest data=selected; - class _all_; - target target_variable / level=nominal; - partition rolevar=target_variable(test="0" train="1"); - input _all_; - forest ntree=100 seed=123; - run; - - /* Noter le modèle sur les données de test */ - proc hpforest predict testdata=selected out=testout; - run; - - /* Affiche le score de précision sur les données de test */ - proc freq data=testout; - tables target_variable*p_target_variable / nocum nocol; - run; - ``` - -#### 5. Évaluer le modèle - -Après avoir entraîné le modèle, vous devez évaluer ses performances sur l'ensemble de test. Cela vous donnera une idée de la performance du modèle sur de nouvelles données inédites. - -=== "Python" - ``` py title="evaluate.py" linenums="1" - - ``` -=== "R" - ``` r title="evaluate.R" linenums="1" - - ``` -=== "SASPy" - ``` py title="evaluate.py" linenums="1" - - ``` -=== "SAS" - ``` sas title="evaluate.sas" linenums="1" - - ``` - -#### 6. Déployer le modèle - -Enfin, vous pouvez déployer le modèle d'apprentissage automatique formé dans un environnement de production. - -=== "Python" - ``` py title="deploy.py" linenums="1" - - ``` -=== "R" - ``` r title="deploy.R" linenums="1" - - ``` -=== "SASPy" - ``` py title="deploy.py" linenums="1" - - ``` -=== "SAS" - ``` sas title="deploy.sas" linenums="1" - - ``` - -### Utilisation des workflows Argo - -![Argo Workflows](../images/argo-workflows-assembly-line.jpg) - - -!!! info "Bonnes pratiques MLOps" - Argo Workflows est un excellent outil pour tous ceux qui cherchent à mettre en œuvre des pratiques MLOps et à rationaliser le processus de formation et de déploiement de modèles d'apprentissage automatique ou d'autres tâches de science des données telles que ETL. - -**[Argo Workflows](https://argoproj.github.io/argo-workflows/)** est un engin de workflow open source natif de conteneur pour orchestrer des tâches parallèles sur Kubernetes. Argo Workflows est implémenté en tant que Kubernetes CRD (Custom Resource Definition). Il est particulièrement bien adapté pour une utilisation dans les flux de travail d'apprentissage automatique et de science des données. - -Argo Workflows vous permet de - -- Définissez des flux de travail où chaque étape du flux de travail est un conteneur. -- Modélisez les flux de travail en plusieurs étapes sous la forme d'une séquence de tâches ou capturez les dépendances entre les tâches à l'aide d'un graphe acyclique dirigé (DAG). -- Exécutez facilement des tâches de calcul intensives pour l'apprentissage automatique ou le traitement de données en une fraction du temps à l'aide des flux de travail Argo sur Kubernetes. -- Exécutez des pipelines CI/CD en mode natif sur Kubernetes sans configurer de produits de développement logiciel complexes. - -ce qui facilite la gestion de l'ensemble du pipeline d'apprentissage automatique de bout en bout. Avec Argo Workflows, vous pouvez facilement créer des workflows qui intègrent des tâches telles que le prétraitement des données, la formation de modèles et le déploiement de modèles, le tout dans un environnement Kubernetes. - - -!!! info "" -
- [![Flux de travail Argo](../images/argo-workflows.jpg)](https://argoproj.github.io/argo-workflows/) -

Flux de travail Argo

-
- -Vous trouverez ci-dessous les étapes pour former un modèle d'apprentissage automatique à l'aide d'Argo Workflows sur l'ETAA. - -#### 1. Écrivez un script pour entraîner votre modèle - -Voici un exemple de script qui entraîne un modèle de régression logistique sur l'ensemble de données iris. N'oubliez pas de consulter le code de chaque langue ci-dessous. - -=== "Python" - - ``` python title="train.py" linenums="1" - #!/usr/bin/env python - - # Importer les bibliothèques nécessaires - import argparse - import pandas as pd - from sklearn.datasets import load_iris - from sklearn.linear_model import LogisticRegression - from sklearn.model_selection import train_test_split - from sklearn.metrics import accuracy_score - import joblib - - # Analyser les arguments d'entrée - parser = argparse.ArgumentParser(description="Train logistic regression model on iris dataset.") - parser.add_argument("--input", default="iris.csv", help="Path to input dataset file.") - parser.add_argument("--output", default="model.pkl", help="Path to output model file.") - args = parser.parse_args() - - # Charger le jeu de données de l'iris - data = load_iris() - X, y = data.data, data.target - - # Diviser les données en ensembles d'entraînement et de test - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - - # Former le modèle de régression logistique - clf = LogisticRegression(random_state=42) - clf.fit(X_train, y_train) - - # Évaluer le modèle sur l'ensemble de test - y_pred = clf.predict(X_test) - accuracy = accuracy_score(y_test, y_pred) - print("Accuracy:", accuracy) - - # Enregistrer le modèle dans un fichier - joblib.dump(clf, args.output) - ``` - -=== "R" - - ``` r title="train.R" linenums="1" - #!/usr/bin/env Rscript - - # Importer les bibliothèques nécessaires - library(caret) - - # Analyser les arguments d'entrée - args <- commandArgs(trailingOnly = TRUE) - input_file <- ifelse(length(args) > 0, args[1], "iris.csv") - output_file <- ifelse(length(args) > 1, args[2], "model.rds") - - # Charger le jeu de données de l'iris - data(iris) - X <- iris[, 1:4] - y <- iris[, 5] - - # Diviser les données en ensembles d'entraînement et de test - set.seed(42) - train_index <- createDataPartition(y, p = 0.8, list = FALSE) - X_train <- X[train_index, ] - y_train <- y[train_index] - X_test <- X[-train_index, ] - y_test <- y[-train_index] - - # Former le modèle de régression logistique - clf <- train(x = X_train, y = y_train, method = "glm") - - # Évaluer le modèle sur l'ensemble de test - y_pred <- predict(clf, newdata = X_test) - accuracy <- confusionMatrix(y_pred, y_test)$overall["Accuracy"] - print(paste0("Accuracy: ", accuracy)) - - # Enregistrer le modèle dans un fichier - saveRDS(clf, output_file) - ``` - -#### 2. Écrivez un Dockerfile pour exécuter votre code - -Vous aurez besoin d'un Dockerfile qui inclut toutes les dépendances nécessaires pour former votre modèle d'apprentissage automatique. Cela pourrait inclure - -- des forfaits comme - - `scikit-learn`, `pandas` ou `numpy` si vous utilisez `Python` - - `caret`, `janitor` et `tidyverse` si vous utilisez `R` -- vos propres bibliothèques ou scripts personnalisés -- le code de votre modèle de l'apprentissage automatique sous la forme d'un script [comme dans l'exemple ci-dessus](#1-write-a-script-to-train-your-model). - -Utilisez le `Dockerfile` suivant comme point de départ pour vos projets `R` et `Python`. - -=== "Python" - - ``` docker title="Dockerfile" linenums="1" - FROM python:3.8-slim-buster - - # Installez toutes les dépendances nécessaires - RUN pip install --no-cache-dir scikit-learn pandas numpy - - # Définir le répertoire de travail - WORKDIR /app - - # Copier le code dans le conteneur - COPY train.py . - - # Définir le point d'entrée - ENTRYPOINT ["python", "train.py"] - ``` - -=== "R" - ``` docker title="Dockerfile" linenums="1" - FROM rocker/r-base:latest - - RUN apt-get update && apt-get install -y --no-install-recommends \ - libssl-dev \ - libcurl4-openssl-dev \ - libxml2-dev \ - && apt-get clean \ - && rm -rf /var/lib/apt/lists/* - - RUN R -e 'install.packages(c("caret", "janitor", "tidyverse"))' - - COPY train.R /app/train.R - - WORKDIR /app - - ENTRYPOINT ["Rscript", "train.R"] - ``` - -#### 3. Écrivez votre workflow en YAML - -YAML est encore un autre langage de balisage et vous devrez écrire les étapes de votre pipeline de formation dans un fichier YAML Argo Workflows. Ce fichier doit inclure une référence au Dockerfile que vous avez créé à l'[Étape 1](#2-write-a-dockerfile-to-run-your-code), ainsi que toutes les données d'entrée et de sortie avec lesquelles vous travaillerez. - -Voici un exemple de fichier YAML pour un pipeline d'apprentissage automatique simple qui forme un modèle de régression logistique sur l'ensemble de données iris. La seule vraie différence entre les versions `Python` et `R` est la commande `command : ["python", "train.py"]` vs `command : ["Rscript", "train.R"]` et la les modèles sont stockés dans différents formats, `pkl` pour `python` et `rds` pour `R`. - -Le fichier YAML définit une seule étape appelée "train" qui exécute un script appelé "train.py" ou "train.R" dans l'image Docker "machine-learning:v1". Le script prend un fichier d'ensemble de données d'entrée, spécifié par un paramètre appelé `dataset`, et génère un fichier de modèle entraîné vers un artefact de sortie appelé `model.pkl` ou `model.rds` selon le langage utilisé. - -=== "Python" - ``` yaml title="workflow.yaml" linenums="1" - apiVersion: argoproj.io/v1alpha1 - kind: Workflow - metadata: - generateName: ml-pipeline- - spec: - entrypoint: train - templates: - - name: train - container: - image: machine-learning:v1 - command: ["python", "train.py"] - args: ["--input", "{{inputs.parameters.dataset}}", "--output", "{{outputs.artifacts.model}}"] - inputs: - parameters: - - name: dataset - default: "iris.csv" - outputs: - artifacts: - - name: model - path: /output/model.pkl - ``` -=== "R" - ``` yaml title="workflow.yaml" linenums="1" - apiVersion: argoproj.io/v1alpha1 - kind: Workflow - metadata: - generateName: ml-pipeline- - spec: - entrypoint: train - templates: - - name: train - container: - image: machine-learning:v1 - command: ["Rscript", "train.R"] - args: ["--input", "{{inputs.parameters.dataset}}", "--output", "{{outputs.artifacts.model}}"] - inputs: - parameters: - - name: dataset - default: "iris.csv" - outputs: - artifacts: - - name: model - path: /output/model.rds - ``` - - -#### 4. Soumettez le workflow à l'aide de la CLI Argo Workflows - -Pour exécuter le flux de travail ci-dessus, vous devez d'abord envoyer le fichier Dockerfile à notre registre de conteneurs, puis envoyer le fichier YAML à l'aide de la commande "argo submit". Une fois le pipeline terminé, vous pouvez récupérer le fichier de modèle formé en téléchargeant l'artefact de sortie à partir de la commande `argo logs`. - -``` bash title="Terminal" -$ argo submit workflow.yaml # soumettre une spécification de workflow à Kubernetes -``` - -#### 5. Surveiller le pipeline à l'aide de la CLI Argo Workflows - -Au fur et à mesure que le pipeline s'exécute, vous pouvez surveiller sa progression à l'aide de la CLI Argo Workflows. Cela vous montrera quelles étapes ont réussi et lesquelles sont toujours en cours. Vous trouverez ci-dessous quelques commandes utiles. Pour plus d'informations sur la CLI Argo Workflows, veuillez consulter [la documentation officielle de la CLI Argo Workflows](https://argoproj.github.io/argo-workflows/walk-through/argo-cli/) . - -``` bash title="Terminal" -$ argo list # liste les workflows actuels -$ argo get workflow-xxx # obtenir des informations sur un workflow spécifique -$ argo logs workflow-xxx # imprimer les logs d'un workflow -$ argo delete workflow-xxx # supprimer le workflow - -``` - -#### 6. Récupérer le modèle formé - -Une fois le pipeline terminé, vous pouvez récupérer les données de sortie à l'aide de la commande argo logs ou en affichant les artefacts de sortie à l'aide de la CLI, c'est-à-dire accéder au répertoire que vous avez spécifié dans votre script et localiser le fichier `model.pkl` ou `model. rds`. L'extrait de code suivant, tiré du [script de formation ci-dessus](#1-define-the-machine-learning-model-and-its-dependencies), indique au langage de programmation respectif où enregistrer le modèle formé. - -=== "Python" - - ``` python title="Enregistrement des données de sortie" linenums="1" - #!/usr/bin/env python - - parser.add_argument("--output", default="model.pkl", help="Path to output model file.") - - # Enregistrer le modèle dans un fichier - joblib.dump(clf, args.sortie) - ``` - -=== "R" - - ``` r title="Enregistrement des données de sortie" linenums="1" - #!/usr/bin/env Rscript - - output_file <- ifelse(length(args) > 1, args[2], "model.rds") - - # Enregistrer le modèle dans un fichier - saveRDS(clf, output_file) - ``` - -### Exemples d'utilisation de SDK basés sur Argo Workflows - -Si vous préférez utiliser un cadre de niveau supérieur, nous avons `Couler` et `Hera`. Ces cadres rendent la création et la gestion de flux de travail complexes plus accessibles à un public plus large. - -#### Hera - -Hera vise à simplifier le processus de création et de soumission des flux de travail en éliminant de nombreux détails techniques via une interface de programmation d'application simple. Il utilise également un ensemble cohérent de terminologie et de concepts qui s'alignent sur Argo Workflows, ce qui permet aux utilisateurs d'apprendre et d'utiliser plus facilement les deux outils ensemble. - -#### Couler - -Couler fournit une interface de programmation d'application simple et unifiée pour définir les flux de travail à l'aide d'un style de programmation impératif. Il construit également automatiquement des graphes acycliques dirigés (DAG) pour les flux de travail, ce qui peut aider à simplifier le processus de création et de gestion de ceux-ci. - -=== "Couler" - ``` py title="couler.py" linenums="1" - #!/usr/bin/env python - - # Préparez votre système - !pip config --user set global.index-url https://jfrog.ETAA.cloud.statcan.ca/artifactory/api/pypi/pypi-remote/simple - !python3 -m pip install git+https://github.com/couler-proj/couler --ignore-installed - - # Définir la variable globale pour plus de commodité - - NAMESPACE = "" - - # Importer les paquets nécessaires - import json - import random - - import couler.argo as couler - from couler.argo_submitter import ArgoSubmitter - - - # Définir les étapes (fonctions) utilisées dans le workflow - def random_code(): - import random - res = "heads" if random.randint(0, 1) == 0 else "tails" - print(res) - - - def flip_coin(): - return couler.run_script( - image="k8scc01covidacr.azurecr.io/ubuntu", - source=random_code - ) - - - def heads(): - return couler.run_container( - image="k8scc01covidacr.azurecr.io/ubuntu", - command=["sh", "-c", 'echo "it was heads"'] - ) - - - def tails(): - return couler.run_container( - image="k8scc01covidacr.azurecr.io/ubuntu", - command=["sh", "-c", 'echo "it was tails"'] - ) - - - result = flip_coin() - - couler.when(couler.equal(result, "heads"), lambda: heads()) - couler.when(couler.equal(result, "tails"), lambda: tails()) - - submitter = ArgoSubmitter(namespace="NAMESPACE") - result = couler.run(submitter=submitter) - - print(json.dumps(result, indent=2)) - ``` -=== "Hera" - ``` py title="hera.py" linenums="1" - #!/usr/bin/env python - - # Préparez votre système - !pip config --user set global.index-url https://jfrog.ETAA.cloud.statcan.ca/artifactory/api/pypi/pypi-remote/simple - !pip install hera-workflows - - # Importer les paquets nécessaires - import hera - from hera import Task, Workflow - - # Configurer Hera - hera.global_config.GlobalConfig.token = "" - hera.global_config.GlobalConfig.host = "https://argo-workflows.ETAA-dev.cloud.statcan.ca:443" - hera.global_config.GlobalConfig.namespace = "" - hera.global_config.GlobalConfig.service_account_name = "" - - - # Définir les étapes (fonctions) utilisées dans le workflow - def random_code(): - res = "heads" if random.randint(0, 1) == 0 else "tails" - print(res) - - - def heads(): - print("it was heads") - - - def tails(): - print("it was tails") - - - # Définir le flux de travail - with Workflow("coin-flip") as w: - r = Task("r", random_code) - h = Task("h", heads) - t = Task("t", tails) - - h.on_other_result(r, "heads") - t.on_other_result(r, "tails") - - # Exécuter le flux de travail - w.create() - ``` -=== "YAML" - ``` py title="workflow.yaml" linenums="1" - - ``` -=== "Seldon ?" - ``` py - - ``` - -### Ressources supplémentaires pour les workflows Argo - -Des exemples de workflows Argo Workflows peuvent être trouvés dans les répos Github suivants : - -- [Documentation Argo Workflows](https://argoproj.github.io/argo-workflows/) -- [Référence Argo CLI] (https://argoproj.github.io/argo-workflows/walk-through/argo-cli/) diff --git a/docs/fr/3-Pipelines/Machine-Learning.md b/docs/fr/3-Pipelines/Machine-Learning.md deleted file mode 100644 index 8f57449a5..000000000 --- a/docs/fr/3-Pipelines/Machine-Learning.md +++ /dev/null @@ -1,369 +0,0 @@ -# Modèles d'apprentissage automatique - -Les modèles d'apprentissage automatique sont des algorithmes de calcul conçus pour apprendre automatiquement des modèles et des relations à partir de données. Ces modèles sont entraînés sur un ensemble de données, qui est généralement une collection d'exemples ou d'instances, chacun d'entre eux se composant d'un ensemble de fonctionnalités ou de variables, ainsi que d'une variable cible ou d'une sortie. - -L'objectif d'un modèle d'apprentissage automatique est d'identifier des modèles et des relations au sein des données qui peuvent être utilisés pour faire des prédictions ou des décisions concernant de nouvelles données invisibles. Cela implique de développer une représentation mathématique de la relation entre les caractéristiques d'entrée et la variable de sortie, sur la base des modèles observés dans les données d'apprentissage. Une fois le modèle entraîné, il peut être utilisé pour faire des prédictions ou prendre des décisions concernant de nouvelles données inédites. - -Il existe plusieurs types de modèles d'apprentissage automatique, chacun étant conçu pour traiter différents types de problèmes ou de données. Certains des types les plus courants de modèles d'apprentissage automatique incluent : - -1. **Modèles de régression :** Les modèles de régression sont utilisés pour prédire des valeurs numériques continues, telles que les cours des actions ou les prix des logements. - -2. **Modèles de classification :** Les modèles de classification sont utilisés pour prédire des valeurs catégorielles discrètes, par exemple si un client achètera ou non un produit. - -3. **Modèles de clustering :** Les modèles de clustering sont utilisés pour identifier des groupes ou des clusters au sein d'un ensemble de données en fonction des similitudes entre les instances. - -4. **Modèles de recommandation :** Les modèles de recommandation sont utilisés pour recommander des produits ou des services aux utilisateurs en fonction de leur comportement ou de leurs préférences passés. - -5. **Réseaux de neurones :** Les réseaux de neurones sont un type de modèle d'apprentissage automatique conçu pour imiter la structure et la fonction du cerveau humain. Ils sont couramment utilisés dans les applications de reconnaissance d'images, de reconnaissance vocale et de traitement du langage naturel. - - -!!! info "Les modèles d'apprentissage automatique peuvent être biaisés" - Les modèles d'apprentissage automatique sont un outil puissant pour analyser et faire des prédictions sur les données, et ils ont un large éventail d'applications dans des domaines tels que la finance, la santé, le marketing, etc. Cependant, il est important de noter que les modèles d'apprentissage automatique ne sont pas parfaits et peuvent parfois faire des erreurs ou produire des résultats biaisés. Par conséquent, il est important d'évaluer et de tester soigneusement les modèles d'apprentissage automatique avant de les utiliser dans des applications réelles. - -## Exemples - -### Régression linéaire - -![Régression linéaire](../images/linear-regression.jpg) - - -!!! info "Régression linéaire" - $$ - \hat{Y}_i = \hat{\beta}_0 + \hat{\beta}_1 X_i + \hat{\epsilon}_i - $$ - - _Où $\hat{Y}_i$ désigne le $i$ième estimateur de la vraie valeur $Y$ en fonction de la $i$ième période d'apprentissage. Chaque $\hat{\beta}$ est un paramètre à apprendre. $\hat{\epsilon}_i$ est la quantité de bruit autorisée dans le modèle et peut varier en fonction du nombre d'époques d'entraînement indiqué par $i$. Chaque $X_i$ représente le $i$ième lot de données d'apprentissage._ - -Dans les modèles statistiques classiques comme la régression linéaire, l'objectif est de trouver une ligne qui correspond le mieux aux données, nous permettant de faire des prédictions sur de nouveaux points de données. - -À mesure que la complexité du problème augmente, des algorithmes plus sophistiqués sont nécessaires, tels que des arbres de décision, des machines à vecteurs de support et des forêts aléatoires. Cependant, ces méthodes ont des limites et peuvent ne pas être en mesure de capturer des modèles complexes dans de grands ensembles de données. - -#### Exemple de code - -=== "Python" - ``` py title="linear_regression.py" linenums="1" - #!/usr/bin/env python - - # Charger les bibliothèques requises - import pandas as pd - from sklearn.model_selection import train_test_split - from sklearn.linear_model import LinearRegression - from sklearn.metrics import mean_squared_error - - # Charger le jeu de données - data = pd.read_csv('path/to/dataset.csv') - - # Diviser les données en ensembles d'entraînement et de test - X_train, X_test, y_train, y_test = train_test_split(data.drop('target_variable', axis=1), data['target_variable'], test_size=0.2) - - # Former le modèle de régression linéaire - linear_model = LinearRegression() - linear_model.fit(X_train, y_train) - - # Faire des prédictions sur l'ensemble de test - y_pred = linear_model.predict(X_test) - - # Évaluer les performances du modèle - mse = mean_squared_error(y_test, y_pred) - rmse = mse ** 0.5 - print('Root Mean Squared Error:', rmse) - ``` - -=== "R" - ``` r title="linear_regression.r" linenums="1" - #!/usr/bin/env Rscript - - # Définir une graine aléatoire pour la reproductibilité - set.seed(123) - - # Charger le jeu de données - data <- read.csv('path/to/dataset.csv') - - # Diviser les données en ensembles d'entraînement et de test - train_index <- sample(1:nrow(data), size=0.8*nrow(data)) - train_data <- data[train_index,] - test_data <- data[-train_index,] - - # Former le modèle de régression linéaire - lm_model <- lm(target_variable ~ ., data=train_data) - - # Faire des prédictions sur l'ensemble de test - y_pred <- predict(lm_model, newdata=test_data[,-which(names(test_data)=='target_variable')]) - - # Évaluer les performances du modèle - mse <- mean((y_pred - test_data$target_variable)^2) - rmse <- sqrt(mse) - print(paste('Root Mean Squared Error:', rmse)) - ``` - -### Machine à vecteurs de support (SVM) - -![Supporter la machine vectorielle](../images/svm.jpg) - - -!!! note "SVM" - $$ - \underset{\mathbf{w},b,\boldsymbol{\xi}}{\operatorname{minimize}} \hspace{0.2cm} \frac{1}{2} ||\mathbf{w}||^2 + C \sum_{i=1}^{N} \xi_i - $$ - - $$ - \text{où} \hspace{0.2cm} y_i(\mathbf{w}^T\mathbf{x}_i + b) \geq 1-\xi_i \quad \text{and} \quad \hspace{0.2cm} \xi_i \geq 0 \hspace{0.2cm} \forall i \in {1,2,...,N} - $$ - - _Dans cette formule, nous utilisons la formulation SVM standard où $\mathbf{w}$ est le vecteur de poids, $b$ est le terme de biais et $\boldsymbol{\xi}$ est le vecteur variable d'écart. L'objectif est de minimiser la norme L2 du vecteur de poids $\mathbf{w}$, sous la contrainte que tous les exemples d'apprentissage sont classés correctement avec une marge d'au moins 1, plus une tolérance pour certaines violations de marge contrôlées par le paramètre de régularisation $C$. La variable cible $y_i$ prend les valeurs 1 ou -1, représentant les deux classes du problème de classification binaire, et $\mathbf{x}_i$ est le vecteur de caractéristiques pour le $i$ième exemple d'entraînement._ - -Une machine à vecteurs de support (SVM) est un algorithme d'apprentissage automatique supervisé qui peut être utilisé pour la classification, la régression et la détection des valeurs aberrantes. C'est un algorithme populaire dans le domaine de l'apprentissage automatique, en particulier pour résoudre les problèmes de classification. - -L'idée de base derrière SVM est de trouver un hyperplan qui sépare au mieux les données d'entrée en différentes classes. Dans un problème de classification à deux classes, l'hyperplan est une ligne qui sépare les points de données d'une classe des points de données de l'autre classe. SVM essaie de trouver l'hyperplan qui maximise la marge entre les deux classes, où la marge est la distance entre l'hyperplan et les points de données les plus proches de chaque classe. - -#### Exemple de code - -=== "Python" - ``` py title="svm.py" linenums="1" - #!/usr/bin/env python - - # Charger les bibliothèques requises - import pandas as pd - from sklearn.model_selection import train_test_split - from sklearn.svm import SVC - from sklearn.metrics import accuracy_score - - # Charger le jeu de données - data = pd.read_csv('path/to/dataset.csv') - - # Diviser les données en ensembles d'entraînement et de test - X_train, X_test, y_train, y_test = train_test_split(data.drop('target_variable', axis=1), data['target_variable'], test_size=0.2) - - # Former le modèle SVM - svm_model = SVC(kernel='linear', C=1.0, random_state=42) - svm_model.fit(X_train, y_train) - - # Faire des prédictions sur l'ensemble de test - y_pred = svm_model.predict(X_test) - - # Évaluer les performances du modèle - accuracy = accuracy_score(y_test, y_pred) - print('Accuracy:', accuracy) - ``` - -=== "R" - - ``` r title="svm.r" linenums="1" - #!/usr/bin/env Rscript - - # Charger les bibliothèques requises - library(e1071) - - # Charger le jeu de données - data <- read.csv('path/to/dataset.csv') - - # Diviser les données en ensembles d'entraînement et de test - set.seed(123) - train_index <- sample(1:nrow(data), size=0.8*nrow(data)) - train_data <- data[train_index,] - test_data <- data[-train_index,] - - # Former le modèle SVM - svm_model <- svm(target_variable ~ ., data=train_data, kernel='linear', cost=1) - - # Faire des prédictions sur l'ensemble de test - y_pred <- predict(svm_model, newdata=test_data[,-which(names(test_data)=='target_variable')]) - - # Évaluer les performances du modèle - accuracy <- mean(y_pred == test_data$target_variable) - print(paste('Accuracy:', accuracy)) - ``` - -### Forêt aléatoire - -![Forêt aléatoire](../images/random-forest.jpg) - - -!!! note "Forêt aléatoire" - $$ - \hat{y} = \frac{1}{T} \sum_{t=1}^{T} f_t(\mathbf{x}), - $$ - - _où $\hat{y}$ est la sortie prédite, $f_t(\mathbf{x})$ est la prédiction du $t$ième arbre de la forêt pour l'entrée $\mathbf{x}$, et $T $ est le nombre d'arbres dans la forêt._ - -Les forêts aléatoires sont une méthode d'apprentissage d'ensemble qui peut être utilisée pour les problèmes de classification et de régression. Ils sont souvent utilisés pour leur capacité à gérer des ensembles de données dimensionnelles à haute variation et des relations non linéaires entre les entités et les cibles. - -Chaque arbre est entraîné sur un sous-ensemble amorcé des données d'entraînement d'origine, et à chaque division, un sous-ensemble aléatoire de caractéristiques est pris en compte pour déterminer la division. La prédiction finale est obtenue en faisant la moyenne des prédictions de tous les arbres de la forêt. - -#### Exemple de code - -=== "Python" - - ``` py title="random_forest.py" linenums="1" - #!/usr/bin/env python - - # Charger les bibliothèques requises - import pandas as pd - from sklearn.model_selection import train_test_split - from sklearn.ensemble import RandomForestRegressor - from sklearn.metrics import mean_squared_error - - # Charger le jeu de données - data = pd.read_csv('path/to/dataset.csv') - - # Diviser les données en ensembles d'entraînement et de test - X_train, X_test, y_train, y_test = train_test_split(data.drop('target_variable', axis=1), data['target_variable'], test_size=0.2) - - # Former le modèle de forêt aléatoire - rf_model = RandomForestRegressor(n_estimators=100, random_state=42) - rf_model.fit(X_train, y_train) - - # Faire des prédictions sur l'ensemble de test - y_pred = rf_model.predict(X_test) - - # Évaluer les performances du modèle - mse = mean_squared_error(y_test, y_pred) - rmse = mse ** 0.5 - print('Root Mean Squared Error:', rmse) - ``` - -=== "R" - - ``` r title="random_forest.r" linenums="1" - #!/usr/bin/env Rscript - - # Charger les bibliothèques requises - library(randomForest) - - # Charger le jeu de données - data <- read.csv('path/to/dataset.csv') - - # Diviser les données en ensembles d'entraînement et de test - set.seed(123) - train_index <- sample(1:nrow(data), size=0.8*nrow(data)) - train_data <- data[train_index,] - test_data <- data[-train_index,] - - # Former le modèle de forêt aléatoire - rf_model <- randomForest(target_variable ~ ., data=train_data, ntree=100, importance=TRUE) - - # Faire des prédictions sur l'ensemble de test - y_pred <- predict(rf_model, newdata=test_data[,-which(names(test_data)=='target_variable')]) - - # Évaluer les performances du modèle - mse <- mean((y_pred - test_data$target_variable)^2) - rmse <- sqrt(mse) - print(paste('Root Mean Squared Error:', rmse)) - ``` - -### L'apprentissage en profondeur - -![Apprentissage en profondeur](../images/deep-learning.jpg) - - -!!! note "Apprentissage en profondeur" - $$ - \hat{y} = f(\mathbf{W}_L f(\mathbf{W}_{L-1} f(\dots f(\mathbf{W}_1\mathbf{x}+\mathbf{b} _1)\dots)+\mathbf{b}_{L-1})+\mathbf{b}_L) - $$ - - _où $\mathbf{x}$ est le vecteur d'entrée, $\mathbf{W}_i$ et $\mathbf{b}_i$ sont respectivement la matrice de pondération et le vecteur de biais pour la $i$ième couche, et $ f$ est la fonction d'activation._ - - _Cette formule représente un réseau de neurones à anticipation avec des couches $L$, où chaque couche applique une transformation linéaire à la sortie de la couche précédente, suivie d'une fonction d'activation non linéaire. La sortie de la couche finale, $\hat{y}$, représente la sortie prédite du réseau de neurones pour l'entrée donnée $\mathbf{x}$._ - -L'apprentissage en profondeur est un sous-ensemble de l'apprentissage automatique qui implique la formation de réseaux de neurones avec de nombreuses couches de nœuds interconnectés. Cette approche peut gérer des ensembles de données volumineux et complexes et est utilisée dans un large éventail d'applications, notamment la reconnaissance d'images, le traitement du langage naturel et la reconnaissance vocale. Le processus de formation consiste à alimenter le réseau de neurones avec un grand ensemble de données et à ajuster les poids des connexions entre les nœuds pour minimiser l'erreur entre les sorties prédites et les sorties réelles. Grâce à des itérations répétées, le réseau de neurones peut apprendre à reconnaître des modèles dans les données et à faire des prédictions précises sur de nouvelles données. - -#### Exemple de code - -=== "Python" - ``` py title="deep_learning.py" linenums="1" - #!/usr/bin/env python - - # Charger les bibliothèques requises - import pandas as pd - import numpy as np - import tensorflow as tf - from tensorflow import keras - from sklearn.model_selection import train_test_split - from sklearn.preprocessing import StandardScaler - from sklearn.metrics import accuracy_score - - # Charger le jeu de données - data = pd.read_csv('path/to/dataset.csv') - - # Diviser les données en ensembles d'entraînement et de test - X_train, X_test, y_train, y_test = train_test_split(data.drop('target_variable', axis=1), data['target_variable'], test_size=0.2) - - # Standardiser les caractéristiques d'entrée - scaler = StandardScaler() - X_train_scaled = scaler.fit_transform(X_train) - X_test_scaled = scaler.transform(X_test) - - # Définir le modèle de deep learning - model = keras.Sequential([ - keras.layers.Dense(64, activation='relu', input_shape=[X_train_scaled.shape[1]]), - keras.layers.Dropout(0.2), - keras.layers.Dense(1, activation='sigmoid') - ]) - - # Compiler le modèle - model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) - - # Former le modèle - history = model.fit(X_train_scaled, y_train, epochs=50, batch_size=32, validation_split=0.1) - - # Évaluer les performances du modèle - y_pred = model.predict_classes(X_test_scaled) - accuracy = accuracy_score(y_test, y_pred) - print('Accuracy:', accuracy) - ``` - -=== "R" - - ``` r title="deep_learning.r" linenums="1" - #!/usr/bin/env Rscript - - # Charger les bibliothèques requises - library(keras) - library(tensorflow) - - # Charger le jeu de données - data <- iris - x <- as.matrix(data[, 1:4]) - y <- to_categorical(as.numeric(data[, 5]) - 1) - - # Diviser les données en ensembles d'entraînement et de test - set.seed(123) - train_index <- sample(1:nrow(data), size=0.8*nrow(data)) - x_train <- x[train_index,] - y_train <- y[train_index,] - x_test <- x[-train_index,] - y_test <- y[-train_index,] - - # Définir l'architecture du réseau neuronal - model <- keras_model_sequential() %>% - layer_dense(units = 8, input_shape = c(4)) %>% - layer_activation('relu') %>% - layer_dense(units = 3) %>% - layer_activation('softmax') - - # Compiler le modèle - model %>% compile( - loss = 'categorical_crossentropy', - optimizer = 'adam', - metrics = c('accuracy') - ) - - # Former le modèle - history <- model %>% fit( - x_train, y_train, - epochs = 50, - batch_size = 10, - validation_split = 0.2, - verbose = 0 - ) - - # Évaluer les performances du modèle - metrics <- model %>% evaluate(x_test, y_test) - print(paste('Test Loss:', metrics[1])) - print(paste('Test Accuracy:', metrics[2])) - - # Tracez la précision de la formation et de la validation dans le temps - plot(history$metrics$accuracy, type='l', col='blue', ylim=c(0,1), ylab='Accuracy', xlab='Epoch') - lines(history$metrics$val_accuracy, type='l', col='red') - legend('bottomright', legend=c('Training', 'Validation'), col=c('blue', 'red'), lty=1) - ``` \ No newline at end of file diff --git a/docs/fr/3-Pipelines/Overview.md b/docs/fr/3-Pipelines/Overview.md deleted file mode 100644 index 4015d3035..000000000 --- a/docs/fr/3-Pipelines/Overview.md +++ /dev/null @@ -1,17 +0,0 @@ -MLOps, ou lopérations d'apprentissage automatique, fait référence à l'ensemble de pratiques et d'outils qui permettent aux organisations de développer, déployer et maintenir des modèles d'apprentissage automatique à grande échelle. MLOps vise à rationaliser le processus de bout en bout de création et de déploiement de modèles d'apprentissage automatique en intégrant les différentes étapes du cycle de vie de l'apprentissage automatique dans un flux de travail cohérent et automatisé. - -MLOps implique une gamme d'activités différentes, y compris la préparation et le prétraitement des données, la formation et l'optimisation des modèles, le déploiement et le service des modèles, la surveillance et la maintenance et l'amélioration continue. Certains des composants clés des MLOps incluent : - -1. **Gestion des données :** MLOps implique la gestion et le traitement de grandes quantités de données pour garantir la qualité et la précision des modèles d'apprentissage automatique. Cela implique des activités telles que le nettoyage, l'intégration et la transformation des données. - -2. **Entraînement et optimisation des modèles :** MLOps implique de développer et de tester des modèles d'apprentissage automatique, ainsi que de les optimiser en termes de performances et de précision. Cela peut impliquer d'expérimenter différents algorithmes, hyperparamètres et techniques de prétraitement des données. - -3. **Déploiement de modèles :** MLOps implique le déploiement de modèles d'apprentissage automatique dans des environnements de production, les rendant disponibles pour une utilisation dans des applications du monde réel. Cela peut impliquer la mise en conteneur de modèles pour un déploiement et une mise à l'échelle faciles, ainsi que la configuration d'API et d'autres interfaces pour la diffusion de modèles. - -4. **Surveillance et maintenance :** MLOps implique la surveillance des modèles d'apprentissage automatique en production pour s'assurer qu'ils fonctionnent comme prévu. Cela peut impliquer la configuration d'alertes et de notifications pour les défaillances du modèle, ainsi que la mise en œuvre de processus pour la maintenance et les mises à jour du modèle. - -5. **Amélioration continue :** MLOps implique l'amélioration continue des modèles d'apprentissage automatique au fil du temps, en fonction des commentaires des utilisateurs et de l'analyse continue des données de performance. Cela peut impliquer de recycler les modèles avec de nouvelles données ou d'intégrer les commentaires des utilisateurs pour affiner les modèles. - -Afin de mettre en œuvre efficacement les MLOps, les organisations doivent généralement adopter une gamme d'outils et de technologies différents, notamment des plates-formes de gestion de données, des cadres d'apprentissage automatique, des outils de conteneurisation et des outils de surveillance et de journalisation. Ils doivent également établir des flux de travail et des processus clairs pour gérer les différentes étapes du cycle de vie de l'apprentissage automatique, ainsi que mettre en œuvre des mesures de gouvernance et de conformité pour garantir la confidentialité et la sécurité des données. - -En résumé, MLOps est un composant essentiel du cycle de vie de l'apprentissage automatique, permettant aux organisations de développer, déployer et maintenir des modèles d'apprentissage automatique à grande échelle. En adoptant des pratiques et des outils MLOps, les organisations peuvent rationaliser leurs flux de travail d'apprentissage automatique, améliorer la précision et les performances des modèles et offrir plus de valeur aux utilisateurs et aux parties prenantes. diff --git a/docs/fr/3-Pipelines/PaaS-Integration.md b/docs/fr/3-Pipelines/PaaS-Integration.md deleted file mode 100644 index bf03427f1..000000000 --- a/docs/fr/3-Pipelines/PaaS-Integration.md +++ /dev/null @@ -1,52 +0,0 @@ -# Aperçu - -_L'un des principaux avantages de la plate-forme ETAA est sa capacité à s'intégrer aux plates-formes d'apprentissage automatique populaires telles que Databricks et AzureML._ - -Espace de travail d'analyse avancée (ETAA) est une plate-forme d'analyse de données open source conçue pour être hautement intégrable. Cela signifie qu'il peut être facilement intégré à d'autres plates-formes et outils pour étendre ses capacités et rationaliser les flux de travail. - -Un exemple de diagramme illustrant une stratégie de connexion PaaS possible : - -
- -
-![PaaS](../images/PaaS.png) -
- -**Configuration :** Si vous avez besoin d'aide pour intégrer une plate-forme en tant qu'offre de service, nous sommes heureux -aider! - -## Intégration avec les offres de plate-forme externe en tant que service (PaaS) - -_L'intégration est la clé du succès._ - -[![Intégration avec PaaS](../images/IntegratePaaS.PNG)]() - -Notre plate-forme open source offre une option inégalée à nos utilisateurs. En permettant aux utilisateurs d'utiliser des outils open source, nous leur donnons la possibilité d'utiliser leurs frameworks de science des données et d'apprentissage automatique préférés. Mais la véritable puissance de notre plateforme vient de sa capacité à s'intégrer à de nombreuses offres de plateforme en tant que service (PaaS), comme Databricks ou AzureML. Cela signifie que nos utilisateurs peuvent tirer parti de la puissance du cloud pour exécuter des pipelines complexes de traitement de données et d'apprentissage automatique à grande échelle. Avec la possibilité de s'intégrer aux offres PaaS, notre plate-forme permet à nos utilisateurs de faire passer leur travail au niveau supérieur, en leur donnant le pouvoir d'adapter facilement leurs charges de travail et de tirer parti des dernières innovations dans le domaine de la science des données et de la machine. apprentissage. En offrant ce niveau d'optionnalité, nous nous assurons que nos utilisateurs peuvent toujours choisir le bon outil pour le travail et garder une longueur d'avance dans un domaine en évolution constante. - -Nous pouvons nous intégrer à de nombreuses offres de plate-forme en tant que service (PaaS), comme Databricks ou AzureML. - -## Briques de données - -- [Databricks de Microsoft](https://azure.microsoft.com/en-ca/services/databricks/) - -Databricks est une plate-forme basée sur le cloud qui fournit une plate-forme d'analyse unifiée pour le traitement du Big Data et l'apprentissage automatique. Avec son puissant moteur de calcul distribué et ses outils de flux de travail rationalisés, Databricks est un choix populaire pour créer et déployer des modèles d'apprentissage automatique. En s'intégrant à Databricks, la plate-forme ETAA peut tirer parti de ses capacités informatiques distribuées pour former et déployer des modèles d'apprentissage automatique à grande échelle. - -## AzureML - -- [Azure ML de Microsoft](https://azure.microsoft.com/en-us/services/machine-learning/) - -AzureML est une autre plate-forme d'apprentissage automatique populaire qui fournit une large gamme d'outils pour créer, former et déployer des modèles d'apprentissage automatique. En s'intégrant à AzureML, la plateforme ETAA peut tirer parti de ses puissants outils de création et de formation de modèles, ainsi que de sa capacité à déployer des modèles dans le cloud. - -### Exemples - -_Des exemples d'intégration de la plate-forme ETAA avec ces plates-formes et d'autres peuvent être trouvés sur le référentiel MLOps Github._ - -- [Dépôt Github MLOps](https://github.com/StatCan/ETAA-kubeflow-mlops) - -Ce référentiel contient une gamme d'exemples et de didacticiels pour l'utilisation de la plate-forme ETAA dans divers flux de travail d'apprentissage automatique, y compris la préparation des données, la formation de modèles et le déploiement de modèles. - -## Conclusion - -En s'intégrant à des plates-formes d'apprentissage automatique populaires telles que Databricks et AzureML, la plate-forme ETAA fournit une solution puissante et flexible pour créer, déployer et gérer des workflows d'apprentissage automatique à grande échelle. - -En tirant parti des intégrations et des outils fournis par ces plates-formes, les scientifiques des données et les ingénieurs en apprentissage automatique peuvent accélérer leurs flux de travail et obtenir de meilleurs résultats avec moins d'effort. diff --git a/docs/fr/3-Pipelines/PaaS.md b/docs/fr/3-Pipelines/PaaS.md deleted file mode 100644 index f3b33a781..000000000 --- a/docs/fr/3-Pipelines/PaaS.md +++ /dev/null @@ -1,12 +0,0 @@ -# Intégrer avec des plateformes comme Databricks et AzureML - -La plateforme AAW est construite autour de l'idée d'intégrations, et nous -pouvons donc s'intégrer avec de nombreuses offres de "plateforme en tant que -service" (PaaS), telles que -[Azure ML](https://azure.microsoft.com/en-us/services/machine-learning/) et -[Databricks](https://azure.microsoft.com/en-ca/services/databricks/). - -Voir quelques exemples dans notre -[Dépôt Github "MLOps"](https://github.com/StatCan/aaw-kubeflow-mlops). - -![PaaS](../images/PaaS.png) diff --git a/docs/fr/3-Pipelines/Serving.md b/docs/fr/3-Pipelines/Serving.md deleted file mode 100644 index 02f5b7d94..000000000 --- a/docs/fr/3-Pipelines/Serving.md +++ /dev/null @@ -1,18 +0,0 @@ -# Service de modèle avec Seldon Core et KFServing - - -!!! warning "⚒ Cette page est en construction ⚒" - La personne qui écrit cette entrée n'en sait pas assez sur cette fonctionnalité pour écrire à son sujet, mais vous pouvez demander sur notre canal en Slack. - -## Sans-serveur avec KNative - -Kubernetes et [KNative] (https://knative.dev/) permettent à vos services de -monter ou descendre en puissance à la demande. Cela vous permet de créer des API -pour servir des modèles d'apprentissage automatique, sans avoir besoin de gérer -l'équilibrage de charge ou la montée en puissance. La plateforme peut gérer -toute votre mise à l'échelle pour vous, afin que vous puissiez vous concentrer -sur la logique du programme. - - -!!! warning "⚒ Cette page est en construction ⚒" - La personne qui rédige cette entrée ne connaît pas suffisamment cette fonctionnalité pour écrire à son sujet, mais vous pouvez demander sur notre canal en Slack. diff --git a/docs/fr/3-Pipelines/sklearn_iris_jsondata.ipynb b/docs/fr/3-Pipelines/sklearn_iris_jsondata.ipynb deleted file mode 100644 index c9256f0e0..000000000 --- a/docs/fr/3-Pipelines/sklearn_iris_jsondata.ipynb +++ /dev/null @@ -1,524 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Servir un classificateur d'iris avec Seldon\n", - "\n", - "## Objectif du bloc-notes\n", - "\n", - "L'objectif de ce bloc-notes est d'encapsuler un modèle Python \"scikit-learn\" à utiliser comme microservice de prédiction avec Seldon Core." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configurer Python\n", - "\n", - "Écrivez requirements.txt et configurez notre environnement virtuel Python." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-27T15:57:57.958799Z", - "iopub.status.busy": "2023-04-27T15:57:57.957866Z", - "iopub.status.idle": "2023-04-27T15:57:57.964285Z", - "shell.execute_reply": "2023-04-27T15:57:57.963484Z", - "shell.execute_reply.started": "2023-04-27T15:57:57.958767Z" - }, - "scrolled": true, - "tags": [] - }, - "outputs": [], - "source": [ - "%%writefile requirements.txt\n", - "scikit-learn\n", - "dill\n", - "pandas\n", - "sklearn\n", - "seldon-core\n", - "requests\n", - "matplotlib" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-27T13:58:18.412667Z", - "iopub.status.busy": "2023-04-27T13:58:18.411840Z", - "iopub.status.idle": "2023-04-27T13:58:19.144219Z", - "shell.execute_reply": "2023-04-27T13:58:19.143136Z", - "shell.execute_reply.started": "2023-04-27T13:58:18.412630Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "!if [ ! -d \"./venv\" ]; then python -m venv venv; fi" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-27T13:58:19.302968Z", - "iopub.status.busy": "2023-04-27T13:58:19.302445Z", - "iopub.status.idle": "2023-04-27T13:58:20.355237Z", - "shell.execute_reply": "2023-04-27T13:58:20.353560Z", - "shell.execute_reply.started": "2023-04-27T13:58:19.302929Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "!source ./venv/bin/activate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-27T13:58:20.713063Z", - "iopub.status.busy": "2023-04-27T13:58:20.712216Z", - "iopub.status.idle": "2023-04-27T13:58:21.461588Z", - "shell.execute_reply": "2023-04-27T13:58:21.460429Z", - "shell.execute_reply.started": "2023-04-27T13:58:20.713016Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "!if [ ! -d \"/home/jovyan/.config/pip\" ]; then mkdir /home/jovyan/.config/pip; fi" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Seldon ne fonctionne que dans l'environnement de développement ETAA; modifiez `pip.conf` pour récupérer les packages sur internet." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-27T13:58:21.463776Z", - "iopub.status.busy": "2023-04-27T13:58:21.463360Z", - "iopub.status.idle": "2023-04-27T13:58:21.469135Z", - "shell.execute_reply": "2023-04-27T13:58:21.468420Z", - "shell.execute_reply.started": "2023-04-27T13:58:21.463740Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%%writefile /home/jovyan/.config/pip/pip.conf\n", - "[global]\n", - "index-url = https://jfrog.aaw.cloud.statcan.ca/artifactory/api/pypi/pypi-remote/simple" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-27T15:58:01.566928Z", - "iopub.status.busy": "2023-04-27T15:58:01.566106Z", - "iopub.status.idle": "2023-04-27T15:58:05.178871Z", - "shell.execute_reply": "2023-04-27T15:58:05.177536Z", - "shell.execute_reply.started": "2023-04-27T15:58:01.566896Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%%capture\n", - "%pip install -r requirements.txt" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Nous pouvons ajouter le noyau au lanceur JupyterLab pour une réutilisation facile." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-27T13:58:48.714311Z", - "iopub.status.busy": "2023-04-27T13:58:48.713884Z", - "iopub.status.idle": "2023-04-27T13:58:50.178610Z", - "shell.execute_reply": "2023-04-27T13:58:50.177586Z", - "shell.execute_reply.started": "2023-04-27T13:58:48.714273Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "!python -m ipykernel install --user --name=sklearn_iris_jsondata" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Déployer le modèle\n", - "\n", - "Écrivez un fichier YAML SeldonDeployment pour le modèle conteneurisé." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-27T14:48:15.600923Z", - "iopub.status.busy": "2023-04-27T14:48:15.600036Z", - "iopub.status.idle": "2023-04-27T14:48:15.606528Z", - "shell.execute_reply": "2023-04-27T14:48:15.605436Z", - "shell.execute_reply.started": "2023-04-27T14:48:15.600890Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "%%writefile sklearn_iris_jsondata_deployment.yaml\n", - "apiVersion: machinelearning.seldon.io/v1alpha2\n", - "kind: SeldonDeployment\n", - "metadata:\n", - " name: seldon-deployment-example\n", - " namespace: bryanpaget\n", - "spec:\n", - " name: sklearn-iris-deployment\n", - " annotations:\n", - " sidecar.istio.io/inject: \"false\"\n", - " predictors:\n", - " - componentSpecs:\n", - " - spec:\n", - " containers:\n", - " - image: seldonio/sklearn-iris:0.3\n", - " imagePullPolicy: IfNotPresent\n", - " name: sklearn-iris-classifier\n", - " graph:\n", - " children: []\n", - " endpoint:\n", - " type: REST\n", - " name: sklearn-iris-classifier\n", - " type: MODEL\n", - " name: sklearn-iris-predictor\n", - " replicas: 1" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Déployez SeldonDeployment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-27T13:58:50.189468Z", - "iopub.status.busy": "2023-04-27T13:58:50.188796Z", - "iopub.status.idle": "2023-04-27T13:58:53.893779Z", - "shell.execute_reply": "2023-04-27T13:58:53.892661Z", - "shell.execute_reply.started": "2023-04-27T13:58:50.189441Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "!kubectl create -f sklearn_iris_jsondata_deployment.yaml" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-27T13:58:53.896155Z", - "iopub.status.busy": "2023-04-27T13:58:53.895313Z", - "iopub.status.idle": "2023-04-27T13:58:55.209852Z", - "shell.execute_reply": "2023-04-27T13:58:55.208787Z", - "shell.execute_reply.started": "2023-04-27T13:58:53.896117Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "!kubectl rollout status deploy \\\n", - " $(kubectl get deploy -l seldon-deployment-id=seldon-deployment-example \\\n", - " -o jsonpath='{.items[0].metadata.name}')" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Le service est-il disponible ?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-27T13:58:55.211582Z", - "iopub.status.busy": "2023-04-27T13:58:55.211279Z", - "iopub.status.idle": "2023-04-27T13:58:55.435452Z", - "shell.execute_reply": "2023-04-27T13:58:55.434211Z", - "shell.execute_reply.started": "2023-04-27T13:58:55.211552Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "for i in range(60):\n", - " state = !kubectl get sdep seldon-deployment-example -o jsonpath='{.status.state}'\n", - " state = state[0]\n", - " print(state)\n", - " if state == \"Available\":\n", - " break\n", - " time.sleep(1)\n", - "assert state == \"Available\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modèle de test\n", - "\n", - "Avec l'environnement virtuel configuré et activé, importez les bibliothèques Python nécessaires." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-27T15:58:43.902715Z", - "iopub.status.busy": "2023-04-27T15:58:43.901670Z", - "iopub.status.idle": "2023-04-27T15:58:43.906588Z", - "shell.execute_reply": "2023-04-27T15:58:43.905572Z", - "shell.execute_reply.started": "2023-04-27T15:58:43.902678Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import json\n", - "import pandas as pd" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Le classificateur d'iris prend un ensemble de quatre mesures, dans ce cas `[5.964, 1.006, 2.081, 1.031]`, et il renvoie un ensemble de probabilités, une pour chacun des trois types de fleurs d'iris. [Vous pouvez en savoir plus sur les données d'iris ici.](https://archive.ics.uci.edu/ml/datasets/iris) Envoyons quelques données au modèle." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-27T16:05:03.253143Z", - "iopub.status.busy": "2023-04-27T16:05:03.252208Z", - "iopub.status.idle": "2023-04-27T16:05:03.304174Z", - "shell.execute_reply": "2023-04-27T16:05:03.303066Z", - "shell.execute_reply.started": "2023-04-27T16:05:03.253106Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "res = !curl -v seldon-deployment-example-sklearn-iris-predictor.bryanpaget.svc.cluster.local:8000/api/v0.1/predictions \\\n", - " -H 'Content-Type: application/json' \\\n", - " -d '{\"data\": {\"ndarray\": [[5.964, 1.006, 2.081, 1.031]]}}'" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Résultats\n", - "\n", - "Ce sont les résultats en JSON brut." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-27T16:05:03.594132Z", - "iopub.status.busy": "2023-04-27T16:05:03.593539Z", - "iopub.status.idle": "2023-04-27T16:05:03.600395Z", - "shell.execute_reply": "2023-04-27T16:05:03.599596Z", - "shell.execute_reply.started": "2023-04-27T16:05:03.594090Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "parsed = json.loads(res[-1])\n", - "print(json.dumps(parsed, indent=4, sort_keys=True))" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Cette fonction de traçage révèle un graphique à barres avec l'inférence du classificateur." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-27T16:05:04.188096Z", - "iopub.status.busy": "2023-04-27T16:05:04.187614Z", - "iopub.status.idle": "2023-04-27T16:05:04.541870Z", - "shell.execute_reply": "2023-04-27T16:05:04.540936Z", - "shell.execute_reply.started": "2023-04-27T16:05:04.188045Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def display_iris_classifier_results(res):\n", - " \"\"\" Prend les résultats de classification du classificateur d'iris et les trace dans un graphique à barres pour une visualisation facile des probabilités de classe.\n", - "\n", - " Arguments :\n", - " res (string) : résultats du modèle d'iris servi par Seldon.\n", - " \"\"\"\n", - " results = res[-1]\n", - " results = json.loads(results)\n", - " names = results[\"data\"][\"names\"]\n", - " predictions = results[\"data\"][\"ndarray\"][0]\n", - " model = results[\"meta\"][\"requestPath\"]\n", - " model = model.popitem()\n", - " model, version = model[0], model[1]\n", - "\n", - " df = pd.DataFrame({\"Class\": names, \"Probability\": predictions})\n", - "\n", - " df.plot(\n", - " kind=\"bar\",\n", - " title=model.replace(\"-\", \" \").title(),\n", - " xlabel=\"Classification\",\n", - " ylabel=\"Probability\",\n", - " legend=False\n", - " )\n", - "\n", - "display_iris_classifier_results(res)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Nettoyer\n", - "\n", - "Nettoyez en supprimant le déploiement." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-04-27T16:05:04.750111Z", - "iopub.status.busy": "2023-04-27T16:05:04.749559Z", - "iopub.status.idle": "2023-04-27T16:05:07.723921Z", - "shell.execute_reply": "2023-04-27T16:05:07.722248Z", - "shell.execute_reply.started": "2023-04-27T16:05:04.750073Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "!kubectl delete -f sklearn_iris_jsondata_deployment.yaml" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", 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zZg*QS6j6IzR4LVKe=lgS&}^q5dzJetx)jDL8cN+|90g_1LCaM(#$b`!N0oO{Jo$sy zD0{mDJrz<=0uf_a8nY3Un9#Y`X`1F;Ps`wa;l}s@Lyr0@4%W%lW-JK!( zp-{%f{-ZzZ_O>d{QBYx_5&n$$LmjJQ3uaE#wOCnoZ+jKIJ?`Y)7dxRHPx4f`aBj3S z{0^otmpKL}HgnT5%S0`e^R|7aFLq4Lrk@Hl$-4vD$E6Ah@RYk$A7@E{qjpL|i`Xnk z9QvHjIR~8}0Vc?LP1X>$tiZ9*{S|WPWxR)jarhIWh!p*AX!)2jifYRcoCBA`A&;%{ z98+5=u+9;VlhA?1#2`ZrlJ%UG zG$l>s-9wvAy6gd4OQy^B4;oe*w+~XIN&+^XGb#}Q=_e4di9*NJdHU41 Date: Tue, 31 Oct 2023 00:08:46 +0000 Subject: [PATCH 05/15] Pipelines and Collaboration --- .../Geospatial-Analytical-Environment.md | 2 +- docs/en/4-Collaboration/Overview.md | 30 ++----- .../en/4-Collaboration/Request-a-Namespace.md | 25 ++---- "docs/fr/4-Collaboration/Aper\303\247u.md" | 90 +++++-------------- .../4-Collaboration/Demander-EspaceDeNom.md | 26 ++---- .../Environnement-Analyse-Geospatiale.md | 21 +++-- 6 files changed, 56 insertions(+), 138 deletions(-) diff --git a/docs/en/4-Collaboration/Geospatial-Analytical-Environment.md b/docs/en/4-Collaboration/Geospatial-Analytical-Environment.md index 199e402fc..837d596f6 100644 --- a/docs/en/4-Collaboration/Geospatial-Analytical-Environment.md +++ b/docs/en/4-Collaboration/Geospatial-Analytical-Environment.md @@ -13,7 +13,7 @@ The ArcGIS Enterprise Portal can be accessed in either the AAW or CAE using the API, from any service which leverages the Python programming language. -For example, in AAW and the use of [Jupyter Notebooks](https://statcan.github.io/aaw/en/1-Experiments/Jupyter/) within the space, or in CAE the use of [Databricks](https://statcan.github.io/cae-eac/en/DataBricks/), DataFactory, etc. +For example, in AAW and the use of [Jupyter Notebooks](../../1-Experiments/Jupyter/) within the space, or in CAE the use of [Databricks](https://statcan.github.io/cae-eac/en/DataBricks/), DataFactory, etc. [The DAS GAE ArcGIS Enterprise Portal can be accessed directly here](https://geoanalytics.cloud.statcan.ca/portal) diff --git a/docs/en/4-Collaboration/Overview.md b/docs/en/4-Collaboration/Overview.md index f06633aba..b484ee3c6 100644 --- a/docs/en/4-Collaboration/Overview.md +++ b/docs/en/4-Collaboration/Overview.md @@ -16,22 +16,17 @@ Sharing code, disks, and workspaces (e.g.: two people sharing the same virtual m ??? question "What is the difference between a bucket and a folder?" Buckets are like Network Storage. See the [Storage overview](../5-Storage/Overview.md) for more discussion of the differences between these two ideas. -Choosing the best way to share code, data, and compute all involve different factors, but you can generally mix and match (share code with your team through -Github, but store your data privately in a personal bucket). These cases are described more in the below sections. +Choosing the best way to share code, data, and compute all involve different factors, but you can generally mix and match (share code with your team through Github, but store your data privately in a personal bucket). These cases are described more in the below sections. ## Share code among team members -In most cases, it is easiest to share code using GitHub or GitLab to share code. -The advantage of sharing with GitHub or GitLab is that it works with users -across namespaces, and keeping code in git is a great way to manage large -software projects. +In most cases, it is easiest to share code using GitHub or GitLab to share code. The advantage of sharing with GitHub or GitLab is that it works with users across namespaces, and keeping code in git is a great way to manage large software projects. ??? note "Don't forget to include a License!" If your code is public, do not forget to keep with the Innovation Team's guidelines and use a proper License if your work is done for Statistics Canada. -If you need to share code without publishing it on a repository, -[sharing a namespace](#share-compute-namespace-in-kubeflow) might work as well. +If you need to share code without publishing it on a repository, [sharing a namespace](#share-compute-namespace-in-kubeflow) might work as well. ## Share compute (namespace) in Kubeflow @@ -39,16 +34,9 @@ If you need to share code without publishing it on a repository, !!! danger "Sharing a namespace means you share **everything** in the namespace" Kubeflow does not support granular sharing of one resource (one notebook, one volume, etc.), but instead sharing of **all** resources. If you want to share a Jupyter Notebook server with someone, you must share your entire namespace and **they will have access to all other resources (Azure Blob Storage, etc.)**. -In Kubeflow every user has a **namespace** that contains their work (their -notebook servers, pipelines, disks, etc.). Your namespace belongs to you, but -can be shared if you want to collaborate with others. You can also -[request a new namespace](Request-a-Namespace.md) (either for yourself or to -share with a team). One option for collaboration is to share namespaces with -others. +In Kubeflow every user has a **namespace** that contains their work (their notebook servers, pipelines, disks, etc.). Your namespace belongs to you, but can be shared if you want to collaborate with others. You can also [request a new namespace](Request-a-Namespace.md) (either for yourself or to share with a team). One option for collaboration is to share namespaces with others. -The advantage of sharing a Kubeflow namespace is that it lets you and your -colleagues share the compute environment and volumes associated with the -namespace. This makes it a very easy and free-form way to share. +The advantage of sharing a Kubeflow namespace is that it lets you and your colleagues share the compute environment and volumes associated with the namespace. This makes it a very easy and free-form way to share. To share your namespace, see [managing contributors](#managing-contributors) @@ -70,15 +58,11 @@ To learn more about the technology behind these, check out the ### Sharing with the world -Ask about that one in our [Slack channel](https://statcan-aaw.slack.com). There -are many ways to do this from the IT side, but it's important for it to go -through proper processes, so this is not done in a "self-serve" way that the -others are. That said, it is totally possible. +Ask about that one in our [Slack channel](https://statcan-aaw.slack.com). There are many ways to do this from the IT side, but it's important for it to go through proper processes, so this is not done in a "self-serve" way that the others are. That said, it is totally possible. ## Managing contributors -You can add or remove people from a namespace you already own through the -**Manage Contributors** menu in Kubeflow. +You can add or remove people from a namespace you already own through the **Manage Contributors** menu in Kubeflow. ![ Manage Contributors Menu](../images/kubeflow_contributors.png) diff --git a/docs/en/4-Collaboration/Request-a-Namespace.md b/docs/en/4-Collaboration/Request-a-Namespace.md index 3f654ca14..cb1da258b 100644 --- a/docs/en/4-Collaboration/Request-a-Namespace.md +++ b/docs/en/4-Collaboration/Request-a-Namespace.md @@ -1,36 +1,23 @@ # Overview -By default, everyone gets their own personal namespace, `firstname-lastname`. If -you want to collaborate with your team, you can -[request a new namespace](Request-a-Namespace.md#requesting-a-namespace) to -[share](Overview.md#share-a-namespace-in-kubeflow). +By default, everyone gets their own personal namespace, `firstname-lastname`. If you want to collaborate with your team, you can [request a new namespace](Request-a-Namespace.md#requesting-a-namespace) to [share](Overview.md#share-a-namespace-in-kubeflow). # Setup ## Requesting a namespace -To create a namespace for a team, go to the AAW portal. **Click the ⋮ menu -on -[the Kubeflow section of the portal](https://analytics-platform.statcan.gc.ca/)**. +To create a namespace for a team, go to the AAW portal. **Click the ⋮ menu on [the Kubeflow section of the portal](https://www.statcan.gc.ca/data-analytics-services/aaw)**. ![The hamburger menu to the right of Kubeflow on the portal](../images/KubeflowNamespace.PNG) -Enter the name you are requesting and submit the request. Be sure to use only -lower case letters plus dashes. -![Submit your request for a shared namespace](../images/KubeflowNamespace2.png) +Enter the name you are requesting and submit the request. Be sure to use only lower case letters plus dashes. ![Submit your request for a shared namespace](../images/KubeflowNamespace2.png) !!! warning "The namespace cannot have special characters other than hyphens" - The namespace name must only be lower-case letters `a-z` with dashes. Otherwise, - the namespace will not be created. + The namespace name must only be lower-case letters `a-z` with dashes. Otherwise, the namespace will not be created. -**You will receive an email notification when the namespace is created.** Once -the shared namespace is created, you can access it the same as any other -namespace you have through the Kubeflow UI, like shown below. You will then be -able to [share and manage](Overview.md#share-compute-namespace-in-kubeflow) to -your namespace. +**You will receive an email notification when the namespace is created.** Once the shared namespace is created, you can access it the same as any other namespace you have through the Kubeflow UI, like shown below. You will then be able to [share and manage](Overview.md#share-compute-namespace-in-kubeflow) to your namespace. -To switch namespaces, take a look at the top of your window, just to the right -of the Kubeflow Logo. +To switch namespaces, take a look at the top of your window, just to the right of the Kubeflow Logo. ![Select your Namespace](../images/kubeflow_manage_contributors.png) diff --git "a/docs/fr/4-Collaboration/Aper\303\247u.md" "b/docs/fr/4-Collaboration/Aper\303\247u.md" index 8eff89de9..f163145d2 100644 --- "a/docs/fr/4-Collaboration/Aper\303\247u.md" +++ "b/docs/fr/4-Collaboration/Aper\303\247u.md" @@ -1,10 +1,6 @@ -Il existe de nombreuses façons de collaborer sur la plateforme. Selon votre -situation,ce qu vous voulez partager et le nombre de personnes que vous -souhaitez partager avec. Les scénarios se décomposent en gros en ce que vous -voulez partager (**Données**, **Code**, ou **Environnements de calcul** (e.g.: -Partager les mêmes machines virtuelles)) et avec qui vous voulez le partager -(**Personne**, **Mon équipe**, ou **Tout le monde**). Cela conduit au tableau -d'options suivant: +La collaboration est essentielle en science des données car elle permet à des individus ayant des perspectives et des parcours différents de travailler ensemble pour résoudre des problèmes complexes et générer de nouvelles informations. En science des données, la collaboration peut impliquer de travailler avec des personnes issues de divers domaines tels que les mathématiques, l’informatique et les affaires, ainsi qu’avec des experts en la matière possédant une connaissance approfondie d’une industrie ou d’un domaine particulier. + +Il existe de nombreuses façons de collaborer sur la plateforme. Selon votre situation,ce qu vous voulez partager et le nombre de personnes que vous souhaitez partager avec. Les scénarios se décomposent en gros en ce que vous voulez partager (**Données**, **Code**, ou **Environnements de calcul** (e.g.: Partager les mêmes machines virtuelles)) et avec qui vous voulez le partager (**Personne**, **Mon équipe**, ou **Tout le monde**). Cela conduit au tableau d'options suivant: | | **Privée** | **Équipe** | **StatCan** | | :---------: | :-----------------------------------: | :----------------------------------------------------------: | :------------------: | @@ -12,50 +8,35 @@ d'options suivant: | **Données** | Dossier personnel ou compartiment | Dossier d'équipe ou compartiment , ou espace de noms partagé | Compartiment partagé | | **Calcul** | Espace de nom personnel | Espace de noms partagé | N/A | +Le partage de code, de disques et d'espaces de travail (par exemple : deux personnes partageant la même machine virtuelle) est décrit plus en détail ci-dessous. Le partage de données via des compartiments est décrit plus en détail dans la section **[Stockage Blob Azure](../5-Stockage/StockageBlobAzure.md)**. + ??? question "Quelle est la différence entre un compartiment et un dossier?" - Les compartiments sont comme le stockage sur réseau. Consulter [ présentation du stockage](../Stockage.md) pour plus de détails sur les différences entre ces deux options. + Les compartiments sont comme le stockage sur réseau. Consulter [ présentation du stockage](../5-Stockage/Aperçu.md) pour plus de détails sur les différences entre ces deux options. -Choisir la meilleure façon de partager le code, les données et le calcul -implique des facteurs, mais vous pouvez généralement mélanger et assortir -(partager le code avec votre équipe via github, mais stockez vos données en -privé dans un compartiment personnel). Ces cas sont décrits plus en détail dans -les sections ci-dessous +Choisir la meilleure façon de partager le code, les données et le calcul implique un ensemble de différent facteurs, mais vous pouvez généralement mélanger et assortir (partager le code avec votre équipe via github, mais stockez vos données en privé dans un compartiment personnel). Ces cas sont décrits plus en détail dans les sections ci-dessous ## Partager le code entre les membres de l'équipe -Dans la plupart des cas, il est plus facile de partager du code en utilisant -GitHub ou GitLab. L'avantage du partage avec GitHub ou GitLab est que cela -fonctionne avec les utilisateurs à travers les espaces de noms, et conserver le -code dans github est un excellent moyen de gérer de grands projets logiciels. +Dans la plupart des cas, il est plus facile de partager du code en utilisant GitHub ou GitLab. L'avantage du partage avec GitHub ou GitLab est que cela fonctionne avec les utilisateurs à travers les espaces de noms, et conserver le code dans git est un excellent moyen de gérer de grands projets logiciels. ??? note "N'oubliez pas d'inclure une licence !" Si votre code est public, n'oubliez pas de respecter les directives de l'équipe d'innovation et d'utiliser une licence appropriée si votre travail est effectué pour Statistique Canada. -Si vous devez partager du code sans le publier sur un -référentiel,[ partager un espace de nom](#share-compute-namespace-in-kubeflow)) -pourrait aussi fonctionner. +Si vous devez partager du code sans le publier sur un référentiel,[ partager un espace de nom](#partager-le-calcul-espace-de-nom-dans-kubeflow)) pourrait aussi fonctionner. ## Partager le calcul (espace de nom) dans Kubeflow -!!! danger "Partager un espace de nom signifie que vous partagez **toutes les choses** - dans l'espace de nom". - Kubeflow ne prend pas en charge le partage granulaire d'une ressource (un bloc-notes, un compartiment MinIO, etc.), mais plutôt le partage de **tous** ressources. Si vous souhaitez partager un serveur Jupyter Carnet note avec quelqu'un, vous devez partager l'intégralité de votre espace de nom et **ils auront accès à toutes les autres ressources (compartiment MinIO, etc.)**. - -Dans Kubeflow, chaque utilisateur dispose d'un **espace de nom** qui contient -son travail (son serveur de Carnets notes, pipelines, disques, etc.). Votre -espace de nom vous appartient, mais peut être partagé si vous voulez collaborer -avec d'autres. Vous pouvez aussi -[ Demander un Espace de nom](Demander-EspaceDeNom.md) (soit pour vous-même, soit -pour partager avec une équipe). Une option de collaboration consiste à partager -des espaces de noms avec les autres. - -L'avantage de partager un espace de nom Kubeflow est qu'il vous permet, ainsi -qu'à vos collègues partagent l'environnement de calcul et les compartiments -MinIO associés au espace de nom. Cela en fait un moyen très simple et libre de -partager. +!!! danger "Partager un espace de nom signifie que vous partagez **toutes les choses** dans l'espace de nom" + Kubeflow ne prend pas en charge le partage granulaire d'une ressource (un bloc-notes, un volume, etc.), mais plutôt le partage de **toutes** ressources. Si vous souhaitez partager un serveur bloc-notes Jupyter avec quelqu'un, vous devez partager l'intégralité de votre espace de nom et **ils auront accès à toutes les autres ressources (Storage Blob Azure, etc.)**. + +Dans Kubeflow, chaque utilisateur dispose d'un **espace de noms** qui contient son travail (ses serveurs bloc-notes, pipelines, disques, etc.). Votre espace de noms vous appartient, mais peut être partagé si vous souhaitez collaborer avec d'autres. Vous pouvez également [demander un nouvel espace de noms](Demander-EspaceDeNom.md) (soit pour vous-même, soit pour le partager avec une équipe). Une option de collaboration consiste à partager des espaces de noms avec d’autres. + +L'avantage du partage d'un espace de noms Kubeflow est qu'il vous permet, à vous et à vos collègues, de partager l'environnement de calcul et les volumes associés à l'espace de noms. Cela en fait un moyen de partage très simple et libre. + +Pour partager votre espace de noms, consultez [gestion des contributeurs](#gestion-des-contributeurs) ??? conseil "Demander de l'aide en production" @@ -63,48 +44,23 @@ partager. ## Partager des données -Une fois que vous avez un espace de nom partagé, vous avez deux approches de -stockage partagé +Une fois que vous avez un espace de nom partagé, vous avez deux approches de stockage partagé | Possibilité de stockage | Avantages | | :------------------------------------------------------------ | :--------------------------------------------------------------------------------------------- | | Serveurs/espaces de travail Jupyter partagés | Plus adapté aux petits fichiers, aux cahiers et aux petites expériences. | -| Compartiments partagés( consultez [Stockage](../Stockage.md)) | Mieux adapté pour une utilisation dans les pipelines, les API et pour les fichiers volumineux. | +| Compartiments partagés( consultez [Stockage](../5-Stockage/Aperçu.md)) | Mieux adapté pour une utilisation dans les pipelines, les API et pour les fichiers volumineux. | Pour en savoir plus sur la technologie qui les sous-tend, consultez le -[Stockage](../Stockage.md). - -### Partager avec StatCan - -En plus des compartiments privés ou des compartiments privés partagés par -l'équipe, vous pouvez également placez vos fichiers dans le _stockage partagé_. -Dans toutes les options de stockage de compartiment (`minimal`, `premium`, -`pachyderm`), vous disposez d'un compartiment privé, **et** d'un dossier à -l'intérieur du compartiment « partagé ». Jetez un œil, par exemple, au lien -ci-dessous : - -- [`shared/blair-drummond/`](https://minimal-tenant1-minio.covid.cloud.statcan.ca/minio/shared/blair-drummond/) - -Tout utilisateur **connecté** peut voir ces fichiers et les lire librement. - -### Partage avec le monde - -Renseignez-vous sur celui-ci dans notre -[chaîne Slack](https://statcan-aaw.slack.com). Là il existe de nombreuses façons -de le faire du côté informatique, mais il est important que cela aille par des -processus appropriés, de sorte que cela ne se fait pas de manière « -libre-service » que d'autres sont. Cela dit, c'est tout à fait possible. +[Stockage](../5-Stockage/Aperçu.md). -## Recommandation : Combinez-les tous +### Partager avec le monde -C'est une excellente idée de toujours utiliser github avec des espaces de -travail partagés est un excellent moyen de combiner le partage ad hoc (via des -fichiers) tout en gardant votre code organisé et suivi +Renseignez-vous à ce sujet sur notre [chaîne Slack](https://statcan-aaw.slack.com). Il existe de nombreuses façons de procéder du côté informatique, mais il est important que cela passe par des processus appropriés, afin que cela ne se fasse pas de manière « libre-service » comme le sont les autres. Cela dit, c'est tout à fait possible. ## Gestion des contributeurs -Vous pouvez ajouter ou supprimer des personnes d'un espace de nom que vous -possédez déjà via le Menu **Gérer les contributeurs** dans Kubeflow. +Vous pouvez ajouter ou supprimer des personnes d'un espace de noms que vous possédez déjà via le menu **Gérer les contributeurs** dans Kubeflow. ![Contributors Menu](../images/kubeflow_contributors.png) diff --git a/docs/fr/4-Collaboration/Demander-EspaceDeNom.md b/docs/fr/4-Collaboration/Demander-EspaceDeNom.md index 64e585c5e..24b871a93 100644 --- a/docs/fr/4-Collaboration/Demander-EspaceDeNom.md +++ b/docs/fr/4-Collaboration/Demander-EspaceDeNom.md @@ -1,37 +1,23 @@ # Aperçu -Par défaut, tout le monde obtient son propre espace de nom personnel, -`prénom-nom`. Si vous souhaitez collaborer avec votre équipe, vous pouvez -[demander un Espace de nom](./Demander-EspaceDeNom.md) à -[partager](Aperçu.md#share-a-namespace-in-kubeflow). +Par défaut, chaque personne a son espace de nom personnel, `prénom-nom`. Si vous souhaitez collaborer avec votre équipe, vous pouvez [demander un Espace de nom](./Demander-EspaceDeNom.md) à [partager](Aperçu.md#partager-le-calcul-espace-de-nom-dans-kubeflow). # Installation ## Demander un espace de nom -Pour créer un espace de noms pour une équipe, accédez au portail ETAA. **Cliquez -sur le ⋮ menu sur -[la section Kubeflow du portail](https://analytics-platform.statcan.gc.ca/)**. +Pour créer un espace de noms pour une équipe, accédez au portail ETAA. **Cliquez sur le ⋮ menu sur [la section Kubeflow du portail](https://www.statcan.gc.ca/services-analyse-donnees/etaa)**. ![Le menu hamburger à droite de Kubeflow sur le portail](../images/KubeflowNamespace.PNG) -Entrez le nom que vous demandez et soumettez la demande. Veillez à n'utiliser -que lettres minuscules plus tirets. -![Soumettez votre demande d'espace de noms partagé](../images/KubeflowNamespace2.png) +Entrez le nom que vous demandez et soumettez la demande. Veillez à n'utiliser que lettres minuscules plus tirets. ![Soumettez votre demande d'espace de noms partagé](../images/KubeflowNamespace2.png) -!!! avertissement "L'espace de noms ne peut pas avoir de caractères spéciaux autres que - des traits d'union" +!!! avertissement "L'espace de noms ne peut pas avoir de caractères spéciaux autres que des traits d'union" Le nom de l'espace de noms ne doit être composé que de lettres minuscules « a-z » avec des tirets. Sinon,l'espace de noms ne sera pas créé. -**Vous recevrez une notification par e-mail lorsque l'espace de noms sera -créé.** Une fois l'espace de noms partagé est créé, vous pouvez y accéder comme -n'importe quel autre l'espace de noms que vous avez via l'interface utilisateur -de Kubeflow, comme illustré ci-dessous. Vous serez alors capable de -[partager et gérer](Aperçu.md#share-a-namespace-in-kubeflow) à votre espace de -nom. +**Vous recevrez une notification par e-mail lorsque l'espace de noms sera créé.** Une fois l'espace de noms partagé créé, vous pouvez y accéder de la même manière que n'importe quel autre espace de noms dont vous disposez via l'interface utilisateur de Kubeflow, comme illustré ci-dessous. Vous serez alors capable de [partager et gérer](Aperçu.md#partager-le-calcul-espace-de-nom-dans-kubeflow)) à votre espace de nom. -Pour changer d'espace de noms, jetez un œil en haut de votre fenêtre, juste à -droite du logo Kubeflow. +Pour changer d'espace de noms, jetez un œil en haut de votre fenêtre, juste à droite du logo Kubeflow. ![Select your Namespace](../images/kubeflow_manage_contributors.png) diff --git a/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md b/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md index 9626416ac..e32f1338a 100644 --- a/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md +++ b/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md @@ -14,7 +14,7 @@ Le portail ArcGIS Enterprise est accessible dans ETAA ou EAC à l'aide de l'API, à partir de n'importe quel service qui exploite le langage de programmation Python. -Par exemple, dans ETAA et l'utilisation de [Jupyter Notebooks](https://statcan.github.io/aaw/en/1-Experiments/Jupyter/) dans l'espace, ou pour EAC l'utilisation de [Databricks](https://statcan.github.io/cae-eac/en/DataBricks/), DataFactory, etc. +Par exemple, dans ETAA et l'utilisation de [Bloc-notes Jupyter](../../1-Experiences/Jupyter/) dans l'espace, ou pour EAC l'utilisation de [Databricks](https://statcan.github.io/cae-eac/en/DataBricks/), DataFactory, etc. [Le portail DAS GAE ArcGIS Enterprise est accessible directement ici](https://geoanalytics.cloud.statcan.ca/portal) @@ -52,7 +52,7 @@ Par exemple, dans ETAA et l'utilisation de [Jupyter Notebooks](https://statcan.g print("Connexion réussie sous le nom de: " + gis.properties.user.username) ``` -4. - La sortie vous redirigera vers un portail de connexion. +4. La sortie vous redirigera vers un portail de connexion. - Utilisez l'option de connexion Azure de StatCan et votre identifiant Cloud - Après une connexion réussie, vous recevrez un code pour vous connecter en utilisant SAML. - Collez ce code dans la sortie. @@ -62,7 +62,7 @@ Par exemple, dans ETAA et l'utilisation de [Jupyter Notebooks](https://statcan.g


### Afficher les informations utilisateur -En utilisant la fonction "me", nous pouvons afficher diverses informations sur l'utilisateur connecté. +En utilisant la fonction « me », nous pouvons afficher diverses informations sur l'utilisateur connecté. ```python me = gis.users.me username = me.username @@ -73,7 +73,7 @@ display(me)
### Rechercher du contenu -Recherchez le contenu que vous avez hébergé sur le portail SAD EAG. En utilisant la fonction "me", nous pouvons rechercher tout le contenu hébergé sur le compte. Il existe plusieurs façons de rechercher du contenu. Deux méthodes différentes sont décrites ci-dessous. +Recherchez le contenu que vous avez hébergé sur le portail SAD EAG. En utilisant la fonction « me », nous pouvons rechercher tout le contenu hébergé sur le compte. Il existe plusieurs façons de rechercher du contenu. Deux méthodes différentes sont décrites ci-dessous. **Recherchez tous vos éléments hébergés dans le portail géographique SAD.** ```python @@ -106,6 +106,7 @@ flood_item ### Obtenir du contenu Nous devons obtenir l'élément du portail géographique SAD afin de l'utiliser dans le bloc-notes Jupyter. Cela se fait en fournissant le numéro d'identification unique de l'article que vous souhaitez utiliser. Trois exemples sont décrits ci-dessous, tous accédant à la même couche. + ```python item1 = gis.content.get(my_content[5].id) #de la recherche de votre contenu ci-dessus display(item1) @@ -120,10 +121,11 @@ display(item3)
### Effectuer une analyse + Une fois les couches importées dans le bloc-note Jupyter, nous sommes en mesure d'effectuer des types d'analyse similaires à ceux que vous vous attendriez à trouver dans un logiciel SIG tel qu'ArcGIS. Il existe de nombreux modules contenant de nombreux sous-modules qui peuvent effectuer plusieurs types d'analyses.
-À l'aide du module arcgis.features, importez le sous-module use_proximity ```from arcgis.features import use_proximity```. Ce sous-module nous permet de `.create_buffers` - des zones à égale distance des entités. Ici, nous spécifions la couche que nous voulons utiliser, la distance, les unités et le nom de sortie (vous pouvez également spécifier d'autres caractéristiques telles que le champ, le type d'anneau, le type de fin et autres). En spécifiant un nom de sortie, après avoir exécuté la commande buffer, une nouvelle couche sera automatiquement téléchargée dans le portail SAD EAG contenant la nouvelle fonctionnalité que vous venez de créer. +À l'aide du module `arcgis.features`, importez le sous-module use_proximity ```from arcgis.features import use_proximity```. Ce sous-module nous permet de `.create_buffers` - des zones à égale distance des entités. Ici, nous spécifions la couche que nous voulons utiliser, la distance, les unités et le nom de sortie (vous pouvez également spécifier d'autres caractéristiques telles que le champ, le type d'anneau, le type de fin et autres). En spécifiant un nom de sortie, après avoir exécuté la commande buffer, une nouvelle couche sera automatiquement téléchargée dans le portail SAD EAG contenant la nouvelle fonctionnalité que vous venez de créer.
```python @@ -165,10 +167,13 @@ plt.show() ``` **Exemple : librairie ipyleaflet** -Dans cet exemple, nous utiliserons la librairie 'ipyleaflet' pour créer une carte interactive. Cette carte sera centrée autour de Toronto, ON. Les données utilisées seront décrites ci-dessous. +Dans cet exemple, nous utiliserons la librairie `ipyleaflet` pour créer une carte interactive. Cette carte sera centrée autour de Toronto, ON. Les données utilisées seront décrites ci-dessous. Commencez par coller ```conda install -c conda-forge ipyleaflet``` vous permettant d'installer les librairies ipyleaflet dans l'environnement Python. +
+ Importer les bibliothèques nécessaires. + ```python import ipyleaflet from ipyleaflet import * @@ -183,8 +188,8 @@ toronto_map ```
-##En savoir plus sur l'API ArcGIS pour Python +## En savoir plus sur l'API ArcGIS pour Python [La documentation complète de l'API ArcGIS peut être trouvée ici](https://developers.arcgis.com/python/) -##En savoir plus sur l'environnement analytique géospatial (GAE) et les services SAD +##E n savoir plus sur l'environnement analytique géospatial (GAE) et les services SAD [Guide d'aide GAE](https://statcan.github.io/daaas-dads-geo/) From 93d3e361a9eb585e779a609c7f7458d5ebec86d8 Mon Sep 17 00:00:00 2001 From: Wendy V Gaultier Date: Tue, 31 Oct 2023 00:13:21 +0000 Subject: [PATCH 06/15] Gitlab --- docs/fr/6-Gitlab/Gitlab.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/docs/fr/6-Gitlab/Gitlab.md b/docs/fr/6-Gitlab/Gitlab.md index 669a0babb..bb7b8c95a 100644 --- a/docs/fr/6-Gitlab/Gitlab.md +++ b/docs/fr/6-Gitlab/Gitlab.md @@ -7,13 +7,13 @@ Heureusement, l'utilisation du cloud main GitLab sur l'ETAA se fait de la même manière que l'utilisation régulière de git. -### Etape 1 : Localisez le dépôt Git que vous voulez cloner et copiez l'option cloner avec HTTPS. +### Étape 1 : Localisez le dépôt Git que vous voulez cloner et copiez l'option cloner avec HTTPS. Si votre dépôt est privé, vous devrez également faire l'étape 4 (Création d'un jeton d'accès personnel) pour que cela fonctionne. Pour moi, il s'agissait d'un dépôt de test -![image](https://user-images.githubusercontent.com/23174198/217060353-ba229ced-b5c1-4eae-8878-9608835cc65f.png) +![Cloner avec image SSH](https://user-images.githubusercontent.com/23174198/217060353-ba229ced-b5c1-4eae-8878-9608835cc65f.png) -### Etape 2 : Coller le lien copié dans l'un des serveurs de votre espace de travail -![image](https://user-images.githubusercontent.com/23174198/217060697-535df6c1-d9bb-4bc3-a42b-9f085a5386d5.png) +### Étape 2 : Coller le lien copié dans l'un des serveurs de votre espace de travail +![Exemple de clonage Git](https://user-images.githubusercontent.com/23174198/217060697-535df6c1-d9bb-4bc3-a42b-9f085a5386d5.png) ### Étape 3 : Succès ! Comme le montre la capture d'écran ci-dessus, j'ai cloné le repo ! @@ -22,19 +22,19 @@ Comme le montre la capture d'écran ci-dessus, j'ai cloné le repo ! Si vous essayez de `git push ....` vous rencontrerez une erreur qui vous mènera à la [documentation d'aide de GitLab](https://gitlab.k8s.cloud.statcan.ca/help/user/profile/account/two_factor_authentication.md#error-http-basic-access-denied-the-provided-password-or-token-) Vous devrez créer un jeton d'accès personnel pour cela. Pour cela, allez dans GitLab, cliquez sur l'icône de votre profil, puis sur `Préférences` et ensuite sur `Tokens d'accès` -![image](https://user-images.githubusercontent.com/23174198/217061060-122dded8-dc80-46ce-a907-a85913cf5dd7.png) +![Personal Access Token](https://user-images.githubusercontent.com/23174198/217061060-122dded8-dc80-46ce-a907-a85913cf5dd7.png) Suivez les instructions en entrant le nom, la date d'expiration du token et en accordant les permissions au token (j'ai accordé `write_repository`). -### Etape 5 : Personnaliser `Git` pour qu'il soit à votre image +### Étape 5 : Personnaliser `Git` pour qu'il soit à votre image Exécutez `git config user.email ....` et `git config user.name ...` pour correspondre à votre identité GitLab. ### Étape 6 : Fournir le jeton généré lorsqu'on vous demande votre mot de passe Le jeton sera copiable en haut une fois que vous aurez cliqué sur `Create personal access token` en bas. -![image](https://user-images.githubusercontent.com/23174198/217062846-03a715f1-ded5-4d80-ad4b-c647ae5e30fd.png) +![Fournir un jeton d'accès personnel](https://user-images.githubusercontent.com/23174198/217062846-03a715f1-ded5-4d80-ad4b-c647ae5e30fd.png) Une fois que vous avez tout préparé, c'est le moment -![image](https://user-images.githubusercontent.com/23174198/217063198-c1bd6c3a-ebc5-444d-98ba-24ef32faa20e.png) +![Dernière étape](https://user-images.githubusercontent.com/23174198/217063198-c1bd6c3a-ebc5-444d-98ba-24ef32faa20e.png) -### Etape 7 : Voir les résultats de votre travail dans GitLab -![image](https://user-images.githubusercontent.com/23174198/217063990-efaa8e81-a0eb-4b6d-842e-2ca3112bb4f7.png) +### Étape 7 : Voir les résultats de votre travail dans GitLab +![Menu Gitlab](https://user-images.githubusercontent.com/23174198/217063990-efaa8e81-a0eb-4b6d-842e-2ca3112bb4f7.png) From e0d3c7483be604c48d4fc6f4a9009e97c5651a1b Mon Sep 17 00:00:00 2001 From: Wendy V Gaultier Date: Tue, 31 Oct 2023 13:53:37 +0000 Subject: [PATCH 07/15] Stockage --- docs/en/5-Storage/Disks.md | 24 ++++------------- "docs/fr/5-Stockage/Aper\303\247u.md" | 10 +++---- docs/fr/5-Stockage/Disque.md | 28 +++++--------------- docs/fr/5-Stockage/StockageBlobAzure.md | 10 +++---- docs/fr/images/container-mount.png | Bin 0 -> 118470 bytes docs/fr/images/kubeflow_existing_volume.png | Bin 49272 -> 31405 bytes docs/fr/images/unclassified-mount.png | Bin 0 -> 17642 bytes mkdocs-fr.yml | 2 +- 8 files changed, 21 insertions(+), 53 deletions(-) create mode 100644 docs/fr/images/container-mount.png create mode 100644 docs/fr/images/unclassified-mount.png diff --git a/docs/en/5-Storage/Disks.md b/docs/en/5-Storage/Disks.md index e8a061031..e267c5985 100644 --- a/docs/en/5-Storage/Disks.md +++ b/docs/en/5-Storage/Disks.md @@ -1,14 +1,10 @@ # Overview -Disks are the familiar hard drive style file systems you're used to, provided to -you from fast solid state drives (SSDs)! +Disks are the familiar hard drive style file systems you're used to, provided to you from fast solid state drives (SSDs)! # Setup -When creating your notebook server, you request disks by adding Data Volumes to -your notebook server (pictured below, with go to `Advanced Options`). They are automatically -mounted at the directory (`Mount Point`) you choose, and serve as a simple and -reliable way to preserve data attached to a Notebook Server. +When creating your notebook server, you request disks by adding Data Volumes to your notebook server (pictured below, with go to `Advanced Options`). They are automatically mounted at the directory (`Mount Point`) you choose, and serve as a simple and reliable way to preserve data attached to a Notebook Server. ![Adding an existing volume to a new notebook server](../images/kubeflow_existing_volume.png) @@ -18,17 +14,11 @@ reliable way to preserve data attached to a Notebook Server. # Once you've got the basics ... -When you delete your Notebook Server, your disks **are not deleted**. This let's -you reuse that same disk (with all its contents) on a new Notebook Server later -(as shown above with `Type = Existing` and the `Name` set to the volume you want -to reuse). If you're done with the disk and it's contents, -[delete it](#deleting-disk-storage). +When you delete your Notebook Server, your disks **are not deleted**. This let's you reuse that same disk (with all its contents) on a new Notebook Server later (as shown above with `Type = Existing` and the `Name` set to the volume you want to reuse). If you're done with the disk and it's contents, [delete it](#deleting-disk-storage). ## Deleting Disk Storage -To see your disks, check the Notebook Volumes section of the Notebook Server -page (shown below). You can delete any unattached disk (icon on the left) -by clicking the trash can icon. +To see your disks, check the Notebook Volumes section of the Notebook Server page (shown below). You can delete any unattached disk (icon on the left) by clicking the trash can icon. ![Delete an unattached volume from the Notebook Server screen](../images/kubeflow_delete_disk.png) @@ -38,11 +28,7 @@ by clicking the trash can icon. ??? info "Pricing models are tentative and may change" As of writing, pricing is covered by the platform for initial users. This guidance explains how things are expected to be priced priced in future, but this may change. -When mounting a disk, you get an -[Azure Managed Disk](https://azure.microsoft.com/en-us/pricing/details/managed-disks/). -The **Premium SSD Managed Disks** pricing shows the cost per disk based on size. -Note that you pay for the size of disk requested, not the amount of space you -are currently using. +When mounting a disk, you get an [Azure Managed Disk](https://azure.microsoft.com/en-us/pricing/details/managed-disks/). The **Premium SSD Managed Disks** pricing shows the cost per disk based on size. Note that you pay for the size of disk requested, not the amount of space you are currently using. ??? info "Tips to minimize costs" diff --git "a/docs/fr/5-Stockage/Aper\303\247u.md" "b/docs/fr/5-Stockage/Aper\303\247u.md" index 1c8852579..bab0cf9c6 100644 --- "a/docs/fr/5-Stockage/Aper\303\247u.md" +++ "b/docs/fr/5-Stockage/Aper\303\247u.md" @@ -2,18 +2,16 @@ La plateforme propose plusieurs types de stockage : -- Disque (également appelé Volumes sur l'écran de création de serveur de - blocs-note) -- Compartiment (stockage "Blob" ou S3, fourni via MinIO) +- Disque (également appelé Volumes sur l'écran de création de serveur de blocs-note) +- Compartiment (Stockage Azure Blob) - Data Lakes (à venir) -Selon votre cas d'utilisation, le disque ou le compartiment peut être le plus -approprié : +Selon votre cas d'utilisation, le disque ou le compartiment peut être le plus approprié : | Type | Utilisateurs simultanés | Vitesse | Taille totale | Partageable avec d'autres utilisateurs | | ---------------------------------------: | ---------------------------------------------------------------------------------------: | -------------------------------------------------------------------------: | -------------------------------------- | -------------------------------------- | | **[Disque](Disque.md)** | Une machine/serveur de bloc-notes à la fois | Le plus rapide (débit et latence) | <=512GB total par stockage | Non | -| **[Compartiment (via MinIO)](MinIO.md)** | Accès simultané depuis plusieurs machines/serveurs d'ordinateurs portables en même temps | Fast-ish ((Téléchargement rapide, téléchargement modeste, latence modeste) | Infini (dans la limite du raisonnable) | [Oui] | +| **[Compartiment (via Stockage Blob Azure)](StockageBlobAzure.md)** | Accès simultané depuis plusieurs machines/serveurs d'ordinateurs portables en même temps | Fast-ish (téléchargement rapide, téléchargement modeste, latence modeste) | Infini (dans la limite du raisonnable) | [Oui] | ??? info "Si vous ne savez pas lequel choisir, ne vous en faites pas" diff --git a/docs/fr/5-Stockage/Disque.md b/docs/fr/5-Stockage/Disque.md index 25c131134..51c3abd18 100644 --- a/docs/fr/5-Stockage/Disque.md +++ b/docs/fr/5-Stockage/Disque.md @@ -1,15 +1,10 @@ # Aperçu -Les disques sont les systèmes de fichiers familiers de type disque dur auxquels -vous êtes habitué, fournis à vous des disques SSD rapides ! +Les disques sont les systèmes de fichiers familiers de type disque dur auxquels vous êtes habitué, fournis à vous des disques SSD rapides ! # Installation -Lors de la création de votre serveur bloc-notes, vous demandez des disques en -ajoutant des volumes de données à votre serveur de bloc-notes (illustré -ci-dessous, avec `Type = New`). Ils sont automatiquement monté dans le -répertoire (`Mount Point`) que vous choisissez, et sert de simple et moyen -fiable de conserver les données attachées à un serveur de bloc-notes . +Lors de la création de votre serveur bloc-notes, vous demandez des disques en ajoutant des volumes de données à votre serveur de notebook (illustré ci-dessous, en allant dans `Options avancées`). Ils sont automatiquement montés dans le répertoire (`Point de montage`) que vous choisissez et constituent un moyen simple et fiable de conserver les données attachées à un serveur Notebook. ![Ajout d'un volume existant à un nouveau serveur de bloc-notes](../images/kubeflow_existing_volume.png) @@ -19,18 +14,11 @@ fiable de conserver les données attachées à un serveur de bloc-notes . # Une fois que vous avez les bases... -Lorsque vous supprimez votre Serveur blocs-note , vos disques **ne sont pas -supprimés**. Cela laisse vous réutilisez ce même disque (avec tout son contenu) -sur un nouveau Serveur blocs-note plus tard (comme indiqué ci-dessus avec « Type -= existant » et le « Nom » défini sur le volume que vous voulez réutiliser). Si -vous avez terminé avec le disque et son contenu, -[ supprimez-le](#suppression-du-stockage-sur-disque). +Lorsque vous supprimez votre serveur bloc-notes, vos disques **ne sont pas supprimés**. Cela vous permet de réutiliser ultérieurement ce même disque (avec tout son contenu) sur un nouveau serveur bloc-notes (comme indiqué ci-dessus avec `Type = Existant` et le `Nom` défini sur le volume que vous souhaitez réutiliser). Si vous en avez terminé avec le disque et son contenu, [supprimez-le](#suppression-du-stockage-sur-disque). ## Suppression du stockage sur disque -Pour voir vos disques, consultez la section Volumes blocs-note de Serveur -blocs-note page (ci-dessous). Vous pouvez supprimer n'importe quel disque non -connecté (icône orange à gauche) en cliquant sur l'icône de la corbeille. +Pour voir vos disques, consultez la section Volumes de la page Serveur bloc-notes (illustré ci-dessous). Vous pouvez supprimer n'importe quel disque non connecté (icône à gauche) en cliquant sur l'icône de la corbeille. ![Supprimer un volume non connecté de l'écran Serveur blocs-note ](../images/kubeflow_delete_disk.png) @@ -40,11 +28,7 @@ connecté (icône orange à gauche) en cliquant sur l'icône de la corbeille. ??? info "Les modèles de tarification sont provisoires et peuvent changer" Au moment de la rédaction, la tarification est couverte par la plate-forme pour les utilisateurs initiaux. Ce guide explique comment les choses devraient être tarifées à l'avenir, mais cela peut changer. -Lors du montage d'un disque, vous obtenez un -[Disque managé Azure](https://azure.microsoft.com/en-us/pricing/details/managed-disks/). -La tarification **Disques gérés SSD Premium** indique le coût par disque en -fonction de la taille. Notez que vous payez pour la taille de disque demandée, -et non pour la quantité d'espace que vous utilisent actuellement. +Lors du montage d'un disque, vous obtenez un [Disque managé Azure](https://azure.microsoft.com/en-us/pricing/details/managed-disks/). La tarification des **Disques gérés SSD Premium** indique le coût par disque en fonction de la taille. Notez que vous payez pour la taille du disque demandée, et non pour la quantité d'espace que vous utilisez actuellement. ??? info "Conseils pour minimiser les coûts" @@ -55,7 +39,7 @@ et non pour la quantité d'espace que vous utilisent actuellement. * Travaillez tout au long de la journée, en enregistrant les résultats sur le disque attaché * À 17h, éteignez votre Serveur blocs-note pour éviter de le payer du jour au lendemain * REMARQUE : Le disque attaché ** n'est pas détruit ** par cette action - * À 9 heures du matin le lendemain, créez un nouveau serveur blocs-note et ** joignez votre disque** + * À 9 heures du matin le lendemain, créez un nouveau serveur blocs-note et ** joignez votre disque** * Continuez votre travail... Cela protège tout votre travail sans payer pour l'ordinateur lorsque vous ne l'utilisez pas diff --git a/docs/fr/5-Stockage/StockageBlobAzure.md b/docs/fr/5-Stockage/StockageBlobAzure.md index d26c8b78c..22923a54e 100644 --- a/docs/fr/5-Stockage/StockageBlobAzure.md +++ b/docs/fr/5-Stockage/StockageBlobAzure.md @@ -1,8 +1,8 @@ # Aperçu -[Azure Blob Storage](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction) est la solution de stockage d'objets de Microsoft pour le cloud. Blob Storage est optimisé pour stocker des quantités massives de données non structurées. Les données non structurées sont des données qui n'adhèrent pas à un modèle de données ou à une définition particulière, comme du texte ou des données binaires. +[Azure Blob Storage](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction) est la solution de stockage d'objets de Microsoft pour le nuage informatique. Le stockage blob est optimisé pour stocker des quantités massives de données non structurées. Les données non structurées sont des données qui n'adhèrent pas à un modèle de données ou à une définition particulière, comme du texte ou des données binaires. -Les conteneurs de stockage Azure Blob sont efficaces dans trois domaines : +Les conteneurs de stockage blob Azure sont efficaces dans trois domaines : - De grandes quantités de données - Les conteneurs peuvent être énormes : bien plus gros que les disques durs. Et ils sont toujours rapides. - Accessible par plusieurs consommateurs à la fois - Vous pouvez accéder à la même source de données à partir de plusieurs serveurs Notebook et pipelines en même temps sans avoir besoin de dupliquer les données. @@ -11,14 +11,14 @@ Les conteneurs de stockage Azure Blob sont efficaces dans trois domaines : # Installation -!!! avertissement "Les conteneurs de stockage Azure Blob et le support de buckets remplaceront les supports de stockage Minio Buckets et Minio" +!!! warning "Les conteneurs de stockage Azure Blob et le support de buckets remplaceront les supports de stockage Minio Buckets et Minio" Les utilisateurs seront responsables de la migration des données des Minio Buckets vers les dossiers Azure Storage. Pour les fichiers plus volumineux, les utilisateurs peuvent contacter AAW pour obtenir de l'aide. ## Conteneur Blob monté sur un serveur de notebook -Les volumes Blob CSI sont conservés sous « /home/jovyan/buckets » lors de la création d'un serveur Notebook. Les fichiers sous « /buckets » sont sauvegardés par le stockage Blob. Tous les ordinateurs portables AAW auront le « /buckets » monté sur le système de fichiers, rendant les données accessibles de partout. +Les volumes Blob CSI sont conservés sous `/home/jovyan/buckets` lors de la création d'un serveur bloc-notes. Les fichiers sous `/buckets` sont sauvegardés par le stockage Blob. Tous les ordinateurs portables AAW auront le `/buckets` monté sur le système de fichiers, rendant les données accessibles de partout. ![Dossiers Blob montés en tant que répertoires Jupyter Notebook](../images/container-mount.png) @@ -32,7 +32,7 @@ Ces dossiers peuvent être utilisés comme n'importe quel autre : vous pouvez co ## Comment migrer de MinIO vers Azure Blob Storage -Tout d’abord, importez les variables d’environnement stockées dans votre coffre-fort de secrets. Vous importerez soit depuis « minio-gateway » soit depuis « fdi-gateway » selon l'endroit où vos données ont été ingérées. +Tout d’abord, importez les variables d’environnement stockées dans votre coffre-fort de secrets. Vous importerez soit depuis `minio-gateway` soit depuis `fdi-gateway` selon l'endroit où vos données ont été ingérées. ``` jovyan@rstudio-0 :~$ source /vault/secrets/fdi-gateway-protected-b diff --git a/docs/fr/images/container-mount.png b/docs/fr/images/container-mount.png new file mode 100644 index 0000000000000000000000000000000000000000..c159a0d3b24fda81f06ce1816dd9a8ca69ad7aad GIT binary patch literal 118470 zcmdRWXH-+$+AhilMZkuFih^#XC{5`d1RGLqAff37LQQC)SEZ;RpnyOKRS-m)(gLAG zKt)Ibp+*QH(n1MMLJRe-fS#>>XP@)kG47wc#&9@*m9^ga_UC!$%o78>tDJj|?O|eK z;?%l!=_V5sD~XAT`R#62;5RpERqDWhJKSzw)nF=YIX({j@Q2Ms-HS|2@1oh)?y~?t zvpHWgc4J~X2xI))(GTYLVq)5SrFH4zZ7=i5b@sL6cTz?-tA?y$cRt*>J)N+>a7U%Z z&WGyPG)}6Ye6WA_C-sxh{@A&L?Qzh({gJ}LXC59oR4;as&U$F)sfU{qiVgYjCcRHH z`A4oUDk)7&PfoU`^iYT1MK{t3IpU-oJ%2q<_rScWygUo3=uyquJMMaSj8fhtE6x;7 zc&^QCuA?KD(>7^=NopJ4qA50Nt8j&-J~y1|>ZIjjzpqo=m)xAxaMn_P#$vZFgFl+u zTa;-!G@ih3Joi6whe5TzF0N3E5NB^?zh=3i3c`mdLeh5pxc$(0eo2d@+9tgfr?f%X 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YtNNgd^lk=MU}4MP>FVdQ&MBb@0Q_g3c>n+a diff --git "a/docs/fr/7-MLOps/Aper\303\247u.md" "b/docs/fr/7-MLOps/Aper\303\247u.md" new file mode 100644 index 000000000..3192f6ac2 --- /dev/null +++ "b/docs/fr/7-MLOps/Aper\303\247u.md" @@ -0,0 +1,17 @@ +MLOps, ou Machine Learning Operations, fait référence à l'ensemble de pratiques et d'outils qui permettent aux organisations de développer, déployer et maintenir des modèles d'apprentissage automatique à grande échelle. MLOps vise à rationaliser le processus de bout en bout de création et de déploiement de modèles d'apprentissage automatique en intégrant les différentes étapes du cycle de vie de l'apprentissage automatique dans un flux de travail cohérent et automatisé. + +MLOps implique une gamme d'activités différentes, notamment la préparation et le prétraitement des données, la formation et l'optimisation des modèles, le déploiement et la maintenance des modèles, la surveillance et la maintenance, ainsi que l'amélioration continue. Certains des composants clés de MLOps incluent : + +1. **Gestion des données :** MLOps implique la gestion et le traitement de grandes quantités de données pour garantir la qualité et l'exactitude des modèles d'apprentissage automatique. Cela implique des activités telles que le nettoyage des données, l'intégration des données et la transformation des données. + +2. **Formation et optimisation des modèles :** MLOps implique le développement et le test de modèles d'apprentissage automatique, ainsi que leur optimisation en termes de performances et de précision. Cela peut impliquer d'expérimenter différents algorithmes, hyperparamètres et techniques de prétraitement des données. + +3. **Déploiement de modèles :** MLOps implique le déploiement de modèles d'apprentissage automatique dans des environnements de production, les rendant disponibles pour une utilisation dans des applications du monde réel. Cela peut impliquer la conteneurisation des modèles pour un déploiement et une mise à l'échelle faciles, ainsi que la configuration d'API et d'autres interfaces pour la diffusion de modèles. + +4. **Surveillance et maintenance :** MLOps implique la surveillance des modèles d'apprentissage automatique en production pour garantir qu'ils fonctionnent comme prévu. Cela peut impliquer la configuration d'alertes et de notifications en cas de défaillance du modèle, ainsi que la mise en œuvre de processus de maintenance et de mise à jour du modèle. + +5. **Amélioration continue :** MLOps implique l'amélioration continue des modèles d'apprentissage automatique au fil du temps, sur la base des commentaires des utilisateurs et de l'analyse continue des données de performances. Cela peut impliquer de recycler les modèles avec de nouvelles données ou d'intégrer les commentaires des utilisateurs pour affiner les modèles. + +Afin de mettre en œuvre efficacement les MLOps, les organisations doivent généralement adopter une gamme d'outils et de technologies différents, notamment des plates-formes de gestion de données, des cadres d'apprentissage automatique, des outils de conteneurisation et des outils de surveillance et de journalisation. Ils doivent également établir des flux de travail et des processus clairs pour gérer les différentes étapes du cycle de vie du machine learning, ainsi que mettre en œuvre des mesures de gouvernance et de conformité pour garantir la confidentialité et la sécurité des données. + +En résumé, MLOps est un composant essentiel du cycle de vie du machine learning, permettant aux organisations de développer, déployer et maintenir des modèles de machine learning à grande échelle. En adoptant les pratiques et outils MLOps, les organisations peuvent rationaliser leurs flux de travail d'apprentissage automatique, améliorer la précision et les performances des modèles et offrir plus de valeur aux utilisateurs et aux parties prenantes. \ No newline at end of file diff --git a/docs/fr/7-MLOps/Integration-PaaS.md b/docs/fr/7-MLOps/Integration-PaaS.md new file mode 100644 index 000000000..340f41552 --- /dev/null +++ b/docs/fr/7-MLOps/Integration-PaaS.md @@ -0,0 +1,54 @@ +# Aperçu + +_L'un des principaux avantages de la plateforme ETAA est sa capacité à s'intégrer aux plateformes d'apprentissage automatique populaires telles que Databricks et AzureML._ + +L'Espace de travail d'analyse avancée (ETAA) est une plateforme d'analyse de données open source conçue pour être hautement intégrable. Cela signifie qu'il peut être facilement intégré à d'autres plates-formes et outils pour étendre ses capacités et rationaliser les flux de travail. + +Un exemple de diagramme illustrant une stratégie de connexion PaaS possible : + +
+ +
+![PaaS](../images/PaaS.png) +
+ +**Configuration :** Si vous avez besoin d'aide pour intégrer une offre de plateforme en tant que service, nous serons heureux de vous aider! + +## Intégration avec les offres de plateforme externe en tant que service (PaaS) + +_L'intégration est la clé du succès._ + +
+[![Integrer avec PaaS](../images/IntegratePaaS.PNG)]() +
+ +Notre plateforme open source offre une optionnalité inégalée à nos utilisateurs. En permettant aux utilisateurs d'utiliser des outils open source, nous leur permettons d'utiliser leurs cadres préférés de science des données et d'apprentissage automatique. Mais la véritable puissance de notre plateforme vient de sa capacité à s'intégrer à de nombreuses offres Platform as a Service (PaaS), comme Databricks ou AzureML. Cela signifie que nos utilisateurs peuvent tirer parti de la puissance du cloud pour exécuter des pipelines complexes de traitement de données et d’apprentissage automatique à grande échelle. Avec la possibilité de s'intégrer aux offres PaaS, notre plateforme permet à nos utilisateurs de faire passer leur travail au niveau supérieur, en leur donnant le pouvoir d'adapter facilement leurs charges de travail et de profiter des dernières innovations dans le domaine de la science des données et des machines. apprentissage. En offrant ce niveau d'optionnalité, nous garantissons que nos utilisateurs peuvent toujours choisir le bon outil pour le travail et garder une longueur d'avance dans un domaine en constante évolution. + +Nous pouvons intégrer de nombreuses offres Platform as a Service (PaaS), comme Databricks ou AzureML. + + +## Databricks + +- [Databricks de Microsoft](https://azure.microsoft.com/en-ca/services/databricks/) + +Databricks est une plateforme basée sur le nuage qui fournit une plateforme d'analyse unifiée pour le traitement du Big Data et l'apprentissage automatique. Avec son puissant moteur informatique distribué et ses outils de flux de travail rationalisés, Databricks est un choix populaire pour créer et déployer des modèles d'apprentissage automatique. En s'intégrant à Databricks, la plateforme ETAA peut exploiter ses capacités informatiques distribuées pour former et déployer des modèles d'apprentissage automatique à grande échelle. + +## AzureML + +- [Azure ML de Microsoft](https://azure.microsoft.com/en-us/services/machine-learning/) + +AzureML est une autre plate-forme d'apprentissage automatique populaire qui fournit une large gamme d'outils pour créer, former et déployer des modèles d'apprentissage automatique. En s'intégrant à AzureML, la plateforme ETAA peut tirer parti de ses puissants outils de création et de formation de modèles, ainsi que de sa capacité à déployer des modèles dans le nuage. + +### Exemples + +_Des exemples de la façon d'intégrer la plate-forme ETAA avec ces plates-formes et d'autres peuvent être trouvés sur le référentiel MLOps Github._ + +- [Dépôt Github MLOps ](https://github.com/StatCan/aaw-kubeflow-mlops) + +Ce référentiel contient une gamme d'exemples et de didacticiels permettant d'utiliser la plateforme ETAA dans divers flux de travail d'apprentissage automatique, notamment la préparation des données, la formation de modèles et le déploiement de modèles. + +## Conclusion + +En s'intégrant à des plateformes d'apprentissage automatique populaires telles que Databricks et AzureML, la plateforme ETAA fournit une solution puissante et flexible pour créer, déployer et gérer des flux de travail d'apprentissage automatique à grande échelle. + +En tirant parti des intégrations et des outils fournis par ces plateformes, les data scientists et les ingénieurs en apprentissage automatique peuvent accélérer leurs flux de travail et obtenir de meilleurs résultats avec moins d'efforts. \ No newline at end of file diff --git a/docs/fr/7-MLOps/Machine-Learning-Model-Cloud-Storage.md b/docs/fr/7-MLOps/Machine-Learning-Model-Cloud-Storage.md deleted file mode 100644 index cd1dd41fc..000000000 --- a/docs/fr/7-MLOps/Machine-Learning-Model-Cloud-Storage.md +++ /dev/null @@ -1,53 +0,0 @@ -# Statcan Protected B Data - - -!!! info "Protected B" - The AAW is certified for hosting Protected B data! - -In order to upload Protected B data to the AAW, users will need to request access through the Customer Success Unit (CSU). AAW users will also need to provide a namespace, get a sponsor and get approval from OPMIC. Once the process has been approved, our Fair Data Infrastructure (FDI) team will then create a folder on Net A which in turn will give access to user(s) through the active directory. The data will then be able to transfer from Net A to AAW Cloud - -Storing machine learning models in a protected cloud storage environment is essential for ensuring the security and privacy of sensitive data. The Advanced Analytics Workspace (AAW) provides a secure and robust cloud storage environment that can be used to store machine learning models and other data assets. - -The AAW platform provides a protected cloud storage environment that is designed to meet the most stringent data security and privacy requirements. The storage environment is protected by industry-standard encryption and access controls, which ensures that only authorized personnel can access sensitive data. This protects against unauthorized access, data breaches, and other security threats. - -In addition to its robust security features, the AAW cloud storage environment is also highly scalable and flexible. This means that data scientists and machine learning engineers can easily scale their storage needs as their datasets and model sizes grow. This enables them to store and manage large volumes of data and models without having to worry about storage limitations or performance bottlenecks. - -Storing machine learning models in a protected cloud storage environment on the Advanced Analytics Workspace provides a secure, scalable, and flexible solution for managing and protecting sensitive data. By leveraging the cloud storage capabilities provided by the AAW platform, data scientists and machine learning engineers can focus on building and deploying their models with confidence, knowing that their data is protected and secure. - -## Cloud Storage - - -!!! info "Cloud Storage Advantages" - Cloud storage offers several advantages for data science and machine learning, particularly in terms of scalability, accessibility, and cost-effectiveness. - -Firstly, cloud storage enables data scientists to store and process large amounts of data without having to worry about the limitations of local storage. This is particularly important in the context of machine learning, where large datasets are required for training and testing models. Cloud storage allows data scientists to scale up their storage capacity as needed, without having to invest in expensive hardware. - -Secondly, cloud storage allows data scientists to access data from anywhere, using any device with an internet connection. This enables collaboration across geographically distributed teams and allows data scientists to work remotely. Additionally, cloud storage makes it easier to share data with other stakeholders, such as business partners or customers. Finally, cloud storage is typically more cost-effective than on-premises storage, particularly for smaller organizations or those with limited IT resources. - -Overall, cloud storage is a reliable and convenient solution for storing and managing your data. Whether you need to store large amounts of data or just a few files, cloud storage makes it easy to manage your storage needs without the hassle of traditional storage solutions. - -The AAW platform provides several types of storage: - -- Disks (also called Volumes on the Kubeflow Notebook Server creation screen) -- Data Lakes (coming soon) - -Depending on your use case, either disk or bucket may be most suitable. Our [storage overview](../5-Storage/Overview.md) will help you compare them. - -### Disks -
- [![Disks](../images/Disks.PNG)](../5-Storage/Disks.md) -
-**[Disks](../5-Storage/Disks.md)** are added to your notebook server by adding Data Volumes. - -### Data Lakes (Coming Soon) - -A data lake is a central repository that allows you to store all your structured and unstructured data at any scale. It's a cost-effective way to store and manage all types of data, from raw data to processed data, and it's an essential tool for data scientists. - -One of the primary advantages of a data lake is its flexibility. It allows you to store all types of data without the need to define a schema in advance, which is especially useful when dealing with unstructured data. This flexibility allows data scientists to easily explore, experiment, and extract insights from their data without being constrained by the limitations of a traditional relational database. - -Data lakes also enable data scientists to centralize their data, making it easier to manage and analyze large volumes of data from various sources. With a data lake, data scientists can easily ingest and store data from a variety of sources, such as databases, cloud storage, and third-party APIs. Additionally, data lakes often provide features for data governance, metadata management, and access control, which helps ensure the data is of high quality and compliant with relevant regulations. - -Furthermore, cloud-based data lakes provide scalable, cost-effective storage solutions that can be easily expanded at the click of a button. As a data scientist's data storage needs grow, they can add additional storage capacity to their data lake with minimal effort, without worrying about the underlying infrastructure or maintenance. - -Overall, data lakes are a critical tool for data scientists, as they provide the flexibility, scalability, and ease of use needed to store and manage large volumes of data, enabling data scientists to focus on extracting insights and value from the data. - diff --git a/docs/fr/7-MLOps/Machine-Learning-Model-Serving.md b/docs/fr/7-MLOps/Machine-Learning-Model-Serving.md deleted file mode 100644 index 2b02e2e10..000000000 --- a/docs/fr/7-MLOps/Machine-Learning-Model-Serving.md +++ /dev/null @@ -1,39 +0,0 @@ -# Introduction to Model Serving - -In the context of governments, serving machine learning models means making trained models available for use by other applications and systems. This could include making predictions or classifications based on input data, or providing insights and recommendations based on the results of data analysis. - -Serving machine learning models in a government context raises important issues related to data privacy. Government agencies are often responsible for collecting and managing sensitive personal data, such as health records, financial data, and criminal records. When serving machine learning models, it's critical to ensure that this data is protected and that access to it is strictly controlled. - -To address these concerns, government agencies must implement robust data privacy and security measures when serving machine learning models. This could include encrypting data both at rest and in transit, implementing access controls and user authentication, and regularly monitoring for data breaches and vulnerabilities. - -In addition to data privacy and security, it's also important to consider the ethical implications of serving machine learning models in a government context. Machine learning models can be biased or discriminatory, leading to unfair treatment of certain groups of people. To mitigate these risks, government agencies must carefully evaluate and monitor their machine learning models, and take steps to address any biases or discrimination that may arise. - -Overall, serving machine learning models in a government context requires careful consideration of data privacy, security, and ethical concerns. By implementing robust measures to protect personal data and prevent bias, government agencies can leverage the power of machine learning to make better decisions and improve outcomes for citizens while maintaining trust and transparency. - -## Why serve with us? - -Serving machine learning models with the Advanced Analytics Workspace (AAW) has several advantages. First, the AAW is an open-source data analytics platform that provides access to a variety of advanced analytics tools, including Python, R, and SAS. This makes it easy to deploy machine learning models and integrate them into existing workflows. - -Second, the AAW supports multiple MLOps frameworks, including Couler, Seldon, and Argo Workflows. These frameworks provide a range of features, including model versioning, model serving, and model monitoring, that simplify the process of deploying and managing machine learning models in production. - -Third, the AAW provides a secure and scalable platform for serving machine learning models with Protected B status. Models can be served using containerized environments, such as Docker, which provide a high level of isolation and security. The AAW also provides access to cloud computing resources, allowing users to scale up their compute power as needed to handle high volumes of requests. - -Finally, the AAW is a collaborative platform that allows users to share code and data with other researchers and analysts. This fosters a community of users who can learn from each other's work and collaborate on projects that require advanced analytics capabilities. - -In summary, serving machine learning models with the Advanced Analytics Workspace provides access to advanced analytics tools, multiple MLOps frameworks, a secure and scalable Proteced B platform, and a collaborative community of users, making it an ideal platform for data scientists and analysts who want to deploy and manage machine learning models in production. - -## Seldon Core - -Seldon Core is an open-source platform for deploying, scaling, and monitoring machine learning models on Kubernetes. It provides a simple and efficient way to deploy machine learning models as microservices in a production environment. - -Serving machine learning models using Seldon Core involves the following steps: - -1. Model packaging: The first step is to package the trained machine learning model in a container image with all the required dependencies. Seldon Core supports various machine learning frameworks, including TensorFlow, PyTorch, and Scikit-learn. - -2. Model deployment: Once the container image is created, the next step is to deploy the model on Kubernetes using Seldon Core. This involves defining the deployment configuration file, which specifies the resources required for the deployment, such as the number of replicas and the compute resources. - -3. Model serving: Once the model is deployed, Seldon Core exposes a REST API endpoint that can be used to make predictions. Clients can send requests to the endpoint with input data, and the model will return the corresponding output. Seldon Core also supports various deployment patterns, such as canary deployment and A/B testing, to enable easy experimentation and testing of different models. - -4. Model monitoring: Seldon Core provides various monitoring capabilities to track the performance of deployed models. This includes real-time monitoring of model metrics, such as latency and throughput, as well as logging of request and response data for debugging purposes. - -Seldon Core makes it easy to serve machine learning models at scale, with support for high availability, scalability, and fault tolerance. It also provides integration with various Kubernetes-native tools, such as Istio and Prometheus, to enable advanced monitoring and observability. diff --git a/docs/fr/7-MLOps/Machine-Learning-Training-Pipelines.md b/docs/fr/7-MLOps/Machine-Learning-Training-Pipelines.md deleted file mode 100644 index 8d2fa5817..000000000 --- a/docs/fr/7-MLOps/Machine-Learning-Training-Pipelines.md +++ /dev/null @@ -1,414 +0,0 @@ -# Training Machine Learning Models on the AAW - -
-![Robots in work](../images/mlops.jpg) -
- - -!!! info - Training machine learning models involves using algorithms to learn patterns and relationships in data. This process involves identifying features or variables that are relevant to the problem at hand and using these features to make predictions or classifications. - -## Why train with us? - -_Training machine learning models on the Advanced Analytics Workspace (AAW) has several advantages._ - -1. **Open Source:** The AAW is an *open source data platform hosted by Statistics Canada* that provides secure (Protected B) access to a variety of data sources, including census data, surveys, and administrative records. This data can be used to train machine learning models and generate insights that can inform policy decisions and improve business processes. - -2. **Versatile:** The AAW is *designed to handle large and complex datasets*. It provides access to a range of advanced analytics tools, in any language you like, including *Python, R, and SAS*, which can be used to preprocess data, train machine learning models, and generate visualizations. *Because the AAW leverages cloud technologies, *users can scale up their computing power as needed*. -* -3. **Secure:** The AAW is a *secure platform (Protected B) that adheres to the highest standards of data privacy and security*. Data can be stored and processed on the platform without risk of unauthorized access or data breaches. - -## MLOps and Data Pipelines - - -!!! info "Optimize Data Workflows" - MLOps and data pipelines are important tools used in the field of data science to manage and optimize data workflows. - -### MLOps - -MLOps refers to the set of practices and tools used to manage the entire lifecycle of a machine learning model. This includes everything from developing and training the model to deploying it in production and maintaining it over time. MLOps ensures that machine learning models are reliable, accurate, and scalable, and that they can be updated and improved as needed. - -### Data Pipelines - -Data pipelines are a series of steps that help move data from one system or application to another. This includes collecting, cleaning, transforming, and storing data, as well as retrieving it when needed. Data pipelines are important for ensuring that data is accurate, reliable, and accessible to those who need it. - - -!!! info "Automation and Reliability" - MLOps and data pipelines help organizations manage the complex process of working with large amounts of data and developing machine learning models. By automating these processes and ensuring that data is accurate and reliable, organizations can save time and resources while making better decisions based on data-driven insights. - -### Why Containerized MLOps? - -The advantages of using a containerized approach for training machine learning models with Argo Workflows include: - -1. **Reproducibility:** Containerizing the machine learning model and its dependencies ensures that the environment remains consistent across different runs, making it easy to reproduce results. - -2. **Scalability:** Argo Workflows can orchestrate parallel jobs and complex workflows, making it easy to scale up the training process as needed. - -3. **Portability:** Containers can be run on any platform that supports containerization, making it easy to move the training process to different environments or cloud providers. - -4. **Collaboration:** By pushing the container to a container registry, other users can easily download and use the container for their own purposes, making it easy to collaborate on machine learning projects. - -Argo Workflows and containerization provide a powerful and flexible approach for training machine learning models. By leveraging these tools, data scientists and machine learning engineers can build, deploy, and manage machine learning workflows with ease and reproducibility. - -## How to Train Models - -There are multiple ways to train machine learning models and it is not our place to tell anyone how to do it. That being said we have provided below a couple of guides on how to train machine learning models using the tools available on the AAW. The first tutorial is about training a simple model directly in a JupyterLab notebook. The second tutorial assumes the user is more advanced and is interested in defining an MLOps pipeline for training models using Argo Workflows. - -### Create a Notebook Server on the AAW - - -!!! info "Notebook Servers" - Regardless of whether you plan on working in JupyterLab, R Studio or something more advanced with Argo Workflows, you'll need the appropriate notebook server. [Follow the instructions found here to get started.](https://docs.google.com/presentation/d/12yTDlbMCmbg0ccdea2h0vwhs5YTa_GHm_3DieG5A-k8/edit?usp=sharing) - -### Using JupyterLab - -![JupyterLab](../images/jupyterlab.jpg) - - -!!! info "JupyterLab is Popular" - Training machine learning models with JupyterLab is a popular approach among data scientists and machine learning engineers. - -Here you will find the steps required to train a machine learning model with JupyterLab on the AAW. Because we are a multi-lingual environment, we've done our best to provide code examples in our most popular languages, `Python`, `R` and `SAS`. - -#### 1. Import the required libraries - -Once you have a JupyterLab session running, you need to import the required libraries for your machine learning model. This could include libraries such as `NumPy`, `Pandas`, `Scikit-learn`, `Tensorflow`, or `PyTorch`. If you are using `R`, you'll want `tidyverse`, `caret` and `janitor`. - -=== "Python" - ``` py title="libraries.py" linenums="1" - #!/usr/bin/env python - - # tensorflow and keras for building and training deep learning models - import tensorflow as tf - from tensorflow import keras - - # numpy for numerical computations - import numpy as np - - # pandas for data manipulation and analysis - import pandas as pd - - # matplotlib for data visualization - import matplotlib.pyplot as plt - ``` -=== "R" - ``` r title="libraries.R" linenums="1" - #!/usr/bin/env Rscript - - # tidyverse for awesome data analysis and munging tools - library(tidyverse) - - # janitor to clean your data - library(janitor) - - # caret for easy machine learning - library(caret) - ``` -=== "SASPy" - ``` py title="libraries.py" linenums="1" - #!/usr/bin/env python - - # the only library you'll need to access SAS from Python - import saspy - ``` -=== "SAS" - ``` sas title="libraries.sas" linenums="1" - - ``` - - -!!! note "About the Code" - The code examples you see in this document and throughout the documentation are for illustrative purposes to get you started on your projects. Depending on the specific task or project, other libraries and steps may be required. - -#### 2. Load and preprocess the data - -Next, you need to load and preprocess the data you'll be using to train your machine learning model. This could include data cleaning, feature extraction, and normalization. The exact preprocessing steps you'll need to perform will depend on the specific dataset you're working with, the requirements of your machine learning model and the job to be done. - -=== "Python" - ``` py title="load_data.py" linenums="1" - #!/usr/bin/env python - - # Import necessary packages - import pandas as pd - from sklearn.preprocessing import StandardScaler - - # Load data from a CSV file - data = pd.read_csv('data.csv') - - # Data cleaning! A lot more can be done, this is basic - data = data.dropna() # Drop rows with missing values - data = data.drop_duplicates() # Drop duplicate rows - - # Feature extraction - X = data[['feature1', 'feature2', 'feature3']] # Select relevant features - - # Normalization - scaler = StandardScaler() # Create a scaler object - X_norm = scaler.fit_transform(X) # Normalize the feature values - ``` -=== "R" - ``` r title="load_data.R" linenums="1" - #!/usr/bin/env Rscript - - # Import necessary packages - library(tidyverse) - library(janitor) - - # Load data from a CSV file - data <- read_csv("data.csv") - - # Clean data using janitor - data_cleaned <- data %>% - # Remove leading/trailing whitespace in column names - clean_names() %>% - # Remove rows with missing values - remove_empty() %>% - # Convert date column to Date format - mutate(date = as.Date(date, format = "%m/%d/%Y")) %>% - # Remove duplicate rows - distinct() %>% - # Reorder columns - select(date, column2, column1, column3) - ``` -=== "SASPy" - ``` py title="load_data.py" linenums="1" - #!/usr/bin/env python - - # Import necessary packages - import saspy - - # Start a SAS session and check configuration information - sas = saspy.SASsession(cfgname='default') - - # Load data from a CSV file - data = sas.read_csv("./data.csv") - ``` -=== "SAS" - ``` sas title="load_data.sas" linenums="1" - /* Reading a comma delimited file with a .csv extension */ - /* */ - /* Since the DBMS= value is CSV, you do not have to use the DELIMITER= */ - /* statement. By default, it is assumed the variable names are on the first */ - /* row, so the GETNAMES= statement is not required. */ - /* */ - /* Create comma delimited test file to read using PROC IMPORT below. */ - - /* Load data from a CSV file */ - proc import - datafile='data.csv' - out=data - dbms=csv - replace; - run; - - /* Display data */ - proc print; - run; - ``` - -#### 3. Split the data into training and testing sets - - Once the data is preprocessed, you need to split it into training and testing sets. The training set will be used to train the machine learning model, while the testing set will be used to evaluate its performance. - -=== "Python" - ``` py title="train_test.py" linenums="1" - #!/usr/bin/env python - - # Import necessary packages - import pandas as pd - from sklearn.model_selection import train_test_split - - # Split the data into training and testing sets - X_train, X_test, y_train, y_test = train_test_split(X_norm, - data['target'], test_size=0.2, random_state=42) - ``` -=== "R" - ``` r title="train_test.R" linenums="1" - #!/usr/bin/env Rscript - - # Import necessary packages - library(caret) - - # Load the dataset - data <- read.csv("my-dataset.csv") - - # Set the seed for reproducibility - set.seed(123) - - # Split the dataset into train and test using caret's createDataPartition function - train_index <- createDataPartition(data$target_variable, p = 0.7, list = FALSE) - train_data <- data[train_index, ] - test_data <- data[-train_index, ] - ``` -=== "SASPy" - ``` py title="train_test.py" linenums="1" - #!/usr/bin/env python - - ``` -=== "SAS" - ``` sas title="train_test.sas" linenums="1" - - ``` - - -!!! note - We split the data into training and testing sets using the `train_test_split` function from `scikit-learn`, which randomly splits the data into two sets based on the specified test size and random seed. - -#### 4. Define and train the machine learning model - -With the data split, you can now define and train your machine learning model using the training set. This could involve selecting the appropriate algorithm, hyperparameter tuning, and cross-validation. - -=== "Python" - ``` py title="train.py" linenums="1" - #!/usr/bin/env python - - # Import necessary packages - from sklearn.ensemble import RandomForestClassifier - from sklearn.model_selection import train_test_split - - # Load the dataset - data = pd.read_csv("my-dataset.csv") - - # Split the dataset into train and test - X_train, X_test, y_train, y_test = train_test_split(data.iloc[:, :-1], data.iloc[:, -1], test_size=0.3, random_state=123) - - # Train the model - model = RandomForestClassifier(n_estimators=100, random_state=123) - model.fit(X_train, y_train) - - # Print the accuracy score on the test data - print("Accuracy on test set: {:.3f}".format(model.score(X_test, y_test))) - ``` -=== "R" - ``` r title="train.R" linenums="1" - #!/usr/bin/env Rscript - - # Import necessary packages - library(caret) - - # Load the dataset - data <- read.csv("my-dataset.csv") - - # Set the seed for reproducibility - set.seed(123) - - # Split the dataset into train and test using caret's createDataPartition function - train_index <- createDataPartition(data$target_variable, p = 0.7, list = FALSE) - train_data <- data[train_index, ] - test_data <- data[-train_index, ] - - # Define the training control - train_control <- trainControl(method = "cv", number = 5) - - # Train the model using caret's train function, (method = "rf" is for random forest) - model <- train(target_variable ~ ., data = train_data, method = "rf", trControl = train_control) - - # Print the model object to view the results - print(model) - ``` -=== "SASPy" - ``` py title="train.py" linenums="1" - #!/usr/bin/env python - - # Import necessary packages - import saspy - import pandas as pd - - # Establish a connection to a SAS session - sas = saspy.SASsession() - - # Load the dataset - data = pd.read_csv("my-dataset.csv") - - # Upload the dataset to SAS - sas_df = sas.df2sd(data, "mydata") - - # Split the dataset into train and test - train, test = sas.surveyselect(data=sas_df, - method="SRS", - seed=123, - samprate=0.7, - outall=True, - strata="target_variable", - partind=True) - - # Train the model using the HPFOREST procedure - model = sas.hpforest(data=train, - target="target_variable", - input="input_variable_1-input_variable_n", - partition="rolevar", - rolevars={"test": "0", "train": "1"}, - nominals=["input_variable_1-input_variable_n"], - forestopts={"ntree": 100, "seed": 123}) - - # Score the model on the test data - predictions = model.predict(newdata=test, out=pred_out) - - # Compute the accuracy score on the test data - accuracy = sas.freq(data=predictions, tables="target_variable*p_target_variable", nocum=True, nocol=True) - - # Print the accuracy score - print("Accuracy on test set: {:.3f}".format(accuracy.Frequency.iloc[0, 1] / accuracy.Frequency.iloc[:, 1].sum())) - - # Disconnect from the SAS session - sas.disconnect() - ``` -=== "SAS" - ``` sas title="train.sas" linenums="1" - /* Load the dataset */ - proc import datafile="my-dataset.csv" out=mydata dbms=csv replace; - run; - - /* Split the dataset into train and test */ - proc surveyselect data=mydata method=srs seed=123 out=selected outall - samprate=0.7; - strata target_variable; - run; - - /* Train the model */ - proc hpforest data=selected; - class _all_; - target target_variable / level=nominal; - partition rolevar=target_variable(test="0" train="1"); - input _all_; - forest ntree=100 seed=123; - run; - - /* Score the model on the test data */ - proc hpforest predict testdata=selected out=testout; - run; - - /* Print the accuracy score on the test data */ - proc freq data=testout; - tables target_variable*p_target_variable / nocum nocol; - run; - ``` - -#### 5. Evaluate the model - -After training the model, you need to evaluate its performance on the testing set. This will give you an idea of how well the model will perform on new, unseen data. - -#### 6. Deploy the model - -Finally, you can deploy the trained machine learning model in a production environment. - -### Using Argo Workflows - -![Workflow Production Art](../images/argo-workflows-assembly-line.jpg) - - -!!! info "MLOps Best Practices" - Argo Workflows is an excellent tool for anyone looking to implement MLOps practices and streamline the process of training and deploying machine learning models or other data science tasks such as ETL. - -**[Argo Workflows](https://argoproj.github.io/argo-workflows/)** is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Argo Workflows is implemented as a Kubernetes CRD (Custom Resource Definition). It is particularly well-suited for use in machine learning and data science workflows. - -Argo Workflows allows you to - -- Define workflows where each step in the workflow is a container. -- Model multi-step workflows as a sequence of tasks or capture the dependencies between tasks using a directed acyclic graph (DAG). -- Easily run compute intensive jobs for machine learning or data processing in a fraction of the time using Argo Workflows on Kubernetes. -- Run CI/CD pipelines natively on Kubernetes without configuring complex software development products. - -making it easy to manage the entire end-to-end machine learning pipeline. With Argo Workflows, you can easily build workflows that incorporate tasks such as data preprocessing, model training, and model deployment, all within a Kubernetes environment. - -See the [argo workflows section](../3-Pipelines/Argo.md) for more details. \ No newline at end of file diff --git a/docs/fr/7-MLOps/Machine-Learning.md b/docs/fr/7-MLOps/Machine-Learning.md deleted file mode 100644 index 49fa0022a..000000000 --- a/docs/fr/7-MLOps/Machine-Learning.md +++ /dev/null @@ -1,367 +0,0 @@ -# Machine Learning Models - -Machine learning models are computational algorithms that are designed to automatically learn patterns and relationships from data. These models are trained on a dataset, which is typically a collection of examples or instances, each of which consists of a set of features or variables, as well as a target variable or output. - -The goal of a machine learning model is to identify patterns and relationships within the data that can be used to make predictions or decisions about new, unseen data. This involves developing a mathematical representation of the relationship between the input features and the output variable, based on the patterns observed in the training data. Once the model is trained, it can be used to make predictions or decisions about new, unseen data. - -There are several different types of machine learning models, each of which is designed to address different types of problems or data. Some of the most common types of machine learning models include: - -1. **Regression Models:** Regression models are used to predict continuous numerical values, such as stock prices or housing prices. - -2. **Classification Models:** Classification models are used to predict discrete categorical values, such as whether a customer will buy a product or not. - -3. **Clustering Models:** Clustering models are used to identify groups or clusters within a dataset based on similarities between instances. - -4. **Recommendation Models:** Recommendation models are used to recommend products or services to users based on their past behavior or preferences. - -5. **Neural Networks:** Neural networks are a type of machine learning model that is designed to mimic the structure and function of the human brain. They are commonly used in image recognition, speech recognition, and natural language processing applications. - - -!!! info "Machine Learning Models Can be Biased" - Machine learning models are a powerful tool for analyzing and making predictions about data, and they have a wide range of applications in fields such as finance, healthcare, marketing, and more. However, it is important to note that machine learning models are not perfect and can sometimes make errors or produce biased results. Therefore, it is important to carefully evaluate and test machine learning models before using them in real-world applications. - -## Examples - -### Linear Regression - -![Linear Regression](../images/linear-regression.jpg) - - -!!! info "Linear Regression" - $$ - \hat{Y}_i = \hat{\beta}_0 + \hat{\beta}_1 X_i + \hat{\epsilon}_i - $$ - - _Where $\hat{Y}_i$ denotes the $i$th estimator of the true value $Y$ based on the $i$th training epoch. Each $\hat{\beta}$ is a parameter to be learned. $\hat{\epsilon}_i$ is the amount of noise permitted in the model and may vary depending on the training epoch number denoted by $i$. Each $X_i$ represents the $i$th batch of training data._ - -In classical statistical models like linear regression, the goal is to find a line that best fits the data, allowing us to make predictions about new data points. - -As the complexity of the problem increases, more sophisticated algorithms are needed, such as decision trees, support vector machines, and random forests. However, these methods have limitations, and they may not be able to capture complex patterns in large datasets. - -#### Example Code - -=== "Python" - ``` py title="linear_regression.py" linenums="1" - #!/usr/bin/env python - - # Load the required libraries - import pandas as pd - from sklearn.model_selection import train_test_split - from sklearn.linear_model import LinearRegression - from sklearn.metrics import mean_squared_error - - # Load the dataset - data = pd.read_csv('path/to/dataset.csv') - - # Split the data into training and testing sets - X_train, X_test, y_train, y_test = train_test_split(data.drop('target_variable', axis=1), data['target_variable'], test_size=0.2) - - # Train the linear regression model - linear_model = LinearRegression() - linear_model.fit(X_train, y_train) - - # Make predictions on the testing set - y_pred = linear_model.predict(X_test) - - # Evaluate the model performance - mse = mean_squared_error(y_test, y_pred) - rmse = mse ** 0.5 - print('Root Mean Squared Error:', rmse) - ``` - -=== "R" - ``` r title="linear_regression.r" linenums="1" - #!/usr/bin/env Rscript - - # Set random seed for reproducibility - set.seed(123) - - # Load the dataset - data <- read.csv('path/to/dataset.csv') - - # Split the data into training and testing sets - train_index <- sample(1:nrow(data), size=0.8*nrow(data)) - train_data <- data[train_index,] - test_data <- data[-train_index,] - - # Train the linear regression model - lm_model <- lm(target_variable ~ ., data=train_data) - - # Make predictions on the testing set - y_pred <- predict(lm_model, newdata=test_data[,-which(names(test_data)=='target_variable')]) - - # Evaluate the model performance - mse <- mean((y_pred - test_data$target_variable)^2) - rmse <- sqrt(mse) - print(paste('Root Mean Squared Error:', rmse)) - ``` - -### Support Vector Machine (SVM) - -![Support Vector Machine](../images/svm.jpg) - - -!!! note "SVM" - $$ - \underset{\mathbf{w},b,\boldsymbol{\xi}}{\operatorname{minimize}} \hspace{0.2cm} \frac{1}{2} ||\mathbf{w}||^2 + C \sum_{i=1}^{N} \xi_i - $$ - - $$ - \text{where} \hspace{0.2cm} y_i(\mathbf{w}^T\mathbf{x}_i + b) \geq 1-\xi_i \quad \text{and} \quad \hspace{0.2cm} \xi_i \geq 0 \hspace{0.2cm} \forall i \in {1,2,...,N} - $$ - - _In this formula, we use the standard SVM formulation where $\mathbf{w}$ is the weight vector, $b$ is the bias term, and $\boldsymbol{\xi}$ is the slack variable vector. The objective is to minimize the L2-norm of the weight vector $\mathbf{w}$, subject to the constraint that all training examples are classified correctly with a margin of at least 1, plus an allowance for some margin violations controlled by the regularization parameter $C$. The target variable $y_i$ takes values of either 1 or -1, representing the two classes in the binary classification problem, and $\mathbf{x}_i$ is the feature vector for the $i$th training example._ - -A support vector machine (SVM) is a supervised machine learning algorithm that can be used for classification, regression, and outlier detection. It is a popular algorithm in the field of machine learning, especially for solving classification problems. - -The basic idea behind SVM is to find a hyperplane that best separates the input data into different classes. In a two-class classification problem, the hyperplane is a line that divides the data points of one class from the data points of the other class. SVM tries to find the hyperplane that maximizes the margin between the two classes, where the margin is the distance between the hyperplane and the nearest data points from each class. - -#### Example Code - -=== "Python" - ``` py title="svm.py" linenums="1" - #!/usr/bin/env python - - # Load the required libraries - import pandas as pd - from sklearn.model_selection import train_test_split - from sklearn.svm import SVC - from sklearn.metrics import accuracy_score - - # Load the dataset - data = pd.read_csv('path/to/dataset.csv') - - # Split the data into training and testing sets - X_train, X_test, y_train, y_test = train_test_split(data.drop('target_variable', axis=1), data['target_variable'], test_size=0.2) - - # Train the SVM model - svm_model = SVC(kernel='linear', C=1.0, random_state=42) - svm_model.fit(X_train, y_train) - - # Make predictions on the testing set - y_pred = svm_model.predict(X_test) - - # Evaluate the model performance - accuracy = accuracy_score(y_test, y_pred) - print('Accuracy:', accuracy) - ``` - -=== "R" - - ``` r title="svm.r" linenums="1" - #!/usr/bin/env Rscript - - # Load the required libraries - library(e1071) - - # Load the dataset - data <- read.csv('path/to/dataset.csv') - - # Split the data into training and testing sets - set.seed(123) - train_index <- sample(1:nrow(data), size=0.8*nrow(data)) - train_data <- data[train_index,] - test_data <- data[-train_index,] - - # Train the SVM model - svm_model <- svm(target_variable ~ ., data=train_data, kernel='linear', cost=1) - - # Make predictions on the testing set - y_pred <- predict(svm_model, newdata=test_data[,-which(names(test_data)=='target_variable')]) - - # Evaluate the model performance - accuracy <- mean(y_pred == test_data$target_variable) - print(paste('Accuracy:', accuracy)) - ``` - -### Random Forest - -![Random Forest](../images/random-forest.jpg) - - -!!! note "Random Forest" - $$ - \hat{y} = \frac{1}{T} \sum_{t=1}^{T} f_t(\mathbf{x}), - $$ - - _where $\hat{y}$ is the predicted output, $f_t(\mathbf{x})$ is the prediction of the $t$th tree in the forest for the input $\mathbf{x}$, and $T$ is the number of trees in the forest._ - -Random Forests are an ensemble learning method that can be used for classification and regression problems. They are often used for their ability to handle high-dimContinuous Improvement:ensional datasets and nonlinear relationships between features and targets. - -Each tree is trained on a bootstrapped subset of the original training data, and at each split, a random subset of features is considered for determining the split. The final prediction is obtained by averaging the predictions of all the trees in the forest. - -#### Example Code - -=== "Python" - ``` py title="random_forest.py" linenums="1" - #!/usr/bin/env python - - # Load the required libraries - import pandas as pd - from sklearn.model_selection import train_test_split - from sklearn.ensemble import RandomForestRegressor - from sklearn.metrics import mean_squared_error - - # Load the dataset - data = pd.read_csv('path/to/dataset.csv') - - # Split the data into training and testing sets - X_train, X_test, y_train, y_test = train_test_split(data.drop('target_variable', axis=1), data['target_variable'], test_size=0.2) - - # Train the random forest model - rf_model = RandomForestRegressor(n_estimators=100, random_state=42) - rf_model.fit(X_train, y_train) - - # Make predictions on the testing set - y_pred = rf_model.predict(X_test) - - # Evaluate the model performance - mse = mean_squared_error(y_test, y_pred) - rmse = mse ** 0.5 - print('Root Mean Squared Error:', rmse) - ``` - -=== "R" - ``` r title="random_forest.r" linenums="1" - #!/usr/bin/env Rscript - - # Load the required libraries - library(randomForest) - - # Load the dataset - data <- read.csv('path/to/dataset.csv') - - # Split the data into training and testing sets - set.seed(123) - train_index <- sample(1:nrow(data), size=0.8*nrow(data)) - train_data <- data[train_index,] - test_data <- data[-train_index,] - - # Train the random forest model - rf_model <- randomForest(target_variable ~ ., data=train_data, ntree=100, importance=TRUE) - - # Make predictions on the testing set - y_pred <- predict(rf_model, newdata=test_data[,-which(names(test_data)=='target_variable')]) - - # Evaluate the model performance - mse <- mean((y_pred - test_data$target_variable)^2) - rmse <- sqrt(mse) - print(paste('Root Mean Squared Error:', rmse)) - ``` - -### Deep Learning - -![Deep Learning](../images/deep-learning.jpg) - - -!!! note "Deep Learning" - $$ - \hat{y} = f(\mathbf{W}_L f(\mathbf{W}_{L-1} f(\dots f(\mathbf{W}_1\mathbf{x}+\mathbf{b}_1)\dots)+\mathbf{b}_{L-1})+\mathbf{b}_L) - $$ - - _where $\mathbf{x}$ is the input vector, $\mathbf{W}_i$ and $\mathbf{b}_i$ are the weight matrix and bias vector, respectively, for the $i$th layer, and $f$ is the activation function._ - - _This formula represents a feedforward neural network with $L$ layers, where each layer applies a linear transformation to the output of the previous layer, followed by a non-linear activation function. The output of the final layer, $\hat{y}$, represents the predicted output of the neural network for the given input $\mathbf{x}$._ - -Deep learning is a subset of machine learning that involves training neural networks with many layers of interconnected nodes. This approach can handle large and complex datasets and is used in a wide range of applications, including image recognition, natural language processing, and speech recognition. The training process involves feeding the neural network a large dataset and adjusting the weights of the connections between the nodes to minimize the error between the predicted outputs and the actual outputs. Through repeated iterations, the neural network can learn to recognize patterns in the data and make accurate predictions on new data. - -#### Example Code - -=== "Python" - ``` py title="deep_learning.py" linenums="1" - #!/usr/bin/env python - - # Load the required libraries - import pandas as pd - import numpy as np - import tensorflow as tf - from tensorflow import keras - from sklearn.model_selection import train_test_split - from sklearn.preprocessing import StandardScaler - from sklearn.metrics import accuracy_score - - # Load the dataset - data = pd.read_csv('path/to/dataset.csv') - - # Split the data into training and testing sets - X_train, X_test, y_train, y_test = train_test_split(data.drop('target_variable', axis=1), data['target_variable'], test_size=0.2) - - # Standardize the input features - scaler = StandardScaler() - X_train_scaled = scaler.fit_transform(X_train) - X_test_scaled = scaler.transform(X_test) - - # Define the deep learning model - model = keras.Sequential([ - keras.layers.Dense(64, activation='relu', input_shape=[X_train_scaled.shape[1]]), - keras.layers.Dropout(0.2), - keras.layers.Dense(1, activation='sigmoid') - ]) - - # Compile the model - model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) - - # Train the model - history = model.fit(X_train_scaled, y_train, epochs=50, batch_size=32, validation_split=0.1) - - # Evaluate the model performance - y_pred = model.predict_classes(X_test_scaled) - accuracy = accuracy_score(y_test, y_pred) - print('Accuracy:', accuracy) - ``` - -=== "R" - - ``` r title="deep_learning.r" linenums="1" - #!/usr/bin/env Rscript - - # Load the required libraries - library(keras) - library(tensorflow) - - # Load the dataset - data <- iris - x <- as.matrix(data[, 1:4]) - y <- to_categorical(as.numeric(data[, 5]) - 1) - - # Split the data into training and testing sets - set.seed(123) - train_index <- sample(1:nrow(data), size=0.8*nrow(data)) - x_train <- x[train_index,] - y_train <- y[train_index,] - x_test <- x[-train_index,] - y_test <- y[-train_index,] - - # Define the neural network architecture - model <- keras_model_sequential() %>% - layer_dense(units = 8, input_shape = c(4)) %>% - layer_activation('relu') %>% - layer_dense(units = 3) %>% - layer_activation('softmax') - - # Compile the model - model %>% compile( - loss = 'categorical_crossentropy', - optimizer = 'adam', - metrics = c('accuracy') - ) - - # Train the model - history <- model %>% fit( - x_train, y_train, - epochs = 50, - batch_size = 10, - validation_split = 0.2, - verbose = 0 - ) - - # Evaluate the model performance - metrics <- model %>% evaluate(x_test, y_test) - print(paste('Test Loss:', metrics[1])) - print(paste('Test Accuracy:', metrics[2])) - - # Plot the training and validation accuracy over time - plot(history$metrics$accuracy, type='l', col='blue', ylim=c(0,1), ylab='Accuracy', xlab='Epoch') - lines(history$metrics$val_accuracy, type='l', col='red') - legend('bottomright', legend=c('Training', 'Validation'), col=c('blue', 'red'), lty=1) - ``` diff --git a/docs/fr/7-MLOps/Modele-ML-stockage.md b/docs/fr/7-MLOps/Modele-ML-stockage.md new file mode 100644 index 000000000..7df636e3c --- /dev/null +++ b/docs/fr/7-MLOps/Modele-ML-stockage.md @@ -0,0 +1,52 @@ +# Données B protégées par Statcan + + +!!! info "Protégé B" + L'ETAA est certifié pour l'hébergement de données Protégé B ! + +Afin de télécharger des données Protégé B vers l'ETAA, les utilisateurs devront demander l'accès via la Customer Success Unit (CSU). Les utilisateurs d'ETAA devront également fournir un espace de noms, trouver un sponsor et obtenir l'approbation d'OPMIC. Une fois le processus approuvé, notre équipe Fair Data Infrastructure (FDI) créera ensuite un dossier sur Net A qui à son tour donnera accès aux utilisateurs via l'annuaire actif. Les données pourront ensuite être transférées de Net A vers le nuage ETAA + +Le stockage des modèles d'apprentissage automatique dans un environnement de stockage cloud protégé est essentiel pour garantir la sécurité et la confidentialité des données sensibles. L'espace de travail d'analyse avancée (ETAA) fournit un environnement de nuage stockage sécurisé et robuste qui peut être utilisé pour stocker des modèles d'apprentissage automatique et d'autres actifs de données. + +La plate-forme ETAA fournit un environnement de nuage stockage protégé conçu pour répondre aux exigences les plus strictes en matière de sécurité et de confidentialité des données. L'environnement de stockage est protégé par un cryptage et des contrôles d'accès conformes aux normes de l'industrie, ce qui garantit que seul le personnel autorisé peut accéder aux données sensibles. Cela protège contre les accès non autorisés, les violations de données et autres menaces de sécurité. + +En plus de ses fonctionnalités de sécurité robustes, l'environnement de nuage stockage ETAA est également hautement évolutif et flexible. Cela signifie que les scientifiques des données et les ingénieurs en apprentissage automatique peuvent facilement faire évoluer leurs besoins de stockage à mesure que leurs ensembles de données et la taille de leurs modèles augmentent. Cela leur permet de stocker et de gérer de gros volumes de données et de modèles sans avoir à se soucier des limitations de stockage ou des goulots d'étranglement des performances. + +Le stockage des modèles d'apprentissage automatique dans un environnement de stockage en nuage protégé sur l'espace de travail d'analyse avancée fournit une solution sécurisée, évolutive et flexible pour gérer et protéger les données sensibles. En tirant parti des capacités de nuage stockage fournies par la plateforme ETAA, les scientifiques des données et les ingénieurs en apprentissage automatique peuvent se concentrer sur la création et le déploiement de leurs modèles en toute confiance, sachant que leurs données sont protégées et sécurisées. + +## Stockage en ligne + + +!!! info "Avantages du nuage de stockage" + Le stockage en nuage offre plusieurs avantages pour la science des données et l'apprentissage automatique, notamment en termes d'évolutivité, d'accessibilité et de rentabilité. + +Premièrement, le stockage dans le nuage permet aux scientifiques des données de stocker et de traiter de grandes quantités de données sans avoir à se soucier des limites du stockage local. Ceci est particulièrement important dans le contexte de l’apprentissage automatique, où de grands ensembles de données sont nécessaires pour la formation et le test des modèles. Le stockage dans le nuage permet aux scientifiques des données d'augmenter leur capacité de stockage selon leurs besoins, sans avoir à investir dans du matériel coûteux. + +Deuxièmement, le stockage dans le nuage permet aux scientifiques des données d'accéder aux données de n'importe où, en utilisant n'importe quel appareil doté d'une connexion Internet. Cela permet la collaboration entre des équipes géographiquement réparties et permet aux scientifiques des données de travailler à distance. De plus, le stockage dans le nuage facilite le partage de données avec d'autres parties prenantes, telles que des partenaires commerciaux ou des clients. Enfin, le stockage dans le nuage est généralement plus rentable que le stockage sur site, en particulier pour les petites organisations ou celles disposant de ressources informatiques limitées. + +Dans l’ensemble, le stockage dans le nuage est une solution fiable et pratique pour stocker et gérer vos données. Que vous ayez besoin de stocker de grandes quantités de données ou seulement quelques fichiers, le stockage dans le cloud facilite la gestion de vos besoins de stockage sans les tracas des solutions de stockage traditionnelles. + +La plateforme ETAA propose plusieurs types de stockage : + +- Disques (également appelés Volumes sur l'écran de création de Kubeflow Serveur Bloc-notes) +- Lacs de donnees (à venir) + +Selon votre cas d'utilisation, le disque ou le compartiment peuvent être les plus adaptés. Notre [aperçu du stockage](../5-Stockage/Aperçu.md) vous aidera à les comparer. + +### Disques +
+ [![Disques](../images/Disque.PNG)](../5-Stockage/Disque.md) +
+**[Disques](../5-Stockage/Disque.md)** sont ajoutés à votre serveur bloc-notes en augmentant les volumes de données. + +### Lacs de données (à venir) + +Un lac de données est un référentiel central qui vous permet de stocker toutes vos données structurées et non structurées à n'importe quelle échelle. C'est un moyen rentable de stocker et de gérer tous les types de données, des données brutes aux données traitées, et c'est un outil essentiel pour les scientifiques des données. + +L’un des principaux avantages d’un lac de données est sa flexibilité. Il permet de stocker tous types de données sans avoir besoin de définir un schéma au préalable, ce qui est particulièrement utile lorsqu'il s'agit de données non structurées. Cette flexibilité permet aux scientifiques des données d'explorer, d'expérimenter et d'extraire facilement des informations à partir de leurs données sans être contraints par les limites d'une base de données relationnelle traditionnelle. + +Les lacs de données permettent également aux scientifiques des données de centraliser leurs données, facilitant ainsi la gestion et l'analyse de gros volumes de données provenant de diverses sources. Avec un lac de données, les scientifiques des données peuvent facilement ingérer et stocker des données provenant de diverses sources, telles que des bases de données, le stockage cloud et des API tierces. De plus, les lacs de données fournissent souvent des fonctionnalités de gouvernance des données, de gestion des métadonnées et de contrôle d'accès, ce qui permet de garantir que les données sont de haute qualité et conformes aux réglementations en vigueur. + +De plus, les lacs de données basés sur le cloud offrent des solutions de stockage évolutives et rentables qui peuvent être facilement étendues d'un simple clic. À mesure que les besoins de stockage des données des scientifiques des données augmentent, ils peuvent ajouter une capacité de stockage supplémentaire à leur lac de données avec un minimum d'effort, sans se soucier de l'infrastructure ou de la maintenance sous-jacente. + +Dans l'ensemble, les lacs de données sont un outil essentiel pour les scientifiques des données, car ils offrent la flexibilité, l'évolutivité et la facilité d'utilisation nécessaires pour stocker et gérer de grands volumes de données, permettant aux data scientists de se concentrer sur l'extraction d'informations et de valeur à partir des données. \ No newline at end of file diff --git a/docs/fr/7-MLOps/Modele-Ml-entrainement.md b/docs/fr/7-MLOps/Modele-Ml-entrainement.md new file mode 100644 index 000000000..defc5bf89 --- /dev/null +++ b/docs/fr/7-MLOps/Modele-Ml-entrainement.md @@ -0,0 +1,419 @@ +# Formation de modèles d'apprentissage automatique sur l'ETAA + +
+![Robots au travail](../images/mlops.jpg) +
+ + +!!! info + La formation de modèles d'apprentissage automatique implique l'utilisation d'algorithmes pour apprendre des modèles et des relations dans les données. Ce processus implique d'identifier les caractéristiques ou les variables pertinentes pour le problème en question et d'utiliser ces caractéristiques pour effectuer des prédictions ou des classifications. + +## Pourquoi s'entraîner avec nous ? + +_La formation de modèles d'apprentissage automatique sur Advanced Analytics Workspace (AAW) présente plusieurs avantages._ + +1. **Code Logiciel Ouvert :** L'ETAA est une *plate-forme de données open source hébergée par Statistique Canada* qui offre un accès sécurisé (Protégé B) à une variété de sources de données, y compris les données de recensement, les enquêtes et les dossiers administratifs. Ces données peuvent être utilisées pour former des modèles d'apprentissage automatique et générer des informations susceptibles d'éclairer les décisions politiques et d'améliorer les processus métier. + +2. **Polyvalent :** L'ETAA est *conçu pour gérer des ensembles de données volumineux et complexes*. Il donne accès à une gamme d'outils d'analyse avancés, dans le langage de votre choix, notamment *Python, R et SAS*, qui peuvent être utilisés pour prétraiter les données, former des modèles d'apprentissage automatique et générer des visualisations. *Étant donné que l'ETAA exploite les technologies cloud, *les utilisateurs peuvent augmenter leur puissance de calcul selon leurs besoins*. +* +3. **Sécurisé :** L'ETAA est une *plateforme sécurisée (Protégée B) qui adhère aux normes les plus élevées en matière de confidentialité et de sécurité des données*. Les données peuvent être stockées et traitées sur la plateforme sans risque d'accès non autorisé ou de violation de données. + +## MLOps et pipelines de données + + +!!! info "Optimiser les flux de données" + Les MLOps et les pipelines de données sont des outils importants utilisés dans le domaine de la science des données pour gérer et optimiser les flux de travail de données. + +### MLOps + +MLOps fait référence à l'ensemble de pratiques et d'outils utilisés pour gérer l'ensemble du cycle de vie d'un modèle d'apprentissage automatique. Cela comprend tout, depuis le développement et la formation du modèle jusqu'à son déploiement en production et sa maintenance au fil du temps. MLOps garantit que les modèles d'apprentissage automatique sont fiables, précis et évolutifs, et qu'ils peuvent être mis à jour et améliorés selon les besoins. + +### Pipelines de données + +Les pipelines de données sont une série d'étapes qui permettent de déplacer les données d'un système ou d'une application à un autre. Cela inclut la collecte, le nettoyage, la transformation et le stockage des données, ainsi que leur récupération en cas de besoin. Les pipelines de données sont importants pour garantir que les données sont exactes, fiables et accessibles à ceux qui en ont besoin. + + +!!! info "Automatisation et fiabilité" + Les MLOps et les pipelines de données aident les organisations à gérer le processus complexe consistant à travailler avec de grandes quantités de données et à développer des modèles d'apprentissage automatique. En automatisant ces processus et en garantissant que les données sont exactes et fiables, les organisations peuvent économiser du temps et des ressources tout en prenant de meilleures décisions basées sur des informations basées sur les données. + +### Pourquoi des MLOps conteneurisés ? + +Les avantages de l'utilisation d'une approche conteneurisée pour la formation de modèles d'apprentissage automatique avec flux de travail Argo incluent : + +1. **Reproductibilité :** La conteneurisation du modèle d'apprentissage automatique et de ses dépendances garantit que l'environnement reste cohérent d'une exécution à l'autre, ce qui facilite la reproduction des résultats. + +2. **Évolutivité :** Les flux de travail Argo peut orchestrer des tâches parallèles et des flux de travail complexes, ce qui facilite l'évolution du processus de formation selon les besoins. + +3. **Portabilité :** Les conteneurs peuvent être exécutés sur n'importe quelle plate-forme prenant en charge la conteneurisation, ce qui facilite le déplacement du processus de formation vers différents environnements ou fournisseurs de cloud. + +4. **Collaboration :** En transférant le conteneur vers un registre de conteneurs, d'autres utilisateurs peuvent facilement télécharger et utiliser le conteneur à leurs propres fins, ce qui facilite la collaboration sur des projets d'apprentissage automatique. + +Lesflux de travail Argo et la conteneurisation offrent une approche puissante et flexible pour la formation de modèles d'apprentissage automatique. En tirant parti de ces outils, les scientifiques des données et les ingénieurs en apprentissage automatique peuvent créer, déployer et gérer des flux de travail d'apprentissage automatique avec facilité et reproductibilité. + +## Comment former des modèles + +Il existe de nombreuses façons de former des modèles d’apprentissage automatique et ce n’est pas à nous de dire à qui que ce soit comment procéder. Cela étant dit, nous avons fourni ci-dessous quelques guides sur la façon de former des modèles d'apprentissage automatique à l'aide des outils disponibles sur l'ETAA. Le premier tutoriel concerne la formation d'un modèle simple directement dans un notebook JupyterLab. Le deuxième didacticiel suppose que l'utilisateur est plus avancé et souhaite définir un pipeline MLOps pour la formation de modèles à l'aide des flux de travail Argo. + +### Créer un serveur de notebook sur l'ETAA + + +!!! info "Serveurs bloc-notes" + Que vous envisagiez de travailler dans JupyterLab, R Studio ou quelque chose de plus avancé avec les flux de travail Argo, vous aurez besoin du serveur bloc-notes approprié. [Suivez les instructions trouvées ici pour commencer.](https://docs.google.com/presentation/d/12yTDlbMCmbg0ccdea2h0vwhs5YTa_GHm_3DieG5A-k8/edit?usp=sharing) + +### Utiliser JupyterLab + +![JupyterLab](../images/jupyterlab.jpg) + + +!!! info "JupyterLab est populaire" + La formation de modèles d'apprentissage automatique avec JupyterLab est une approche populaire parmi les scientifiques des données et les ingénieurs en apprentissage automatique. + +Vous trouverez ici les étapes nécessaires pour entraîner un modèle d'apprentissage automatique avec JupyterLab sur AAW. Parce que nous sommes un environnement multilingue, nous avons fait de notre mieux pour fournir des exemples de code dans nos langages les plus populaires, `Python`, `R` et `SAS`. + +#### 1. Importez les bibliothèques requises + +Une fois qu'une session JupyterLab est en cours d'exécution, vous devez importer les bibliothèques requises pour votre modèle d'apprentissage automatique. Cela pourrait inclure des bibliothèques telles que `NumPy`, `Pandas`, `Scikit-learn`, `Tensorflow` ou `PyTorch`. Si vous utilisez `R`, vous aurez besoin de `tidyverse`, `caret` et `janitor`. + +=== "Python" + ``` py title="libraries.py" linenums="1" + #!/usr/bin/env python + + # tensorflow et keras pour la création et la formation de modèles d'apprentissage profond + import tensorflow as tf + from tensorflow import keras + + # numpy pour les calculs numériques + import numpy as np + + # pandas pour la manipulation et l'analyse des données + import pandas as pd + + # matplotlib pour la visualisation de données + import matplotlib.pyplot as plt + ``` +=== "R" + ``` r title="libraries.R" linenums="1" + #!/usr/bin/env Rscript + + # tidyverse pour des outils impressionnants d'analyse de données et de munging + library(tidyverse) + + # janitor pour nettoyer vos données + library(janitor) + + # caret pour un apprentissage automatique facile + library(caret) + ``` +=== "SASPy" + ``` py title="libraries.py" linenums="1" + #!/usr/bin/env python + + # la seule bibliothèque dont vous aurez besoin pour accéder à SAS depuis Python + import saspy + ``` +=== "SAS" + ``` sas title="libraries.sas" linenums="1" + + ``` + + +!!! note "À propos du code" + Les exemples de code que vous voyez dans ce document et dans toute la documentation sont fournis à des fins d'illustration pour vous aider à démarrer vos projets. En fonction de la tâche ou du projet spécifique, d'autres bibliothèques et étapes peuvent être nécessaires. + +#### 2. Charger et prétraiter les données + +Ensuite, vous devez charger et prétraiter les données que vous utiliserez pour entraîner votre modèle d'apprentissage automatique. Cela pourrait inclure le nettoyage des données, l’extraction de fonctionnalités et la normalisation. Les étapes exactes de prétraitement que vous devrez effectuer dépendront de l'ensemble de données spécifique avec lequel vous travaillez, des exigences de votre modèle d'apprentissage automatique et du travail à effectuer. + +=== "Python" + ``` py title="load_data.py" linenums="1" + #!/usr/bin/env python + + # Importer les packages nécessaires + import pandas as pd + from sklearn.preprocessing import StandardScaler + + # Charger des données à partir d'un fichier CSV + data = pd.read_csv('data.csv') + + # Nettoyage des données ! On peut faire beaucoup plus, c'est fondamental + data = data.dropna() # Drop rows with missing values + data = data.drop_duplicates() # Drop duplicate rows + + # Extraction de caractéristiques + X = data[['feature1', 'feature2', 'feature3']] # Select relevant features + + # Normalisation + scaler = StandardScaler() # Create a scaler object + X_norm = scaler.fit_transform(X) # Normalize the feature values + ``` +=== "R" + ``` r title="load_data.R" linenums="1" + #!/usr/bin/env Rscript + + # Importer les packages nécessaires + library(tidyverse) + library(janitor) + + # Charger des données à partir d'un fichier CSV + data <- read_csv("data.csv") + + # Nettoyer les données en utilisant janitor + data_cleaned <- data %>% + + # Supprimer les espaces de début/fin dans les noms de colonnes + clean_names() %>% + + # Supprimer les lignes avec des valeurs manquantes + remove_empty() %>% + + # Convertir la colonne de date au format Date + mutate(date = as.Date(date, format = "%m/%d/%Y")) %>% + + # Supprimer les lignes en double + distinct() %>% + + # Réorganiser les colonnes + select(date, column2, column1, column3) + ``` +=== "SASPy" + ``` py title="load_data.py" linenums="1" + #!/usr/bin/env python + + # Importer les packages nécessaires + import saspy + + # Démarrez une session SAS et vérifiez les informations de configuration + sas = saspy.SASsession(cfgname='default') + + # Charger des données à partir d'un fichier CSV + data = sas.read_csv("./data.csv") + ``` +=== "SAS" + ``` sas title="load_data.sas" linenums="1" + /* Lecture d'un fichier délimité par des virgules avec une extension .csv */ + /* */ + /* Puisque la valeur DBMS= est CSV, vous n'avez pas besoin d'utiliser DELIMITER= */ + /* déclaration. Par défaut, on suppose que les noms des variables sont en premier */ + /* ligne, donc l'instruction GETNAMES= n'est pas requise. */ + /* */ + /* Créez un fichier de test délimité par des virgules à lire en utilisant PROC IMPORT ci-dessous. */ + + /* Charger les données depuis un fichier CSV */ + proc import + datafile='data.csv' + out=data + dbms=csv + replace; + run; + + /* Afficher les données */ + proc print; + run; + ``` + +#### 3. Divisez les données en ensembles de formation et de test + + Une fois les données prétraitées, vous devez les diviser en ensembles de formation et de test. L'ensemble de formation sera utilisé pour entraîner le modèle d'apprentissage automatique, tandis que l'ensemble de test sera utilisé pour évaluer ses performances. + +=== "Python" + ``` py title="train_test.py" linenums="1" + #!/usr/bin/env python + + # Importer les packages nécessaires + import pandas as pd + from sklearn.model_selection import train_test_split + + # Divisez les données en ensembles de formation et de test + X_train, X_test, y_train, y_test = train_test_split(X_norm, + data['target'], test_size=0.2, random_state=42) + ``` +=== "R" + ``` r title="train_test.R" linenums="1" + #!/usr/bin/env Rscript + + # Importer les packages nécessaires + library(caret) + + # Charger l'ensemble de données + data <- read.csv("my-dataset.csv") + + # Définir la graine pour la reproductibilité + set.seed(123) + + # Divisez l'ensemble de données en train et test à l'aide de la fonction createDataPartition de Caret + train_index <- createDataPartition(data$target_variable, p = 0.7, list = FALSE) + train_data <- data[train_index, ] + test_data <- data[-train_index, ] + ``` +=== "SASPy" + ``` py title="train_test.py" linenums="1" + #!/usr/bin/env python + + ``` +=== "SAS" + ``` sas title="train_test.sas" linenums="1" + + ``` + + +!!! note + Nous divisons les données en ensembles d'entraînement et de test à l'aide de la fonction `train_test_split` de `scikit-learn`, qui divise aléatoirement les données en deux ensembles en fonction de la taille de test spécifiée et de la graine aléatoire. + +#### 4. Définir et entraîner le modèle d'apprentissage automatique + +Avec la répartition des données, vous pouvez désormais définir et entraîner votre modèle d'apprentissage automatique à l'aide de l'ensemble de formation. Cela pourrait impliquer la sélection de l’algorithme approprié, le réglage des hyperparamètres et la validation croisée. + +=== "Python" + ``` py title="train.py" linenums="1" + #!/usr/bin/env python + + # Importer les packages nécessaires + from sklearn.ensemble import RandomForestClassifier + from sklearn.model_selection import train_test_split + + # Charger l'ensemble de données + data = pd.read_csv("my-dataset.csv") + + # Divisez l'ensemble de données en entraînement et test + X_train, X_test, y_train, y_test = train_test_split(data.iloc[:, :-1], data.iloc[:, -1], test_size=0.3, random_state=123) + + # Entraîner le modèle + model = RandomForestClassifier(n_estimators=100, random_state=123) + model.fit(X_train, y_train) + + # Imprimer le score de précision sur les données de test + print("Accuracy on test set: {:.3f}".format(model.score(X_test, y_test))) + ``` +=== "R" + ``` r title="train.R" linenums="1" + #!/usr/bin/env Rscript + + # Importer les packages nécessaires + library(caret) + + # Charger l'ensemble de données + data <- read.csv("my-dataset.csv") + + # Définir la graine pour la reproductibilité + set.seed(123) + + # Divisez l'ensemble de données en train et test à l'aide de la fonction curseur createDataPartition + train_index <- createDataPartition(data$target_variable, p = 0.7, list = FALSE) + train_data <- data[train_index, ] + test_data <- data[-train_index, ] + + # Définir le contrôle de la formation + train_control <- trainControl(method = "cv", number = 5) + + # Entraînez le modèle à l'aide de la fonction d'entraînement de Caret (la méthode = "rf" est pour la forêt aléatoire) + model <- train(target_variable ~ ., data = train_data, method = "rf", trControl = train_control) + + # Imprimez l'objet modèle pour afficher les résultats + print(model) + ``` +=== "SASPy" + ``` py title="train.py" linenums="1" + #!/usr/bin/env python + + # Importer les packages nécessaires + import saspy + import pandas as pd + + # Établir une connexion à une session SAS + sas = saspy.SASsession() + + # Charger l'ensemble de données + data = pd.read_csv("my-dataset.csv") + + # Téléchargez l'ensemble de données sur SAS + sas_df = sas.df2sd(data, "mydata") + + # Divisez l'ensemble de données en train et test + train, test = sas.surveyselect(data=sas_df, + method="SRS", + seed=123, + samprate=0.7, + outall=True, + strata="target_variable", + partind=True) + + # Entraîner le modèle à l'aide de la procédure HPFOREST + model = sas.hpforest(data=train, + target="target_variable", + input="input_variable_1-input_variable_n", + partition="rolevar", + rolevars={"test": "0", "train": "1"}, + nominals=["input_variable_1-input_variable_n"], + forestopts={"ntree": 100, "seed": 123}) + + # Noter le modèle sur les données de test + predictions = model.predict(newdata=test, out=pred_out) + + # Calculer le score de précision sur les données de test + accuracy = sas.freq(data=predictions, tables="target_variable*p_target_variable", nocum=True, nocol=True) + + # Imprimer le score de précision + print("Accuracy on test set: {:.3f}".format(accuracy.Frequency.iloc[0, 1] / accuracy.Frequency.iloc[:, 1].sum())) + + # Se déconnecter de la session SAS + sas.disconnect() + ``` +=== "SAS" + ``` sas title="train.sas" linenums="1" + /* Charger l'ensemble de données */ + proc import datafile="my-dataset.csv" out=mydata dbms=csv replace; + run; + + /* Diviser l'ensemble de données en train et test */ + proc surveyselect data=mydata method=srs seed=123 out=selected outall + samprate=0.7; + strata target_variable; + run; + + /* Entraîner le modèle */ + proc hpforest data=selected; + class _all_; + target target_variable / level=nominal; + partition rolevar=target_variable(test="0" train="1"); + input _all_; + forest ntree=100 seed=123; + run; + + /* Noter le modèle sur les données de test */ + proc hpforest predict testdata=selected out=testout; + run; + + /* Imprimer le score de précision sur les données du test */ + proc freq data=testout; + tables target_variable*p_target_variable / nocum nocol; + run; + ``` + +#### 5. Évaluer le modèle + +Après avoir entraîné le modèle, vous devez évaluer ses performances sur l’ensemble de tests. Cela vous donnera une idée des performances du modèle sur de nouvelles données invisibles. + +#### 6. Déployer le modèle + +Enfin, vous pouvez déployer le modèle d'apprentissage automatique formé dans un environnement de production. + +### Utiliser les flux de travail Argo + +![Art de production de flux de travail](../images/argo-workflows-assembly-line.jpg) + + +!!! info "Meilleures pratiques MLOps" + Les flux de travail Argo sont un excellent outil pour tous ceux qui cherchent à mettre en œuvre des pratiques MLOps et à rationaliser le processus de formation et de déploiement de modèles d'apprentissage automatique ou d'autres tâches de science des données telles que ETL. + +**[Flux de travail Argo](https://argoproj.github.io/argo-workflows/)** est un moteur de flux de travail open source natif pour conteneurs permettant d'orchestrer des tâches parallèles sur Kubernetes. Les flux de travail Argo sont implémentés en tant que Kubernetes CRD (Custom Resource Definition). Il est particulièrement adapté aux flux de travail d’apprentissage automatique et de science des données. + +Les flux de travail Argo vous permettent de + +- Définir des flux de travail où chaque étape du flux est un conteneur. +- Modélisez des flux de travail en plusieurs étapes sous la forme d'une séquence de tâches ou capturez les dépendances entre les tâches à l'aide d'un graphe acyclique dirigé (DAG). +- Exécutez facilement des tâches de calcul intensives pour l'apprentissage automatique ou le traitement des données en une fraction du temps à l'aide de flux de travail Argo sur Kubernetes. +- Exécutez des pipelines CI/CD de manière native sur Kubernetes sans configurer de produits de développement logiciel complexes. + +ce qui facilite la gestion de l'ensemble du pipeline d'apprentissage automatique de bout en bout. Avec les flux de travail Argo, vous pouvez facilement créer des flux de travail qui intègrent des tâches telles que le prétraitement des données, la formation de modèles et le déploiement de modèles, le tout dans un environnement Kubernetes. + +Voir la [section flux de travail argo](../3-Pipelines/Argo.md) pour plus de détails. \ No newline at end of file diff --git a/docs/fr/7-MLOps/Modele-Service-ML.md b/docs/fr/7-MLOps/Modele-Service-ML.md new file mode 100644 index 000000000..bffcbc56d --- /dev/null +++ b/docs/fr/7-MLOps/Modele-Service-ML.md @@ -0,0 +1,39 @@ +# Introduction à la diffusion de modèles + +Dans le contexte des gouvernements, servir des modèles d’apprentissage automatique signifie rendre les modèles formés disponibles pour être utilisés par d’autres applications et systèmes. Cela pourrait inclure la réalisation de prédictions ou de classifications basées sur les données d'entrée, ou la fourniture d'informations et de recommandations basées sur les résultats de l'analyse des données. + +La mise en œuvre de modèles d'apprentissage automatique dans un contexte gouvernemental soulève des problèmes importants liés à la confidentialité des données. Les agences gouvernementales sont souvent chargées de collecter et de gérer des données personnelles sensibles, telles que les dossiers médicaux, les données financières et les casiers judiciaires. Lorsque vous servez des modèles d'apprentissage automatique, il est essentiel de garantir que ces données sont protégées et que leur accès est strictement contrôlé. + +Pour répondre à ces préoccupations, les agences gouvernementales doivent mettre en œuvre des mesures robustes de confidentialité et de sécurité des données lorsqu'elles servent des modèles d'apprentissage automatique. Cela pourrait inclure le chiffrement des données au repos et en transit, la mise en œuvre de contrôles d'accès et d'authentification des utilisateurs, ainsi qu'une surveillance régulière des violations de données et des vulnérabilités. + +Outre la confidentialité et la sécurité des données, il est également important de prendre en compte les implications éthiques de la mise en œuvre de modèles d'apprentissage automatique dans un contexte gouvernemental. Les modèles d’apprentissage automatique peuvent être biaisés ou discriminatoires, conduisant à un traitement injuste de certains groupes de personnes. Pour atténuer ces risques, les agences gouvernementales doivent évaluer et surveiller soigneusement leurs modèles d'apprentissage automatique et prendre des mesures pour remédier à tout préjugé ou discrimination qui pourrait survenir. + +Dans l’ensemble, la mise en œuvre de modèles d’apprentissage automatique dans un contexte gouvernemental nécessite un examen attentif de la confidentialité des données, de la sécurité et des préoccupations éthiques. En mettant en œuvre des mesures robustes pour protéger les données personnelles et prévenir les préjugés, les agences gouvernementales peuvent tirer parti de la puissance de l'apprentissage automatique pour prendre de meilleures décisions et améliorer les résultats pour les citoyens tout en préservant la confiance et la transparence. + +## Pourquoi servir avec nous ? + +Servir des modèles d'apprentissage automatique avec l'Espace de travail d'analyse avancée (ETAA) présente plusieurs avantages. Premièrement, ETAA est une plate-forme d'analyse de données open source qui donne accès à une variété d'outils d'analyse avancés, notamment Python, R et SAS. Cela facilite le déploiement de modèles d'apprentissage automatique et leur intégration dans les flux de travail existants. + +Deuxièmement, l'ETAA prend en charge plusieurs frameworks MLOps, notamment les workflows Couler, Seldon et Argo. Ces frameworks fournissent une gamme de fonctionnalités, notamment la gestion des versions de modèles, la diffusion de modèles et la surveillance de modèles, qui simplifient le processus de déploiement et de gestion des modèles d'apprentissage automatique en production. + +Troisièmement, l'ETAA fournit une plate-forme sécurisée et évolutive pour servir des modèles d'apprentissage automatique avec le statut Protégé B. Les modèles peuvent être servis à l'aide d'environnements conteneurisés, tels que Docker, qui offrent un haut niveau d'isolation et de sécurité. L'ETAA donne également accès aux ressources de cloud computing, permettant aux utilisateurs d'augmenter leur puissance de calcul selon leurs besoins pour gérer des volumes élevés de requêtes. + +Enfin, l'ETAA est une plateforme collaborative qui permet aux utilisateurs de partager du code et des données avec d'autres chercheurs et analystes. Cela favorise une communauté d'utilisateurs qui peuvent apprendre du travail de chacun et collaborer sur des projets nécessitant des capacités d'analyse avancées. + +En résumé, servir des modèles d'apprentissage automatique avec Advanced Analytics Workspace donne accès à des outils d'analyse avancés, à plusieurs frameworks MLOps, à une plate-forme Proteced B sécurisée et évolutive et à une communauté collaborative d'utilisateurs, ce qui en fait une plate-forme idéale pour les scientifiques et les analystes de données qui souhaitent pour déployer et gérer des modèles d’apprentissage automatique en production. + +## Noyau Seldon + +Seldon Core est une plate-forme open source permettant de déployer, de mettre à l'échelle et de surveiller des modèles d'apprentissage automatique sur Kubernetes. Il fournit un moyen simple et efficace de déployer des modèles d'apprentissage automatique sous forme de microservices dans un environnement de production. + +La diffusion de modèles d'apprentissage automatique à l'aide de Seldon Core implique les étapes suivantes : + +1. Conditionnement du modèle : la première étape consiste à empaqueter le modèle d'apprentissage automatique formé dans une image de conteneur avec toutes les dépendances requises. Seldon Core prend en charge divers frameworks d'apprentissage automatique, notamment TensorFlow, PyTorch et Scikit-learn. + +2. Déploiement du modèle : une fois l'image du conteneur créée, l'étape suivante consiste à déployer le modèle sur Kubernetes à l'aide de Seldon Core. Cela implique de définir le fichier de configuration de déploiement, qui spécifie les ressources requises pour le déploiement, telles que le nombre de réplicas et les ressources de calcul. + +3. Service de modèle : une fois le modèle déployé, Seldon Core expose un point de terminaison d'API REST qui peut être utilisé pour effectuer des prédictions. Les clients peuvent envoyer des requêtes au point de terminaison avec des données d'entrée, et le modèle renverra la sortie correspondante. Seldon Core prend également en charge divers modèles de déploiement, tels que le déploiement Canary et les tests A/B, pour permettre une expérimentation et un test faciles de différents modèles. + +4. Surveillance des modèles : Seldon Core fournit diverses capacités de surveillance pour suivre les performances des modèles déployés. Cela inclut la surveillance en temps réel des métriques du modèle, telles que la latence et le débit, ainsi que la journalisation des données de requête et de réponse à des fins de débogage. + +Seldon Core facilite la mise en œuvre de modèles d'apprentissage automatique à grande échelle, avec la prise en charge de la haute disponibilité, de l'évolutivité et de la tolérance aux pannes. Il fournit également une intégration avec divers outils natifs de Kubernetes, tels que Istio et Prometheus, pour permettre une surveillance et une observabilité avancées. \ No newline at end of file diff --git "a/docs/fr/7-MLOps/Mod\303\250les-d'apprentissage-automatique.md" "b/docs/fr/7-MLOps/Mod\303\250les-d'apprentissage-automatique.md" new file mode 100644 index 000000000..41f2ce51b --- /dev/null +++ "b/docs/fr/7-MLOps/Mod\303\250les-d'apprentissage-automatique.md" @@ -0,0 +1,367 @@ +# Modèles d'apprentissage automatique + +Les modèles d'apprentissage automatique sont des algorithmes informatiques conçus pour apprendre automatiquement des modèles et des relations à partir des données. Ces modèles sont formés sur un ensemble de données, qui est généralement une collection d'exemples ou d'instances, chacun étant constitué d'un ensemble de fonctionnalités ou de variables, ainsi que d'une variable ou d'une sortie cible. + +L’objectif d’un modèle d’apprentissage automatique est d’identifier des modèles et des relations au sein des données qui peuvent être utilisés pour faire des prédictions ou prendre des décisions concernant de nouvelles données invisibles. Cela implique de développer une représentation mathématique de la relation entre les caractéristiques d'entrée et la variable de sortie, basée sur les modèles observés dans les données d'entraînement. Une fois le modèle entraîné, il peut être utilisé pour faire des prédictions ou prendre des décisions sur de nouvelles données invisibles. + +Il existe plusieurs types de modèles d'apprentissage automatique, chacun étant conçu pour résoudre différents types de problèmes ou de données. Certains des types de modèles d’apprentissage automatique les plus courants incluent : + +1. **Modèles de régression :** Les modèles de régression sont utilisés pour prédire des valeurs numériques continues, telles que les cours des actions ou les prix de l'immobilier. + +2. **Modèles de classification :** Les modèles de classification sont utilisés pour prédire des valeurs catégorielles discrètes, par exemple si un client achètera un produit ou non. + +3. **Modèles de grappe :** Les modèles de grappe sont utilisés pour identifier des groupes ou des clusters au sein d'un ensemble de données en fonction des similitudes entre les instances. + +4. **Modèles de recommandation :** Les modèles de recommandation sont utilisés pour recommander des produits ou des services aux utilisateurs en fonction de leur comportement ou de leurs préférences passées. + +5. **Réseaux de neurones :** Les réseaux de neurones sont un type de modèle d'apprentissage automatique conçu pour imiter la structure et le fonctionnement du cerveau humain. Ils sont couramment utilisés dans les applications de reconnaissance d’images, de reconnaissance vocale et de traitement du langage naturel. + + +!!! info "Les modèles d'apprentissage automatique peuvent être biaisés" + Les modèles d'apprentissage automatique sont un outil puissant pour analyser et faire des prédictions sur les données, et ils ont un large éventail d'applications dans des domaines tels que la finance, la santé, le marketing, etc. Cependant, il est important de noter que les modèles d’apprentissage automatique ne sont pas parfaits et peuvent parfois commettre des erreurs ou produire des résultats biaisés. Par conséquent, il est important d’évaluer et de tester soigneusement les modèles d’apprentissage automatique avant de les utiliser dans des applications réelles. + +## Exemples + +### Régression linéaire + +![Régression linéaire](../images/linear-regression.jpg) + + +!!! info "Régression linéaire" + $$ + \hat{Y}_i = \hat{\beta}_0 + \hat{\beta}_1 X_i + \hat{\epsilon}_i + $$ + + _Où $\hat{Y}_i$ désigne le $i$ième estimateur de la vraie valeur $Y$ basé sur la $i$ième époque d'entraînement. Chaque $\hat{\beta}$ est un paramètre à apprendre. $\hat{\epsilon}_i$ est la quantité de bruit autorisée dans le modèle et peut varier en fonction du numéro d'époque d'entraînement indiqué par $i$. Chaque $X_i$ représente le $i$ème lot de données d'entraînement._ + +Dans les modèles statistiques classiques comme la régression linéaire, l’objectif est de trouver une droite qui correspond le mieux aux données, nous permettant ainsi de faire des prédictions sur de nouveaux points de données. + +À mesure que la complexité du problème augmente, des algorithmes plus sophistiqués sont nécessaires, tels que des arbres de décision, des machines à vecteurs de support et des forêts aléatoires. Cependant, ces méthodes ont leurs limites et peuvent ne pas être en mesure de capturer des modèles complexes dans de grands ensembles de données. + +#### Exemple de code + +=== "Python" + ``` py title="linear_regression.py" linenums="1" + #!/usr/bin/env python + + # Charger les bibliothèques requises + import pandas as pd + from sklearn.model_selection import train_test_split + from sklearn.linear_model import LinearRegression + from sklearn.metrics import mean_squared_error + + # Charger l'ensemble de données + data = pd.read_csv('path/to/dataset.csv') + + # Divisez les données en ensembles de formation et de test + X_train, X_test, y_train, y_test = train_test_split(data.drop('target_variable', axis=1), data['target_variable'], test_size=0.2) + + # Entraîner le modèle de régression linéaire + linear_model = LinearRegression() + linear_model.fit(X_train, y_train) + + # Faire des prédictions sur l'ensemble de tests + y_pred = linear_model.predict(X_test) + + # Évaluer les performances du modèle + mse = mean_squared_error(y_test, y_pred) + rmse = mse ** 0.5 + print('Root Mean Squared Error:', rmse) + ``` + +=== "R" + ``` r title="linear_regression.r" linenums="1" + #!/usr/bin/env Rscript + + # Définir une graine aléatoire pour la reproductibilité + set.seed(123) + + # Charger l'ensemble de données + data <- read.csv('path/to/dataset.csv') + + # Divisez les données en ensembles de formation et de test + train_index <- sample(1:nrow(data), size=0.8*nrow(data)) + train_data <- data[train_index,] + test_data <- data[-train_index,] + + # Entraîner le modèle de régression linéaire + lm_model <- lm(target_variable ~ ., data=train_data) + + # Faire des prédictions sur l'ensemble de tests + y_pred <- predict(lm_model, newdata=test_data[,-which(names(test_data)=='target_variable')]) + + # Évaluer les performances du modèle + mse <- mean((y_pred - test_data$target_variable)^2) + rmse <- sqrt(mse) + print(paste('Root Mean Squared Error:', rmse)) + ``` + +### Machine à vecteurs de support (SVM) + +![Machine à vecteurs de support](../images/svm.jpg) + + +!!! note "SVM" + $$ + \underset{\mathbf{w},b,\boldsymbol{\xi}}{\operatorname{minimiser}} \hspace{0.2cm} \frac{1}{2} ||\mathbf{w}||^2 + C \sum_{i=1}^{N} \xi_i + $$ + + $$ + \text{où} \hspace{0.2cm} y_i(\mathbf{w}^T\mathbf{x}_i + b) \geq 1-\xi_i \quad \text{et} \quad \hspace{0.2cm} \xi_i \geq 0 \hspace{0.2cm} \forall i \in {1,2,...,N} + $$ + + _Dans cette formule, nous utilisons la formulation SVM standard où $\mathbf{w}$ est le vecteur de poids, $b$ est le terme de biais et $\boldsymbol{\xi}$ est le vecteur variable de relâchement. L'objectif est de minimiser la norme L2 du vecteur de poids $\mathbf{w}$, sous la contrainte que tous les exemples d'entraînement soient classés correctement avec une marge d'au moins 1, plus une tolérance pour certaines violations de marge contrôlées par le paramètre de régularisation $C$. La variable cible $y_i$ prend les valeurs de 1 ou -1, représentant les deux classes du problème de classification binaire, et $\mathbf{x}_i$ est le vecteur de caractéristiques du $i$ème exemple de formation._ + +Une machine à vecteurs de support (SVM) est un algorithme d'apprentissage automatique supervisé qui peut être utilisé pour la classification, la régression et la détection des valeurs aberrantes. Il s’agit d’un algorithme populaire dans le domaine de l’apprentissage automatique, notamment pour résoudre des problèmes de classification. + +L'idée de base derrière SVM est de trouver un hyperplan qui sépare au mieux les données d'entrée en différentes classes. Dans un problème de classification à deux classes, l'hyperplan est une ligne qui divise les points de données d'une classe des points de données de l'autre classe. SVM essaie de trouver l'hyperplan qui maximise la marge entre les deux classes, la marge étant la distance entre l'hyperplan et les points de données les plus proches de chaque classe. + +#### Exemple de code + +=== "Python" + ``` py title="svm.py" linenums="1" + #!/usr/bin/env python + + # Charger les bibliothèques requises + import pandas as pd + from sklearn.model_selection import train_test_split + from sklearn.svm import SVC + from sklearn.metrics import accuracy_score + + # Charger l'ensemble de données + data = pd.read_csv('path/to/dataset.csv') + + # Divisez les données en ensembles de formation et de test + X_train, X_test, y_train, y_test = train_test_split(data.drop('target_variable', axis=1), data['target_variable'], test_size=0.2) + + # Entraîner le modèle SVM + svm_model = SVC(kernel='linear', C=1.0, random_state=42) + svm_model.fit(X_train, y_train) + + # Faire des prédictions sur l'ensemble de tests + y_pred = svm_model.predict(X_test) + + # Évaluer les performances du modèle + accuracy = accuracy_score(y_test, y_pred) + print('Accuracy:', accuracy) + ``` + +=== "R" + + ``` r title="svm.r" linenums="1" + #!/usr/bin/env Rscript + + # Charger les bibliothèques requises + library(e1071) + + # Charger l'ensemble de données + data <- read.csv('path/to/dataset.csv') + + # Divisez les données en ensembles de formation et de test + set.seed(123) + train_index <- sample(1:nrow(data), size=0.8*nrow(data)) + train_data <- data[train_index,] + test_data <- data[-train_index,] + + # Entraîner le modèle SVM + svm_model <- svm(target_variable ~ ., data=train_data, kernel='linear', cost=1) + + # Faire des prédictions sur l'ensemble de tests + y_pred <- predict(svm_model, newdata=test_data[,-which(names(test_data)=='target_variable')]) + + # Évaluer les performances du modèle + accuracy <- mean(y_pred == test_data$target_variable) + print(paste('Accuracy:', accuracy)) + ``` + +### Forêt aléatoire + +![Forêt aléatoire](../images/random-forest.jpg) + + +!!! note "Forêt aléatoire" + $$ + \hat{y} = \frac{1}{T} \sum_{t=1}^{T} f_t(\mathbf{x}), + $$ + + _où $\hat{y}$ est la sortie prédite, $f_t(\mathbf{x})$ est la prédiction du $t$ème arbre dans la forêt pour l'entrée $\mathbf{x}$ et $T $ est le nombre d'arbres dans la forêt._ + +Les forêts aléatoires sont une méthode d'apprentissage d'ensemble qui peut être utilisée pour des problèmes de classification et de régression. Ils sont souvent utilisés pour leur capacité à gérer des ensembles de données d'amélioration continue à forte intensité variable et des relations non linéaires entre les caractéristiques et les cibles. + +Chaque arbre est formé sur un sous-ensemble amorcé des données d'entraînement d'origine, et à chaque division, un sous-ensemble aléatoire de fonctionnalités est pris en compte pour déterminer la division. La prédiction finale est obtenue en faisant la moyenne des prédictions de tous les arbres de la forêt. + +#### Exemple de code + +=== "Python" + ``` py title="random_forest.py" linenums="1" + #!/usr/bin/env python + + # Charger les bibliothèques requises + import pandas as pd + from sklearn.model_selection import train_test_split + from sklearn.ensemble import RandomForestRegressor + from sklearn.metrics import mean_squared_error + + # Charger l'ensemble de données + data = pd.read_csv('path/to/dataset.csv') + + # Divisez les données en ensembles de formation et de test + X_train, X_test, y_train, y_test = train_test_split(data.drop('target_variable', axis=1), data['target_variable'], test_size=0.2) + + # Entraîner le modèle de forêt aléatoire + rf_model = RandomForestRegressor(n_estimators=100, random_state=42) + rf_model.fit(X_train, y_train) + + # Faire des prédictions sur l'ensemble de tests + y_pred = rf_model.predict(X_test) + + # Évaluer les performances du modèle + mse = mean_squared_error(y_test, y_pred) + rmse = mse ** 0.5 + print('Root Mean Squared Error:', rmse) + ``` + +=== "R" + ``` r title="random_forest.r" linenums="1" + #!/usr/bin/env Rscript + + # Charger les bibliothèques requises + library(randomForest) + + # Charger l'ensemble de données + data <- read.csv('path/to/dataset.csv') + + # Divisez les données en ensembles de formation et de test + set.seed(123) + train_index <- sample(1:nrow(data), size=0.8*nrow(data)) + train_data <- data[train_index,] + test_data <- data[-train_index,] + + # Entraîner le modèle de forêt aléatoire + rf_model <- randomForest(target_variable ~ ., data=train_data, ntree=100, importance=TRUE) + + # Faire des prédictions sur l'ensemble de tests + y_pred <- predict(rf_model, newdata=test_data[,-which(names(test_data)=='target_variable')]) + + # Évaluer les performances du modèle + mse <- mean((y_pred - test_data$target_variable)^2) + rmse <- sqrt(mse) + print(paste('Root Mean Squared Error:', rmse)) + ``` + +### L'apprentissage en profondeur + +![L'apprentissage en profondeur](../images/deep-learning.jpg) + + +!!! note "L'apprentissage en profondeur" + $$ + \hat{y} = f(\mathbf{W}_L f(\mathbf{W}_{L-1} f(\dots f(\mathbf{W}_1\mathbf{x}+\mathbf{b}_1)\dots)+\mathbf{b}_{L-1})+\mathbf{b}_L) + $$ + + _où $\mathbf{x}$ est le vecteur d'entrée, $\mathbf{W}_i$ et $\mathbf{b}_i$ sont respectivement la matrice de poids et le vecteur de biais pour la $i$ème couche, et $ f$ est la fonction d'activation._ + + _Cette formule représente un réseau neuronal à action directe avec des couches $L$, où chaque couche applique une transformation linéaire à la sortie de la couche précédente, suivie d'une fonction d'activation non linéaire. La sortie de la couche finale, $\hat{y}$, représente la sortie prévue du réseau neuronal pour l'entrée donnée $\mathbf{x}$._ + +L'apprentissage profond est un sous-ensemble de l'apprentissage automatique qui implique la formation de réseaux de neurones comportant de nombreuses couches de nœuds interconnectés. Cette approche peut gérer des ensembles de données volumineux et complexes et est utilisée dans un large éventail d'applications, notamment la reconnaissance d'images, le traitement du langage naturel et la reconnaissance vocale. Le processus de formation consiste à alimenter le réseau neuronal avec un grand ensemble de données et à ajuster les poids des connexions entre les nœuds pour minimiser l'erreur entre les sorties prédites et les sorties réelles. Grâce à des itérations répétées, le réseau neuronal peut apprendre à reconnaître des modèles dans les données et à faire des prédictions précises sur les nouvelles données. + +#### Exemple de code + +=== "Python" + ``` py title="deep_learning.py" linenums="1" + #!/usr/bin/env python + + # Charger les bibliothèques requises + import pandas as pd + import numpy as np + import tensorflow as tf + from tensorflow import keras + from sklearn.model_selection import train_test_split + from sklearn.preprocessing import StandardScaler + from sklearn.metrics import accuracy_score + + # Charger l'ensemble de données + data = pd.read_csv('path/to/dataset.csv') + + # Divisez les données en ensembles de formation et de test + X_train, X_test, y_train, y_test = train_test_split(data.drop('target_variable', axis=1), data['target_variable'], test_size=0.2) + + # Standardiser les fonctionnalités d'entrée + scaler = StandardScaler() + X_train_scaled = scaler.fit_transform(X_train) + X_test_scaled = scaler.transform(X_test) + + # Définir le modèle d'apprentissage profond + model = keras.Sequential([ + keras.layers.Dense(64, activation='relu', input_shape=[X_train_scaled.shape[1]]), + keras.layers.Dropout(0.2), + keras.layers.Dense(1, activation='sigmoid') + ]) + + # Compiler le modèle + model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) + + # Entraîner le modèle + history = model.fit(X_train_scaled, y_train, epochs=50, batch_size=32, validation_split=0.1) + + # Évaluer les performances du modèle + y_pred = model.predict_classes(X_test_scaled) + accuracy = accuracy_score(y_test, y_pred) + print('Accuracy:', accuracy) + ``` + +=== "R" + + ``` r title="deep_learning.r" linenums="1" + #!/usr/bin/env Rscript + + # Charger les bibliothèques requises + library(keras) + library(tensorflow) + + # Charger l'ensemble de données + data <- iris + x <- as.matrix(data[, 1:4]) + y <- to_categorical(as.numeric(data[, 5]) - 1) + + # Divisez les données en ensembles de formation et de test + set.seed(123) + train_index <- sample(1:nrow(data), size=0.8*nrow(data)) + x_train <- x[train_index,] + y_train <- y[train_index,] + x_test <- x[-train_index,] + y_test <- y[-train_index,] + + # Définir l'architecture du réseau de neurones + model <- keras_model_sequential() %>% + layer_dense(units = 8, input_shape = c(4)) %>% + layer_activation('relu') %>% + layer_dense(units = 3) %>% + layer_activation('softmax') + + # Compiler le modèle + model %>% compile( + loss = 'categorical_crossentropy', + optimizer = 'adam', + metrics = c('accuracy') + ) + + # Entraîner le modèle + history <- model %>% fit( + x_train, y_train, + epochs = 50, + batch_size = 10, + validation_split = 0.2, + verbose = 0 + ) + + # Évaluer les performances du modèle + metrics <- model %>% evaluate(x_test, y_test) + print(paste('Test Loss:', metrics[1])) + print(paste('Test Accuracy:', metrics[2])) + + # Tracez la précision de la formation et de la validation au fil du temps + plot(history$metrics$accuracy, type='l', col='blue', ylim=c(0,1), ylab='Accuracy', xlab='Epoch') + lines(history$metrics$val_accuracy, type='l', col='red') + legend('bottomright', legend=c('Training', 'Validation'), col=c('blue', 'red'), lty=1) + ``` diff --git a/docs/fr/7-MLOps/Overview.md b/docs/fr/7-MLOps/Overview.md deleted file mode 100644 index d3df5e162..000000000 --- a/docs/fr/7-MLOps/Overview.md +++ /dev/null @@ -1,18 +0,0 @@ - -MLOps, or Machine Learning Operations, refers to the set of practices and tools that enable organizations to develop, deploy, and maintain machine learning models at scale. MLOps aims to streamline the end-to-end process of building and deploying machine learning models by integrating the various stages of the machine learning lifecycle into a cohesive and automated workflow. - -MLOps involves a range of different activities, including data preparation and preprocessing, model training and optimization, model deployment and serving, monitoring and maintenance, and continuous improvement. Some of the key components of MLOps include: - -1. **Data Management:** MLOps involves managing and processing large amounts of data to ensure the quality and accuracy of machine learning models. This involves activities such as data cleaning, data integration, and data transformation. - -2. **Model Training and Optimization:** MLOps involves developing and testing machine learning models, as well as optimizing them for performance and accuracy. This may involve experimenting with different algorithms, hyperparameters, and data pre-processing techniques. - -3. **Model Deployment:** MLOps involves deploying machine learning models to production environments, making them available for use in real-world applications. This may involve containerizing models for easy deployment and scaling, as well as setting up APIs and other interfaces for model serving. - -4. **Monitoring and Maintenance:** MLOps involves monitoring machine learning models in production to ensure that they are performing as expected. This may involve setting up alerts and notifications for model failures, as well as implementing processes for model maintenance and updates. - -5. **Continuous Improvement:** MLOps involves continually improving machine learning models over time, based on feedback from users and ongoing analysis of performance data. This may involve retraining models with new data or incorporating feedback from users to refine models. - -In order to implement MLOps effectively, organizations typically need to adopt a range of different tools and technologies, including data management platforms, machine learning frameworks, containerization tools, and monitoring and logging tools. They also need to establish clear workflows and processes for managing the various stages of the machine learning lifecycle, as well as implementing governance and compliance measures to ensure data privacy and security. - -In summary, MLOps is a critical component of the machine learning lifecycle, enabling organizations to develop, deploy, and maintain machine learning models at scale. By adopting MLOps practices and tools, organizations can streamline their machine learning workflows, improve model accuracy and performance, and deliver more value to users and stakeholders. \ No newline at end of file diff --git a/docs/fr/7-MLOps/PaaS-Integration.md b/docs/fr/7-MLOps/PaaS-Integration.md deleted file mode 100644 index 5ce7f47f9..000000000 --- a/docs/fr/7-MLOps/PaaS-Integration.md +++ /dev/null @@ -1,55 +0,0 @@ -# Overview - -_One of the main advantages of the AAW platform is its ability to integrate with popular machine learning platforms such as Databricks and AzureML._ - -The Advanced Analytics Workspace (AAW) is an open source data analytics platform that is designed to be highly integrable. This means that it can be easily integrated with other platforms and tools to extend its capabilities and streamline workflows. - -An example diagram depicting a possible PaaS connection strategy: - -
- -
-![PaaS](../images/PaaS.png) -
- -**Setup:** If you need help integrating with a platform as a service offering, we're happy -to help! - -## Integration with External Platform as a Service (PaaS) Offerings - -_Integration is key to success._ - -
-[![Integrate with PaaS](../images/IntegratePaaS.PNG)]() -
- -Our open source platform offers unparalleled optionality to our users. By allowing users to use open source tools, we empower them to use their preferred data science and machine learning frameworks. But the real power of our platform comes from its ability to integrate with many Platform as a Service (PaaS) offerings, like Databricks or AzureML. This means that our users can leverage the power of the cloud to run complex data processing and machine learning pipelines at scale. With the ability to integrate with PaaS offerings, our platform enables our users to take their work to the next level, by giving them the power to scale their workloads with ease, and take advantage of the latest innovations in the field of data science and machine learning. By providing this level of optionality, we ensure that our users can always choose the right tool for the job, and stay ahead of the curve in an ever-changing field. - -We can integrate with many Platform as a Service (PaaS) offerings, like Databricks or AzureML. - - -## Databricks - -- [Databricks from Microsoft](https://azure.microsoft.com/en-ca/services/databricks/) - -Databricks is a cloud-based platform that provides a unified analytics platform for big data processing and machine learning. With its powerful distributed computing engine and streamlined workflow tools, Databricks is a popular choice for building and deploying machine learning models. By integrating with Databricks, the AAW platform can leverage its distributed computing capabilities to train and deploy machine learning models at scale. - -## AzureML - -- [Azure ML from Microsoft](https://azure.microsoft.com/en-us/services/machine-learning/) - -AzureML is another popular machine learning platform that provides a wide range of tools for building, training, and deploying machine learning models. By integrating with AzureML, the AAW platform can leverage its powerful tools for building and training models, as well as its ability to deploy models to the cloud. - -### Examples - -_Examples of how to integrate the AAW platform with these and other platforms can be found on the MLOps Github repository._ - -- [MLOps Github Repository](https://github.com/StatCan/aaw-kubeflow-mlops) - -This repository contains a range of examples and tutorials for using the AAW platform in various machine learning workflows, including data preparation, model training, and model deployment. - -## Conclusion - -By integrating with popular machine learning platforms like Databricks and AzureML, the AAW platform provides a powerful and flexible solution for building, deploying, and managing machine learning workflows at scale. - -By leveraging the integrations and tools provided by these platforms, data scientists and machine learning engineers can accelerate their workflows and achieve better results with less effort. \ No newline at end of file diff --git a/docs/fr/7-MLOps/Serving.md b/docs/fr/7-MLOps/Serving.md index e786fa8e7..c42d90670 100644 --- a/docs/fr/7-MLOps/Serving.md +++ b/docs/fr/7-MLOps/Serving.md @@ -1,18 +1,18 @@ -# Model Serving with Seldon Core and KFServing +# Service de modèles avec Seldon Core et KFServing -!!! warning "⚒ This page is under construction ⚒" - The person writing this entry does not know enough about - this feature to write about it, but you can ask on our Slack channel. +!!! warning "⚒ Cette page est en construction ⚒" + La personne qui écrit cet article n'en sait pas assez sur + cette fonctionnalité pour en parler, mais vous pouvez demander sur notre chaîne Slack. -## Serverless with KNative +## Sans serveur avec KNative -Kubernetes and [KNative](https://knative.dev/) let your services scale up and -down on demand. This lets you create APIs to serve Machine Learning models, -without the need to manage load balancing or scale-up. The platform can handle -all of your scaling for you, so that you can focus on the program logic. +Kubernetes et [KNative](https://knative.dev/) permettent à vos services d'évoluer et +vers le bas sur demande. Cela vous permet de créer des API pour servir des modèles de Machine Learning, +sans avoir besoin de gérer l’équilibrage de charge ou la mise à l’échelle. La plateforme peut gérer +toute votre mise à l'échelle pour vous, afin que vous puissiez vous concentrer sur la logique du programme. -!!! warning "⚒ This page is under construction ⚒" - The person writing this entry does not know enough about this - feature to write about it, but you can ask on our Slack channel. +!!! warning "⚒ Cette page est en construction ⚒" + La personne qui écrit cet article n'en sait pas assez à ce sujet + fonctionnalité pour écrire à ce sujet, mais vous pouvez demander sur notre chaîne Slack. \ No newline at end of file diff --git a/docs/fr/images/Disque.PNG b/docs/fr/images/Disque.PNG new file mode 100644 index 0000000000000000000000000000000000000000..2e99465d325b945582dc738f9498eb0d480201ef GIT binary patch literal 2354 zcma);_fykZ7seyf0!tAP5JNSD4mN541p?$70thHeRR}DhD3Dk{QA075P@)MP=_Nr# z3}sP_V4@I;bSWZT&;TpNRRKZRy#K(v`@=oYJTvFaoqOh<&zzKVPWECV3L+p7NDM$C zTzUGMccO*)c=46wpaV}1Ty?d#20iJ6Eb$CK*2>We1Y(dycl`x;9vq4Eyb1z|^Qy#O 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Introduction: 7-MLOps/Machine-Learning-Model-Serving.md - - Example Notebooks: - - Seldon Iris Classifier: 7-MLOps/sklearn_iris_jsondata.ipynb - - Integrate with External PaaS: 7-MLOps/PaaS-Integration.md + - Aperçu: 7-MLOps/Aperçu.md + - Modèles d'apprentissage automatique: 7-MLOps/Modèles-d'apprentissage-automatique.md + - Modèles ML d'entraînement: 7-MLOps/Modele-Ml-entrainement.md + - Modèles ML de stockage: 7-MLOps/Modele-ML-stockage.md + - Modèles de service ML: + - Introduction: 7-MLOps/Modele-Service-ML.md + - Bloc-note d'exemple: + - Classificateur Seldon Iris : 7-MLOps/sklearn_iris_jsondata.ipynb + - Intégration avec PaaS externe: 7-MLOps/Integration-PaaS.md - Aide/Contactez-nous: Aide.md From dfb44c2132a2c0a0206fa45cdde0d031076e422a Mon Sep 17 00:00:00 2001 From: Wendy V Gaultier Date: Tue, 31 Oct 2023 17:58:59 +0000 Subject: [PATCH 09/15] Add missing links --- .../Geospatial-Analytical-Environment.md | 2 +- docs/en/4-Collaboration/Request-a-Namespace.md | 6 ++---- docs/en/images/DasGettingStarted.PNG | Bin 0 -> 235433 bytes .../fr/4-Collaboration/Demander-EspaceDeNom.md | 6 ++---- .../Environnement-Analyse-Geospatiale.md | 2 +- docs/fr/images/CommencerKubeflow.PNG | Bin 0 -> 246719 bytes 6 files changed, 6 insertions(+), 10 deletions(-) create mode 100644 docs/en/images/DasGettingStarted.PNG create mode 100644 docs/fr/images/CommencerKubeflow.PNG diff --git a/docs/en/4-Collaboration/Geospatial-Analytical-Environment.md b/docs/en/4-Collaboration/Geospatial-Analytical-Environment.md index 837d596f6..030e65885 100644 --- a/docs/en/4-Collaboration/Geospatial-Analytical-Environment.md +++ b/docs/en/4-Collaboration/Geospatial-Analytical-Environment.md @@ -212,4 +212,4 @@ toronto_map ## Learn More about DAS Geospatial Analytical Environment (GAE) and Services -[GAE Help Guide](https://statcan.github.io/daaas-dads-geo/) +[GAE Help Guide](https://statcan.github.io/gae-eag/) diff --git a/docs/en/4-Collaboration/Request-a-Namespace.md b/docs/en/4-Collaboration/Request-a-Namespace.md index cb1da258b..ef1edabc5 100644 --- a/docs/en/4-Collaboration/Request-a-Namespace.md +++ b/docs/en/4-Collaboration/Request-a-Namespace.md @@ -6,11 +6,9 @@ By default, everyone gets their own personal namespace, `firstname-lastname`. If ## Requesting a namespace -To create a namespace for a team, go to the AAW portal. **Click the ⋮ menu on [the Kubeflow section of the portal](https://www.statcan.gc.ca/data-analytics-services/aaw)**. +To create a namespace for a team, **Click the [Getting Started](https://www.statcan.gc.ca/data-analytics-services/overview)**. -![The hamburger menu to the right of Kubeflow on the portal](../images/KubeflowNamespace.PNG) - -Enter the name you are requesting and submit the request. Be sure to use only lower case letters plus dashes. ![Submit your request for a shared namespace](../images/KubeflowNamespace2.png) +![Getting Started](../images/DasGettingStarted.PNG) !!! warning "The namespace cannot have special characters other than hyphens" diff --git a/docs/en/images/DasGettingStarted.PNG b/docs/en/images/DasGettingStarted.PNG new file mode 100644 index 0000000000000000000000000000000000000000..c6d32ef3edddf1bb81afcece9ec98ad12c3d818d GIT binary patch literal 235433 zcmb5VWl)=K)CP#PSb-v;xEI$JcMI+iC|;~c(cS> z2=NQqRZCtPsdAKJ5Ag=gT1r(42?-XDeQ)**@gCDzLEjY#3Ag9(5AuLhi6s)!Q<0*q zl#Z9lK^FRZ!YS&3bG}ZbD7ZvYL@uXDPDD`C_2pT`>EPD`2YFXTr%q|o zkt@HLUI(<{H|!RvZ*(z-72lPm)Ys%%y?-GPfeIjs5)OK#i2%G~0AL=(_=dEX#FwB_ zB~ygp%8Lzsd9nYVoG=#2ke87)iDylJRdcs6w_f3$Tz3$$gxWZ1e=eTzvuLwngF^UM z@EC-e86Yv*q9-2D2}* zd>axR?Kgp#RTnzhv_&4hPgqoONvSJ@52Qm6*?!vh{&Q7+^EVyNwg48suE@v&-49`j zy~?sNmZ84RuR0h@uQg<=jU6p?To^7H;aNCsQo)|07jo9AUDuqDm^C^g&`i<2Zyof{ 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des traits d'union" diff --git a/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md b/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md index e32f1338a..783f763bf 100644 --- a/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md +++ b/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md @@ -192,4 +192,4 @@ toronto_map [La documentation complète de l'API ArcGIS peut être trouvée ici](https://developers.arcgis.com/python/) ##E n savoir plus sur l'environnement analytique géospatial (GAE) et les services SAD -[Guide d'aide GAE](https://statcan.github.io/daaas-dads-geo/) +[Guide d'aide GAE](https://statcan.github.io/gae-eag/) diff --git a/docs/fr/images/CommencerKubeflow.PNG b/docs/fr/images/CommencerKubeflow.PNG new file mode 100644 index 0000000000000000000000000000000000000000..229f0b7c202c3d55b3b8ac8e82eaa617bc6de63c GIT binary patch literal 246719 zcmZ^K1yGxP_a#oz6t@7yt!Qy~YjJIHE$;48pjd*I7Ax*rTnYpWQmnWXOK}VCbkpyB 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zm&UK>1=^!8jXW5)w{0eNN;TnC7jWTBE#mVM!Y|BTW=raC#dlO6UE8vVLxB2lD9O1e z84(~BLS}4JQ}V zcwjwzaJhAowT?vTu=4Q44Sgq9Qt^w{56&QVmWRUS6JEZtLDC?4aumq>PMie|Ku3=n z_s~M;L#O7aQ$*)O&K`es7@_FG+uCJ(RD^U$8CXnyE=&fVh;FfS)2twB3xBjB7xh}Fn{jQJ7_+?v z2k6&0aZ&m~zwPeaGkI?d4d zkr$dmscz6`TY8ns(G$yFT=L_*f3zQ_v<&NCBp*yc@@?r3nezztrmK3gH-c3@A&`+I zVE4`5x+!yDmZyPw_H5w{sCzpmA@s#04R1#?(p?8SBiJjcLut&TcTt@VmEDy7bCm3g z!)y?A8S$9)T$4RfPsP)EXI%3~f@`qo Date: Wed, 1 Nov 2023 08:55:01 -0400 Subject: [PATCH 10/15] Update Gitlab.md missed translation for access token --- docs/fr/6-Gitlab/Gitlab.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/fr/6-Gitlab/Gitlab.md b/docs/fr/6-Gitlab/Gitlab.md index bb7b8c95a..4b2691f30 100644 --- a/docs/fr/6-Gitlab/Gitlab.md +++ b/docs/fr/6-Gitlab/Gitlab.md @@ -22,7 +22,7 @@ Comme le montre la capture d'écran ci-dessus, j'ai cloné le repo ! Si vous essayez de `git push ....` vous rencontrerez une erreur qui vous mènera à la [documentation d'aide de GitLab](https://gitlab.k8s.cloud.statcan.ca/help/user/profile/account/two_factor_authentication.md#error-http-basic-access-denied-the-provided-password-or-token-) Vous devrez créer un jeton d'accès personnel pour cela. Pour cela, allez dans GitLab, cliquez sur l'icône de votre profil, puis sur `Préférences` et ensuite sur `Tokens d'accès` -![Personal Access Token](https://user-images.githubusercontent.com/23174198/217061060-122dded8-dc80-46ce-a907-a85913cf5dd7.png) +![Jeton d'accès personnel](https://user-images.githubusercontent.com/23174198/217061060-122dded8-dc80-46ce-a907-a85913cf5dd7.png) Suivez les instructions en entrant le nom, la date d'expiration du token et en accordant les permissions au token (j'ai accordé `write_repository`). ### Étape 5 : Personnaliser `Git` pour qu'il soit à votre image From 7bd7c8df25df3585af4af19873ab84d8112d4626 Mon Sep 17 00:00:00 2001 From: Wendy Gaultier Date: Wed, 1 Nov 2023 10:11:55 -0400 Subject: [PATCH 11/15] Update sklearn_iris_jsondata.ipynb Undo English update --- docs/fr/7-MLOps/sklearn_iris_jsondata.ipynb | 47 +++++++++++---------- 1 file changed, 24 insertions(+), 23 deletions(-) diff --git a/docs/fr/7-MLOps/sklearn_iris_jsondata.ipynb b/docs/fr/7-MLOps/sklearn_iris_jsondata.ipynb index e0a993312..c9256f0e0 100644 --- a/docs/fr/7-MLOps/sklearn_iris_jsondata.ipynb +++ b/docs/fr/7-MLOps/sklearn_iris_jsondata.ipynb @@ -5,11 +5,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Serving an Iris Classifier with Seldon\n", + "# Servir un classificateur d'iris avec Seldon\n", "\n", - "## Goal of Notebook\n", + "## Objectif du bloc-notes\n", "\n", - "The goal of this notebook is to wrap a `scikit-learn` Python model for use as a prediction microservice with Seldon Core." + "L'objectif de ce bloc-notes est d'encapsuler un modèle Python \"scikit-learn\" à utiliser comme microservice de prédiction avec Seldon Core." ] }, { @@ -17,9 +17,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Setup Python\n", + "## Configurer Python\n", "\n", - "Write requirements.txt and configure our Python virtual environment." + "Écrivez requirements.txt et configurez notre environnement virtuel Python." ] }, { @@ -107,7 +107,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Seldon only runs in the AAW dev environment; modify `pip.conf` to retrieve packages from the internet." + "Seldon ne fonctionne que dans l'environnement de développement ETAA; modifiez `pip.conf` pour récupérer les packages sur internet." ] }, { @@ -154,7 +154,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can add the kernel to the JupyterLab launcher for easy reusing." + "Nous pouvons ajouter le noyau au lanceur JupyterLab pour une réutilisation facile." ] }, { @@ -180,9 +180,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Deploy Model\n", + "## Déployer le modèle\n", "\n", - "Write a SeldonDeployment YAML file for the containerized model." + "Écrivez un fichier YAML SeldonDeployment pour le modèle conteneurisé." ] }, { @@ -228,10 +228,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Deploy the SeldonDeployment." + "Déployez SeldonDeployment." ] }, { @@ -277,7 +278,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Is the service available?" + "Le service est-il disponible ?" ] }, { @@ -310,9 +311,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Test Model\n", + "## Modèle de test\n", "\n", - "With the virtual environment setup and activated, import the necessary Python libraries." + "Avec l'environnement virtuel configuré et activé, importez les bibliothèques Python nécessaires." ] }, { @@ -339,7 +340,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The Iris Classifier takes a set of four measurements, in this case `[5.964, 1.006, 2.081, 1.031]`, and it returns a set of probabilities, one for each of three types of iris flowers. [You can read more about iris data here.](https://archive.ics.uci.edu/ml/datasets/iris) Let's send some data to the model. " + "Le classificateur d'iris prend un ensemble de quatre mesures, dans ce cas `[5.964, 1.006, 2.081, 1.031]`, et il renvoie un ensemble de probabilités, une pour chacun des trois types de fleurs d'iris. [Vous pouvez en savoir plus sur les données d'iris ici.](https://archive.ics.uci.edu/ml/datasets/iris) Envoyons quelques données au modèle." ] }, { @@ -367,9 +368,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Results\n", + "## Résultats\n", "\n", - "These are the results in raw JSON." + "Ce sont les résultats en JSON brut." ] }, { @@ -396,7 +397,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This plotting function reveals a bar chart with the classifier's inference." + "Cette fonction de traçage révèle un graphique à barres avec l'inférence du classificateur." ] }, { @@ -415,11 +416,11 @@ "outputs": [], "source": [ "def display_iris_classifier_results(res):\n", - " \"\"\"Takes the classification results from the iris classifier and plots them in a bar chart for easy viewing of class probabilities.\n", + " \"\"\" Prend les résultats de classification du classificateur d'iris et les trace dans un graphique à barres pour une visualisation facile des probabilités de classe.\n", "\n", - " Args:\n", - " res (string): results from iris model served by Seldon.\n", - " \"\"\"\n", + " Arguments :\n", + " res (string) : résultats du modèle d'iris servi par Seldon.\n", + " \"\"\"\n", " results = res[-1]\n", " results = json.loads(results)\n", " names = results[\"data\"][\"names\"]\n", @@ -446,9 +447,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Clean Up\n", + "## Nettoyer\n", "\n", - "Clean up by removing the deployment." + "Nettoyez en supprimant le déploiement." ] }, { From a7c7cff0a154f1d8a5abb09848f553689fdaaa22 Mon Sep 17 00:00:00 2001 From: Wendy V Gaultier Date: Wed, 1 Nov 2023 19:05:51 +0000 Subject: [PATCH 12/15] Fix PR --- docs/fr/1-Experiences/Apercu.md | 4 +- docs/fr/1-Experiences/Bureau-virtuel.md | 6 +-- docs/fr/1-Experiences/Jupyter.md | 2 +- docs/fr/1-Experiences/Kubeflow.md | 39 +++++++------------ docs/fr/1-Experiences/RStudio.md | 5 +-- .../1-Experiences/Selectionner-une-image.md | 10 ++--- docs/fr/2-Publication/Datasette.md | 8 ++-- docs/fr/2-Publication/PowerBI.md | 6 +-- docs/fr/3-Pipelines/Argo.md | 10 ++--- .../4-Collaboration/Demander-EspaceDeNom.md | 2 +- .../Environnement-Analyse-Geospatiale.md | 12 +++--- 11 files changed, 46 insertions(+), 58 deletions(-) diff --git a/docs/fr/1-Experiences/Apercu.md b/docs/fr/1-Experiences/Apercu.md index 0469032d2..e519a997f 100644 --- a/docs/fr/1-Experiences/Apercu.md +++ b/docs/fr/1-Experiences/Apercu.md @@ -4,7 +4,7 @@ _Traitez les données à l'aide de R, Python ou Julia avec Kubeflow, une platefo Avec Kubeflow, vous pouvez traiter les données de manière évolutive et efficace en utilisant le langage de programmation de votre choix. Une fois Kubeflow configuré, utilisez Jupyter Notebooks pour créer et partager des documents contenant du code en direct, des équations ou des visualisations. -Vous pouvez également exécuter Ubuntu en tant que bureau virtuel avec Kubeflow, vous donnant accès à un environnement de développement puissant qui peut être personnalisé selon vos besoins. Avec R Shiny, un framework d'application Web pour R, vous pouvez facilement créer et publier des tableaux de bord statiques et interactifs pour communiquer les résultats de votre analyse aux parties prenantes. +Vous pouvez également exécuter Ubuntu en tant que bureau virtuel avec Kubeflow, vous donnant accès à un environnement de développement puissant qui peut être personnalisé selon vos besoins. Avec R Shiny, un cadre d'application Web pour R, vous pouvez facilement créer et publier des tableaux de bord statiques et interactifs pour communiquer les résultats de votre analyse aux parties prenantes. Kubeflow fournit également une intégration avec des plates-formes externes en tant que service, telles que Google Cloud Platform (GCP) et Amazon Web Services (AWS), vous permettant de déplacer facilement des données et des charges de travail entre différents services cloud. De plus, grâce aux fonctionnalités de collaboration de Kubeflow, vous pouvez travailler sur vos projets avec votre équipe en temps réel, en partageant votre analyse, votre code et vos résultats de manière transparente. @@ -18,7 +18,7 @@ L'expérimentation en science des données fait référence au processus de conc 4. Analyser les données : Une fois les données propres, vous pouvez commencer à les analyser pour tester votre hypothèse. Cela peut impliquer l'exécution de tests statistiques ou d'algorithmes d'apprentissage automatique, la visualisation des données pour identifier des modèles ou des tendances, ou l'utilisation d'autres techniques analytiques pour obtenir des informations. -5. Tirer des conclusions : sur la base des résultats de votre analyse, vous pouvez tirer des conclusions quant à savoir si votre hypothèse est étayée ou non. Vous pourrez peut-être également identifier des domaines nécessitant des recherches ou des expérimentations plus approfondies. +5. Tirer des conclusions : sur la base des résultats de votre analyse, vous pouvez tirer des conclusions quant à savoir si votre hypothèse est supportée ou non. Vous pourrez peut-être également identifier des domaines nécessitant des recherches ou des expérimentations plus approfondies. L'analyse des données est un élément clé de l'expérimentation en science des données et implique l'utilisation de diverses techniques et outils pour donner un sens à de grandes quantités de données. Cela peut impliquer une analyse exploratoire des données, dans laquelle vous utilisez des visualisations et des statistiques récapitulatives pour acquérir une première compréhension des données, ou des techniques plus avancées telles que l'apprentissage automatique ou la modélisation statistique. L'analyse des données peut être utilisée pour répondre à un large éventail de questions, depuis de simples questions descriptives sur les données jusqu'à des questions prédictives ou prescriptives plus complexes. diff --git a/docs/fr/1-Experiences/Bureau-virtuel.md b/docs/fr/1-Experiences/Bureau-virtuel.md index 0e36714e2..478ee153d 100644 --- a/docs/fr/1-Experiences/Bureau-virtuel.md +++ b/docs/fr/1-Experiences/Bureau-virtuel.md @@ -2,13 +2,13 @@ ### Bureau virtuel Ubuntu -_Vous pouvez exécuter un [**Bureau Ubuntu**](https://ubuntu.com/desktop) complet, avec des applications typiques, directement dans votre navigateur, en utilisant [**Kubeflow!**](https://kubeflow. aaw.cloud.statcan.ca/)_ +_Vous pouvez exécuter un [**Bureau Ubuntu**](https://ubuntu.com/desktop) complet, avec des applications typiques, directement dans votre navigateur, en utilisant [**Kubeflow!**](https://kubeflow.aaw.cloud.statcan.ca/)_

[![Bureau Virtuel](../images/VirtualDesktop.jpg)](https://novnc.com/info.html)
-Le bureau virtuel Ubuntu est un outil puissant pour les scientifiques de données et les ingénieurs en apprentissage automatique qui doivent exécuter des charges de travail gourmandes en ressources dans le nuage informatique. Ubuntu est une distribution Linux populaire largement utilisée dans les communautés de science des données et d'apprentissage automatique en raison de sa forte prise en charge des outils open source tels que R et Python. Avec Ubuntu Virtual Desktop, vous pouvez rapidement démarrer une machine virtuelle avec Ubuntu préinstallé et y accéder depuis n'importe où avec une connexion Internet. Cela signifie que vous pouvez effectuer des tâches d'analyse de données et d'apprentissage automatique depuis votre ordinateur portable, votre tablette ou votre téléphone sans avoir à vous soucier des limitations matérielles. +Le bureau virtuel Ubuntu est un outil puissant pour les scientifiques de données et les ingénieurs en apprentissage automatique qui doivent exécuter des charges de travail gourmandes en ressources dans le nuage informatique. Ubuntu est une distribution Linux populaire largement utilisée dans les communautés de science des données et d'apprentissage automatique en raison de sa forte prise en charge des outils de logiciels libres tels que R et Python. Avec Ubuntu Virtual Desktop, vous pouvez rapidement démarrer une machine virtuelle avec Ubuntu préinstallé et y accéder depuis n'importe où avec une connexion Internet. Cela signifie que vous pouvez effectuer des tâches d'analyse de données et d'apprentissage automatique depuis votre ordinateur portable, votre tablette ou votre téléphone sans avoir à vous soucier des limitations matérielles. ## Qu'est-ce que le Bureau à distance ? @@ -26,7 +26,7 @@ Notre version de bureau à distance est construite sur une image R Geospatiale. ## Personnalisation -_pip_, _conda_, _npm_ et _yarn_ sont disponibles pour installer divers packages. +_pip_, _conda_, _npm_ et _yarn_ sont disponibles pour installer divers paquets. # Installation diff --git a/docs/fr/1-Experiences/Jupyter.md b/docs/fr/1-Experiences/Jupyter.md index aa34d1832..2bf3941e7 100644 --- a/docs/fr/1-Experiences/Jupyter.md +++ b/docs/fr/1-Experiences/Jupyter.md @@ -2,7 +2,7 @@ ## Jupyter: Expérience conviviale de R et Python -Jupyter vous propose des **bloc-notes** pour écrire votre code et réaliser des visualisations. Vous pouvez rapidement itérer, visualiser et partager vos analyses. Parce qu'il fonctionne sur un serveur (que vous avez configuré dans la section Kubeflow), vous pouvez effectuer de très grandes analyses sur du matériel centralisé, en ajoutant autant de puissance que nécessaire ! Et comme il est sur le cloud, vous pouvez également le partager avec vos collègues. +Jupyter vous propose des **bloc-notes** pour écrire votre code et réaliser des visualisations. Vous pouvez rapidement itérer, visualiser et partager vos analyses. Parce qu'il fonctionne sur un serveur (que vous avez configuré dans la section Kubeflow), vous pouvez effectuer de très grandes analyses sur du matériel centralisé, en ajoutant autant de puissance que nécessaire! Et comme il est sur le nuage, vous pouvez également le partager avec vos collègues. ### Explorez vos données diff --git a/docs/fr/1-Experiences/Kubeflow.md b/docs/fr/1-Experiences/Kubeflow.md index e102048be..2eb7ac396 100644 --- a/docs/fr/1-Experiences/Kubeflow.md +++ b/docs/fr/1-Experiences/Kubeflow.md @@ -36,6 +36,8 @@ votre équipe. - Accédez à l'onglet Serveurs bloc-notes +- Assurez-vous que l'espace de noms de votre projet est sélectionné dans le menu déroulant des espaces de noms et que l'exemple dans la capture d'écran est "aaw-team". + ![Kubeflow gère les serveurs Jupyter](../images//kubeflow-main-ui.PNG) - Puis clique **+ Nouveau serveur** @@ -46,18 +48,11 @@ votre équipe. :** le nom de votre serveur doit être en lettres minuscules avec des tirets. **Pas d'espaces, et non souligne.** -- Vous devrez spécifier un espace de noms. Par défaut, vous disposerez d'un espace de noms par défaut pour votre compte, mais pour les projets, vous devrez peut-être sélectionner l'espace de noms créé spécifiquement pour ce projet. Sinon, le serveur de notebooks que vous créez risque de ne pas disposer des droits d'accès aux ressources requises pour le projet. - ## Image -Vous devrez choisir une image. Il existe JupyterLab, RStudio, Ubuntu distant -images de bureau et SAS disponibles. L'image SAS est disponible uniquement pour StatCan -employés (en raison des limitations de licence), les autres sont disponibles pour tout le monde. -Sélectionnez le menu déroulant pour sélectionner des options supplémentaires parmi celles-ci (par exemple -instance, images CPU, PyTorch et TensorFlow pour JupyterLab). +Vous devrez choisir une image. Il existe JupyterLab, RStudio, Ubureau à distance ubuntu et SAS disponibles. L'image SAS est disponible uniquement pour les employés de StatCan (en raison des limitations de licence), les autres sont disponibles pour tout le monde. Sélectionnez le menu déroulant pour sélectionner des options supplémentaires parmi celles-ci (par exemple les images CPU, PyTorch et TensorFlow pour JupyterLab). -Vérifiez le nom des images et choisissez-en une qui correspond à ce que vous souhaitez faire. Je ne sais pas -lequel choisir ? Découvrez vos options [ici](../Selectionner-une-image). +Vérifiez le nom des images et choisissez-en une qui correspond à ce que vous souhaitez faire. Je ne sais pas lequel choisir ? Découvrez vos options [ici](../Selectionner-une-image). ![Choisir une image](../images/select-image-screenshot.PNG) @@ -71,7 +66,7 @@ Lors de la création d'un bloc-notes, cliquez sur « Options avancées », coche ![Choisir une image](../images/customNotebook.PNG) -Une fois le notebook créé, si le statut n'est pas prêt au bout de quelques minutes, cliquez sur son nom pour voir la page de détails. Ensuite l'onglet « Événements » vous permet de savoir exactement ce qui se passe. Si nécessaire, contactez le canal d’aide sur Slack si le problème persiste. +Une fois le bloc-note créé, si le statut n'est pas prêt au bout de quelques minutes, cliquez sur son nom pour voir la page de détails. Ensuite l'onglet « Événements » vous permet de savoir exactement ce qui se passe. Si nécessaire, contactez le canal d’aide sur Slack si le problème persiste. ## CPU et mémoire @@ -91,7 +86,7 @@ Cela vous permet de répondre à vos besoins de calcul tout en minimisant les coûts. Pour un serveur portable GPU, vous obtiendrez toujours le serveur complet (6 cœurs CPU, 96 Gio de mémoire accessible et 1 GPU). -Dans les options avancées, vous pouvez sélectionner une limite supérieure au nombre de cœurs de processeur et de RAM demandés. Le montant demandé est le montant garanti disponible pour votre serveur notebook et vous paierez toujours au moins ce montant. Si la limite est supérieure à la quantité demandée, si des cœurs de RAM et de processeur supplémentaires sont disponibles sur ce serveur partagé dans le cluster, votre serveur de notebook peut les utiliser selon vos besoins. Un cas d’utilisation est celui des tâches qui ne nécessitent généralement qu’un seul cœur de processeur mais qui peuvent bénéficier du multithreading pour accélérer certaines opérations. En demandant un cœur de processeur mais une limite plus élevée, vous pouvez payer beaucoup moins pour le serveur d'ordinateurs portables tout en lui permettant d'utiliser des cœurs de processeur de rechange inutilisés si nécessaire pour accélérer les calculs. +Dans les options avancées, vous pouvez sélectionner une limite supérieure au nombre de cœurs de processeur et de RAM demandés. Le montant demandé est le montant garanti disponible pour votre serveur bloc-notes et vous paierez toujours au moins ce montant. Si la limite est supérieure à la quantité demandée, si des cœurs de RAM et de processeur supplémentaires sont disponibles sur ce serveur partagé dans le cluster, votre serveur de bloc-notes peut les utiliser selon vos besoins. Un cas d’utilisation est celui des tâches qui ne nécessitent généralement qu’un seul cœur de processeur mais qui peuvent bénéficier du multithreading pour accélérer certaines opérations. En demandant un cœur de processeur mais une limite plus élevée, vous pouvez payer beaucoup moins pour le serveur boc-notes tout en lui permettant d'utiliser des cœurs de processeur de rechange inutilisés si nécessaire pour accélérer les calculs. ![Choisir CPU et RAM](../images/cpu-ram.PNG) @@ -100,8 +95,8 @@ Dans les options avancées, vous pouvez sélectionner une limite supérieure au Si vous voulez un serveur GPU, sélectionnez « 1 » comme nombre de GPU et « NVIDIA » comme GPU fournisseur (le bouton de création sera grisé jusqu'à ce que le fournisseur de GPU soit sélectionné si -vous avez un GPU spécifié). Les serveurs multi-GPU sont actuellement pris en charge sur l'AAW -système uniquement sur une base spéciale sur demande, veuillez contacter les mainteneurs d'AAW si +vous avez un GPU spécifié). Les serveurs multi-GPU sont actuellement pris en charge sur le +système ETAA uniquement sur une base spéciale sur demande, veuillez contacter les mainteneurs d'ETAA si vous souhaitez un serveur multi-GPU. ![Configuration GPU](../images/kubeflow_gpu_selection.PNG) @@ -116,8 +111,7 @@ cœurs et 112 Go de mémoire. ## Volume d'espace de travail -Vous aurez besoin d'un volume d'espace de travail, sur lequel le dossier personnel sera monté. Là -différentes options de configuration sont disponibles : +Vous aurez besoin d'un volume d'espace de travail, sur lequel le dossier personnel sera monté. Ces différentes options de configuration sont disponibles : - Vous pouvez soit réutiliser un volume d'espace de travail existant auparavant, soit en créer un nouveau. @@ -132,27 +126,22 @@ différentes options de configuration sont disponibles : ## Volumes de données -Vous pouvez également créer des volumes de données pouvant être utilisés pour stocker des données supplémentaires. Plusieurs -des volumes de données peuvent être créés. Cliquez sur le bouton Ajouter un nouveau volume pour créer un nouveau volume et -préciser sa configuration. Cliquez sur le bouton Attacher un volume existant pour monter un volume existant. -volume de données vers le serveur de notebook. Il existe les paramètres de configuration suivants pour -volumes de données : +Vous pouvez également créer des volumes de données pouvant être utilisés pour stocker des données supplémentaires. Plusieurs volumes de données peuvent être créés. Cliquez sur le bouton Ajouter un nouveau volume pour créer un nouveau volume et préciser sa configuration. Cliquez sur le bouton Attacher un volume existant pour monter un volume existant vers le serveur de bloc-notes. Il existe les paramètres de configuration suivants pour les volumes de données : - **Nom** : Nom du volume. - **Taille en GiB** : De 4 Gio à 512 Gio. -- **Chemin de montage** : Chemin où le volume de données est accessible sur le serveur notebook, par +- **Chemin de montage** : Chemin où le volume de données est accessible sur le serveur bloc-notes, par par défaut `/home/jovyan/vol-1`, `/home/jovyan/vol-2`, etc. (compteur incrémentiel par données volume monté). Lors du montage d'un volume de données existant, l'option de nom devient une liste déroulante du -volumes de données existants. Uniquement un volume non actuellement monté sur un serveur de notebook existant +volumes de données existants. Uniquement un volume non actuellement monté sur un serveur de bloc-notes existant peut être utilisé. L'option du chemin de montage reste configurable par l'utilisateur avec les mêmes valeurs par défaut que créer un nouveau volume. -L'icône de la poubelle à droite peut être utilisée pour supprimer un fichier existant ou créé accidentellement. -volume de données. +L'icône de la poubelle à droite peut être utilisée pour supprimer un volume de données existant ou créé accidentellement. ![Créer un volume de données](../images/kubeflow_volumes.png) @@ -187,5 +176,5 @@ Les éléments suivants peuvent être personnalisés ici : ## Partagez votre espace de travail -Dans Kubeflow, chaque utilisateur dispose d'un **espace de noms** qui contient son travail (son serveurs de blocs-note, pipelines, disques, etc.). Votre espace de nom vous appartient, mais peut être partagé si vous souhaitez collaborer avec d'autres. **Pour plus de détails sur collaboration sur la plateforme, voir +Dans Kubeflow, chaque utilisateur dispose d'un **espace de noms** qui contient son travail (son serveurs blocs-note, pipelines, disques, etc.). Votre espace de nom vous appartient, mais peut être partagé si vous souhaitez collaborer avec d'autres. **Pour plus de détails sur collaboration sur la plateforme, voir [Collaboration](../4-Collaboration/Aperçu.md).** diff --git a/docs/fr/1-Experiences/RStudio.md b/docs/fr/1-Experiences/RStudio.md index 07896b8b9..f51ca81a2 100644 --- a/docs/fr/1-Experiences/RStudio.md +++ b/docs/fr/1-Experiences/RStudio.md @@ -18,10 +18,9 @@ Vous pouvez installer les packages `R` ou `python` avec `conda` ou `install.pack # Une fois que vous avez les bases... -## R-Brillant +## R-Shiny -Vous pouvez aussi utiliser « Shiny » ! Shiny est un package R open source qui fournit un web -cadre pour créer des applications Web à l'aide de R. Shiny vous aide à transformer votre +Vous pouvez aussi utiliser « Shiny » ! Shiny est un package R open source qui fournit un cadre web pour créer des applications Web à l'aide de R. Shiny vous aide à transformer votre analyses en applications Web interactives. ![RStudio avec R-Shiny](../images/rstudio_rshiny_visual.png) diff --git a/docs/fr/1-Experiences/Selectionner-une-image.md b/docs/fr/1-Experiences/Selectionner-une-image.md index d73b27198..dc80d0997 100644 --- a/docs/fr/1-Experiences/Selectionner-une-image.md +++ b/docs/fr/1-Experiences/Selectionner-une-image.md @@ -1,6 +1,6 @@ -# Sélection d'une image pour votre serveur Notebook +# Sélection d'une image pour votre serveur bloc-notes -En fonction de votre projet ou du cas d'utilisation du Notebook Server, certaines images peuvent être plus adapté que d'autres. Ce qui suit passera en revue les principales caractéristiques de chacun pour vous aider à choisir l’image la plus appropriée pour vous. +En fonction de votre projet ou du cas d'utilisation du serveur bloc-notes, certaines images peuvent être plus adapté que d'autres. Ce qui suit passera en revue les caractéristiques principales de chacun pour vous aider à choisir l’image la plus appropriée pour vous. Lors de la sélection d'une image, vous disposez de 3 options principales : @@ -21,9 +21,9 @@ Les [bloc-notes Jupyter](https://jupyter.org/) sont utilisés pour créer et par transformation de données, simulation numérique, statistique modélisation, apprentissage automatique et bien plus encore. -Les notebooks Jupyter sont d'excellents tremplins pour l'analyse, y compris les apprentissage automatique. L'image `jupyterlab-cpu` donne une bonne expérience de base pour python, y compris des packages courants tels que `numpy`, `pandas` et `scikit-learn`. Si vous souhaitez spécifiquement utiliser **_TensorFlow_** ou **_PyTorch_**, nous ont également `jupyterlab-tensorflow` et `jupyterlab-pytorch` qui viennent avec ceux outils préinstallés. +Les bloc-notes Jupyter sont d'excellents tremplins pour l'analyse, y compris les apprentissage automatique. L'image `jupyterlab-cpu` donne une bonne expérience de base pour python, y compris des paquets courants tels que `numpy`, `pandas` et `scikit-learn`. Si vous souhaitez spécifiquement utiliser **_TensorFlow_** ou **_PyTorch_**, nous ont également `jupyterlab-tensorflow` et `jupyterlab-pytorch` qui viennent avec ceux outils préinstallés. -Pour l'image `jupyterlab-pytorch`, les packages PyTorch (torch, torchvision, et torchaudio) sont installés dans l'environnement conda `torch`. Vous devez activez cet environnement pour utiliser PyTorch. +Pour l'image `jupyterlab-pytorch`, les paquets PyTorch (torch, torchvision, et torchaudio) sont installés dans l'environnement conda `torch`. Vous devez activez cet environnement pour utiliser PyTorch. Pour `jupyterlab-cpu`, `jupyterlab-tensorflow` et `jupyterlab-pytorch` images, dans le shell par défaut, la commande `conda activate` peut ne pas fonctionner. C'est en raison de l'environnement qui n'est pas initialisé correctement. Dans ce cas, lancez `bash`, vous devriez voir le logo AAW et quelques instructions apparaître. Après ça `conda activate` devrait fonctionner correctement. Si vous voyez le logo AAW au démarrage signifie que l'environnement est correctement initialisé et que `conda activate` devrait fonctionner correctement. Un correctif pour ce bug est en préparation, une fois celui-ci corrigé, ce paragraphe sera supprimé. @@ -31,7 +31,7 @@ Chaque image est préchargée avec VS Code dans le navigateur si vous préférez ## RStudio -**[RStudio](../RStudio/)** vous offre un environnement de développement intégré spécifiquement pour `R`. Si vous codez en `R`, il s'agit généralement du Serveur Bloc-notesà utiliser. Utilisez l'image `rstudio` pour obtenir un environnement RStudio. +**[RStudio](../RStudio/)** vous offre un environnement de développement intégré spécifiquement pour `R`. Si vous codez en `R`, il s'agit généralement du Serveur Bloc-notes à utiliser. Utilisez l'image `rstudio` pour obtenir un environnement RStudio. ![RStudio](../images/rstudio_visual.png) diff --git a/docs/fr/2-Publication/Datasette.md b/docs/fr/2-Publication/Datasette.md index ec8472866..00fc1e8d9 100644 --- a/docs/fr/2-Publication/Datasette.md +++ b/docs/fr/2-Publication/Datasette.md @@ -26,15 +26,15 @@ Vous pouvez même explorer des cartes au sein de l'outil! # Commencer -## Installation de l'ensemble de données +## Installation de Datasette -Dans votre Jupyter Notebook, ouvrez une fenêtre de terminal et exécutez la commande `pip3 install datasette`. +Dans votre bloc-notes Jupyter, ouvrez une fenêtre de terminal et exécutez la commande `pip3 install datasette`.
- ![Installer l'ensemble de données](../images/InstallDatasette.PNG) + ![Installer Datasette](../images/InstallDatasette.PNG)
-## Démarrage de l'ensemble de données +## Démarrage de Datasette Pour afficher votre propre base de données dans votre bloc-notes Jupyter, créez un fichier appelé `start.sh` dans le répertoire de votre projet et copiez-y le code ci-dessous. Rendez le fichier exécutable en utilisant `chmod +x start.sh`. Exécutez le fichier avec `./start.sh`. Accédez au serveur Web à l'aide de l'**URL de base** avec le numéro de port que vous utilisez dans le fichier ci-dessous. diff --git a/docs/fr/2-Publication/PowerBI.md b/docs/fr/2-Publication/PowerBI.md index add2a9da9..e0fb739a7 100644 --- a/docs/fr/2-Publication/PowerBI.md +++ b/docs/fr/2-Publication/PowerBI.md @@ -6,9 +6,9 @@ Nous ne proposons pas de serveur Power BI, mais vous pouvez extraire vos donnée ![Tableau de bord sur Power BI](../images/powerbi_dashboard.png) -## Installation +# Installation -### Ce dont vous aurez besoin +## Ce dont vous aurez besoin 1. Un ordinateur avec Power BI et Python 3.6 2. Vos `ACCESS_KEY` et `SECRET_KEY` MinIO (voir [Stockage](../5-Stockage/Aperçu.md)) @@ -23,7 +23,7 @@ Assurez-vous que `pandas`, `boto3` et `numpy` sont installés et que vous utilis Ensuite, assurez-vous que Power BI utilise le bon environnement Python. Vous pouvez modifier cet élément à partir du menu des options. Le chemin d'accès exact est indiqué dans le guide de démarrage rapide. -### Modifiez votre script Python +## Modifiez votre script Python Ensuite, modifiez votre script Python pour utiliser vos `ACCESS_KEY` et `SECRET_KEY` MinIO, puis cliquez sur « Obtenir des données » et copiez-le en tant que script Python. diff --git a/docs/fr/3-Pipelines/Argo.md b/docs/fr/3-Pipelines/Argo.md index f33060ea3..4bc17cfd1 100644 --- a/docs/fr/3-Pipelines/Argo.md +++ b/docs/fr/3-Pipelines/Argo.md @@ -3,7 +3,7 @@ ![Logo calmar Argo Workflows](../images/argo.png) -**[Flux de travail Argo](https://argoproj.github.io/argo-workflows/)** est un moteur de flux de travail open source natif de conteneur pour orchestrer des tâches parallèles sur Kubernetes. Les flux de travails Argo sont implémentés en tant que Kubernetes CRD (Custom Resource Definition). Il est particulièrement adapté aux flux de travail de science des données et aux flux de travail d’apprentissage automatique. +**[Flux de travail Argo](https://argoproj.github.io/argo-workflows/)** est un moteur de flux de travail à logiciel libre natif de conteneur pour orchestrer des tâches parallèles sur Kubernetes. Les flux de travails Argo sont implémentés en tant que Kubernetes CRD (Custom Resource Definition). Il est particulièrement adapté aux flux de travail de science des données et aux flux de travail d’apprentissage automatique. La documentation complète peut être trouvée [ici](https://argoproj.github.io/argo-workflows/walk-through/). @@ -158,7 +158,7 @@ Utilisez le `Dockerfile` suivant comme point de départ pour vos projets `R` et #### 3. Écrivez votre flux de travail en YAML -YAML est encore un autre langage de balisage et vous devrez écrire les étapes de votre pipeline de formation dans un fichier YAML Argo Workflows. Ce fichier doit inclure une référence au Dockerfile que vous avez créé à l'[Étape 1](#2-ecrivez-un-dockerfile-pour-executer-votre-code), ainsi que toutes les données d'entrée et de sortie avec lesquelles vous travaillerez. +YAML est encore un autre langage de balisage et vous devrez écrire les étapes de votre pipeline de formation dans un fichier YAML de flux de travails Argo. Ce fichier doit inclure une référence au Dockerfile que vous avez créé à l'[Étape 1](#2-ecrivez-un-dockerfile-pour-executer-votre-code), ainsi que toutes les données d'entrée et de sortie avec lesquelles vous travaillerez. Voici un exemple de fichier YAML pour un pipeline d'apprentissage automatique simple qui entraîne un modèle de régression logistique sur l'ensemble de données iris. La seule vraie différence entre les versions `Python` et `R` est la commande `command: ["python", "train.py"]` vs `command: ["Rscript", "train.R"]` et le les modèles sont stockés dans différents formats, `pkl` pour `python` et `rds` pour `R`. @@ -219,11 +219,11 @@ Pour exécuter le flux de travail ci-dessus, vous devrez d'abord envoyer le Dock $ argo submit workflow.yaml # soumettre une spécification de flux de travail à Kubernetes ``` -Il est également possible de soumettre des workflows argo à partir des workflows à l'aide de SDK et d'appels API (c'est juste un autre service Web !). Voir cette [section](#exemples-utilisant-des-sdk-bases-sur-flux-de-travail-argo). +Il est également possible de soumettre des flox de travail argo à partir des flox de travail à l'aide de SDK et d'appels API (c'est juste un autre service Web !). Voir cette [section](#exemples-utilisant-des-sdk-bases-sur-flux-de-travail-argo). #### 5. Surveillez le pipeline à l'aide de la CLI du flux de travail Argo -Pendant l'exécution du pipeline, vous pouvez surveiller sa progression à l'aide de la CLI Argo Workflows. Cela vous montrera quelles étapes se sont terminées avec succès et lesquelles sont toujours en cours. Vous trouverez ci-dessous quelques commandes utiles. Pour plus d'informations sur la CLI Argo Workflows, veuillez consulter [la documentation officielle de la CLI Argo Workflows](https://argoproj.github.io/argo-workflows/walk-through/argo-cli/) . +Pendant l'exécution du pipeline, vous pouvez surveiller sa progression à l'aide de la CLI de flux de travail Argo. Cela vous montrera quelles étapes se sont terminées avec succès et lesquelles sont toujours en cours. Vous trouverez ci-dessous quelques commandes utiles. Pour plus d'informations sur la CLI des flux de travail Argo, veuillez consulter [la documentation officielle de la CLI de flux de travail Argo](https://argoproj.github.io/argo-workflows/walk-through/argo-cli/) . ``` bash title="Terminal" $ argo list # lister les flux de travail actuels @@ -379,5 +379,5 @@ Couler fournit une interface de programmation d'applications simple et unifiée Des exemples de flux de travail Argo peuvent être trouvés dans les référentiels Github suivants : -- [Documentation des workflows Argo](https://argoproj.github.io/argo-workflows/) +- [Documentation des flux de travail Argo](https://argoproj.github.io/argo-workflows/) - [Référence Argo CLI](https://argoproj.github.io/argo-workflows/walk-through/argo-cli/) diff --git a/docs/fr/4-Collaboration/Demander-EspaceDeNom.md b/docs/fr/4-Collaboration/Demander-EspaceDeNom.md index ba6abbce2..c53461c82 100644 --- a/docs/fr/4-Collaboration/Demander-EspaceDeNom.md +++ b/docs/fr/4-Collaboration/Demander-EspaceDeNom.md @@ -16,6 +16,6 @@ Pour créer un espace de noms pour une équipe, accédez au portail ETAA. **Cliq **Vous recevrez une notification par e-mail lorsque l'espace de noms sera créé.** Une fois l'espace de noms partagé créé, vous pouvez y accéder de la même manière que n'importe quel autre espace de noms dont vous disposez via l'interface utilisateur de Kubeflow, comme illustré ci-dessous. Vous serez alors capable de [partager et gérer](Aperçu.md#partager-le-calcul-espace-de-nom-dans-kubeflow)) à votre espace de nom. -Pour changer d'espace de noms, jetez un œil en haut de votre fenêtre, juste à droite du logo Kubeflow. +Pour changer d'espace de noms, regardez en haut de votre fenêtre, juste à droite du logo Kubeflow. ![Select your Namespace](../images/kubeflow_manage_contributors.png) diff --git a/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md b/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md index 783f763bf..b66ffe764 100644 --- a/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md +++ b/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md @@ -38,7 +38,7 @@ Par exemple, dans ETAA et l'utilisation de [Bloc-notes Jupyter](../../1-Experien conda install -c https://jfrog.aaw.cloud.statcan.ca/artifactory/api/conda/esri-remote arcgis ``` -2. Importez les librairies nécessaires dont vous aurez besoin dans le bloc-note. +2. Importez les bibliothèques nécessaires dont vous aurez besoin dans le bloc-note. ```python from arcgis.gis import GIS from arcgis.gis import Item @@ -157,7 +157,7 @@ item1_buffer.update(item_properties={'title': 'Saisir le titre' ### Visualisez vos données sur une carte interactive -**Exemple : Librairie MatplotLib** +**Exemple : bibliothèque MatplotLib** Dans le code ci-dessous, nous créons un objet ax, qui est un tracé de style carte. Nous traçons ensuite notre colonne de changement de données ("Population Change") sur les axes ```python import matplotlib.pyplot as plt @@ -166,9 +166,9 @@ shape.plot(ax=ax, column='Population Change', legend=True) plt.show() ``` -**Exemple : librairie ipyleaflet** -Dans cet exemple, nous utiliserons la librairie `ipyleaflet` pour créer une carte interactive. Cette carte sera centrée autour de Toronto, ON. Les données utilisées seront décrites ci-dessous. -Commencez par coller ```conda install -c conda-forge ipyleaflet``` vous permettant d'installer les librairies ipyleaflet dans l'environnement Python. +**Exemple : bibliothèque ipyleaflet** +Dans cet exemple, nous utiliserons la bibliothèque `ipyleaflet` pour créer une carte interactive. Cette carte sera centrée autour de Toronto, ON. Les données utilisées seront décrites ci-dessous. +Commencez par coller ```conda install -c conda-forge ipyleaflet``` vous permettant d'installer les bibliothèques ipyleaflet dans l'environnement Python.
@@ -191,5 +191,5 @@ toronto_map ## En savoir plus sur l'API ArcGIS pour Python [La documentation complète de l'API ArcGIS peut être trouvée ici](https://developers.arcgis.com/python/) -##E n savoir plus sur l'environnement analytique géospatial (GAE) et les services SAD +##En savoir plus sur l'environnement analytique géospatial (GAE) et les services SAD [Guide d'aide GAE](https://statcan.github.io/gae-eag/) From a7cf4b25a7d887473e764e0f88f6f5fd0149b99e Mon Sep 17 00:00:00 2001 From: Wendy V Gaultier Date: Thu, 2 Nov 2023 13:13:10 +0000 Subject: [PATCH 13/15] Fix errors --- docs/fr/1-Experiences/Kubeflow.md | 4 ++-- docs/fr/3-Pipelines/Argo.md | 2 +- docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/fr/1-Experiences/Kubeflow.md b/docs/fr/1-Experiences/Kubeflow.md index 2eb7ac396..85b4719c7 100644 --- a/docs/fr/1-Experiences/Kubeflow.md +++ b/docs/fr/1-Experiences/Kubeflow.md @@ -50,7 +50,7 @@ votre équipe. ## Image -Vous devrez choisir une image. Il existe JupyterLab, RStudio, Ubureau à distance ubuntu et SAS disponibles. L'image SAS est disponible uniquement pour les employés de StatCan (en raison des limitations de licence), les autres sont disponibles pour tout le monde. Sélectionnez le menu déroulant pour sélectionner des options supplémentaires parmi celles-ci (par exemple les images CPU, PyTorch et TensorFlow pour JupyterLab). +Vous devrez choisir une image. Il existe JupyterLab, RStudio, bureau à distance Ubuntu et SAS disponibles. L'image SAS est disponible uniquement pour les employés de StatCan (en raison des limitations de licence), les autres sont disponibles pour tout le monde. Sélectionnez le menu déroulant pour sélectionner des options supplémentaires parmi celles-ci (par exemple les images CPU, PyTorch et TensorFlow pour JupyterLab). Vérifiez le nom des images et choisissez-en une qui correspond à ce que vous souhaitez faire. Je ne sais pas lequel choisir ? Découvrez vos options [ici](../Selectionner-une-image). @@ -176,5 +176,5 @@ Les éléments suivants peuvent être personnalisés ici : ## Partagez votre espace de travail -Dans Kubeflow, chaque utilisateur dispose d'un **espace de noms** qui contient son travail (son serveurs blocs-note, pipelines, disques, etc.). Votre espace de nom vous appartient, mais peut être partagé si vous souhaitez collaborer avec d'autres. **Pour plus de détails sur collaboration sur la plateforme, voir +Dans Kubeflow, chaque utilisateur dispose d'un **espace de noms** qui contient son travail (ses serveurs blocs-note, pipelines, disques, etc.). Votre espace de nom vous appartient, mais peut être partagé si vous souhaitez collaborer avec d'autres. **Pour plus de détails sur collaboration sur la plateforme, voir [Collaboration](../4-Collaboration/Aperçu.md).** diff --git a/docs/fr/3-Pipelines/Argo.md b/docs/fr/3-Pipelines/Argo.md index 4bc17cfd1..8f69c7f23 100644 --- a/docs/fr/3-Pipelines/Argo.md +++ b/docs/fr/3-Pipelines/Argo.md @@ -219,7 +219,7 @@ Pour exécuter le flux de travail ci-dessus, vous devrez d'abord envoyer le Dock $ argo submit workflow.yaml # soumettre une spécification de flux de travail à Kubernetes ``` -Il est également possible de soumettre des flox de travail argo à partir des flox de travail à l'aide de SDK et d'appels API (c'est juste un autre service Web !). Voir cette [section](#exemples-utilisant-des-sdk-bases-sur-flux-de-travail-argo). +Il est également possible de soumettre des flux de travail argo à partir des flux de travail à l'aide de SDK et d'appels API (c'est juste un autre service Web !). Voir cette [section](#exemples-utilisant-des-sdk-bases-sur-flux-de-travail-argo). #### 5. Surveillez le pipeline à l'aide de la CLI du flux de travail Argo diff --git a/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md b/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md index b66ffe764..9bdba1f09 100644 --- a/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md +++ b/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md @@ -191,5 +191,5 @@ toronto_map ## En savoir plus sur l'API ArcGIS pour Python [La documentation complète de l'API ArcGIS peut être trouvée ici](https://developers.arcgis.com/python/) -##En savoir plus sur l'environnement analytique géospatial (GAE) et les services SAD +## En savoir plus sur l'environnement analytique géospatial (GAE) et les services SAD [Guide d'aide GAE](https://statcan.github.io/gae-eag/) From 58b5bbe9b870daa39924fbe9109f7a7f7422ba10 Mon Sep 17 00:00:00 2001 From: Wendy V Gaultier Date: Thu, 2 Nov 2023 13:27:13 +0000 Subject: [PATCH 14/15] French corrections --- docs/fr/1-Experiences/Kubeflow.md | 3 +-- docs/fr/3-Pipelines/Argo.md | 8 ++++---- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/docs/fr/1-Experiences/Kubeflow.md b/docs/fr/1-Experiences/Kubeflow.md index 85b4719c7..cfd313b76 100644 --- a/docs/fr/1-Experiences/Kubeflow.md +++ b/docs/fr/1-Experiences/Kubeflow.md @@ -93,8 +93,7 @@ Dans les options avancées, vous pouvez sélectionner une limite supérieure au ## GPUs -Si vous voulez un serveur GPU, sélectionnez « 1 » comme nombre de GPU et « NVIDIA » comme GPU -fournisseur (le bouton de création sera grisé jusqu'à ce que le fournisseur de GPU soit sélectionné si +Si vous voulez un serveur GPU, sélectionnez « 1 » comme nombre de GPU et « NVIDIA » comme fournisseur de GPU (le bouton de création sera grisé jusqu'à ce que le fournisseur de GPU soit sélectionné si vous avez un GPU spécifié). Les serveurs multi-GPU sont actuellement pris en charge sur le système ETAA uniquement sur une base spéciale sur demande, veuillez contacter les mainteneurs d'ETAA si vous souhaitez un serveur multi-GPU. diff --git a/docs/fr/3-Pipelines/Argo.md b/docs/fr/3-Pipelines/Argo.md index 8f69c7f23..46211b1ad 100644 --- a/docs/fr/3-Pipelines/Argo.md +++ b/docs/fr/3-Pipelines/Argo.md @@ -1,7 +1,7 @@ ## Flux de travail Argo -![Logo calmar Argo Workflows](../images/argo.png) +![Logo calmar de flux de travail Argo](../images/argo.png) **[Flux de travail Argo](https://argoproj.github.io/argo-workflows/)** est un moteur de flux de travail à logiciel libre natif de conteneur pour orchestrer des tâches parallèles sur Kubernetes. Les flux de travails Argo sont implémentés en tant que Kubernetes CRD (Custom Resource Definition). Il est particulièrement adapté aux flux de travail de science des données et aux flux de travail d’apprentissage automatique. @@ -9,7 +9,7 @@ La documentation complète peut être trouvée [ici](https://argoproj.github.io/ Les flux de travails Argo ont les avantages suivants: -- Les tâches de workflow peuvent être définies sous forme de scripts (ex. Python) ou être conteneurisées (ex. Docker). +- Les tâches des flux de travail peuvent être définies sous forme de scripts (ex. Python) ou être conteneurisées (ex. Docker). - Des flux de travail complexes peuvent être modélisés à l'aide de graphes acycliques dirigés (DAG) pour capturer les chaînes de dépendance. - Les tâches indépendantes peuvent être exécutées en parallèle avec une granularité jusqu'au niveau de mise en œuvre, réduisant ainsi les charges de tâches chronophages. - Agnositique de la plateforme Kubernetes, ce qui signifie que votre travail est très portable. @@ -160,7 +160,7 @@ Utilisez le `Dockerfile` suivant comme point de départ pour vos projets `R` et YAML est encore un autre langage de balisage et vous devrez écrire les étapes de votre pipeline de formation dans un fichier YAML de flux de travails Argo. Ce fichier doit inclure une référence au Dockerfile que vous avez créé à l'[Étape 1](#2-ecrivez-un-dockerfile-pour-executer-votre-code), ainsi que toutes les données d'entrée et de sortie avec lesquelles vous travaillerez. -Voici un exemple de fichier YAML pour un pipeline d'apprentissage automatique simple qui entraîne un modèle de régression logistique sur l'ensemble de données iris. La seule vraie différence entre les versions `Python` et `R` est la commande `command: ["python", "train.py"]` vs `command: ["Rscript", "train.R"]` et le les modèles sont stockés dans différents formats, `pkl` pour `python` et `rds` pour `R`. +Voici un exemple de fichier YAML pour une pipeline d'apprentissage automatique simple qui entraîne un modèle de régression logistique sur l'ensemble de données iris. La seule vraie différence entre les versions `Python` et `R` est la commande `command: ["python", "train.py"]` vs `command: ["Rscript", "train.R"]` et le les modèles sont stockés dans différents formats, `pkl` pour `python` et `rds` pour `R`. Le fichier YAML définit une seule étape appelée `train` qui exécute un script appelé `train.py` ou `train.R` dans l'image Docker `machine-learning:v1`. Le script prend un fichier d'ensemble de données d'entrée, spécifié par un paramètre appelé `dataset`, et génère un fichier de modèle entraîné vers un artefact de sortie appelé `model.pkl` ou `model.rds` selon le langage utilisé. @@ -292,7 +292,7 @@ Couler fournit une interface de programmation d'applications simple et unifiée from couler.argo_submitter import ArgoSubmitter - # Définir les étapes (fonctions) utilisées dans le workflow + # Définir les étapes (fonctions) utilisées dans le flux de travail def random_code(): import random res = "heads" if random.randint(0, 1) == 0 else "tails" From e8a1d46054ba743c162d7be689b53ba598f990ca Mon Sep 17 00:00:00 2001 From: Wendy V Gaultier Date: Thu, 2 Nov 2023 13:56:07 +0000 Subject: [PATCH 15/15] Correct masculine pipeline --- docs/fr/3-Pipelines/Argo.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/fr/3-Pipelines/Argo.md b/docs/fr/3-Pipelines/Argo.md index 46211b1ad..51197c772 100644 --- a/docs/fr/3-Pipelines/Argo.md +++ b/docs/fr/3-Pipelines/Argo.md @@ -160,7 +160,7 @@ Utilisez le `Dockerfile` suivant comme point de départ pour vos projets `R` et YAML est encore un autre langage de balisage et vous devrez écrire les étapes de votre pipeline de formation dans un fichier YAML de flux de travails Argo. Ce fichier doit inclure une référence au Dockerfile que vous avez créé à l'[Étape 1](#2-ecrivez-un-dockerfile-pour-executer-votre-code), ainsi que toutes les données d'entrée et de sortie avec lesquelles vous travaillerez. -Voici un exemple de fichier YAML pour une pipeline d'apprentissage automatique simple qui entraîne un modèle de régression logistique sur l'ensemble de données iris. La seule vraie différence entre les versions `Python` et `R` est la commande `command: ["python", "train.py"]` vs `command: ["Rscript", "train.R"]` et le les modèles sont stockés dans différents formats, `pkl` pour `python` et `rds` pour `R`. +Voici un exemple de fichier YAML pour un pipeline d'apprentissage automatique simple qui entraîne un modèle de régression logistique sur l'ensemble de données iris. La seule vraie différence entre les versions `Python` et `R` est la commande `command: ["python", "train.py"]` vs `command: ["Rscript", "train.R"]` et le les modèles sont stockés dans différents formats, `pkl` pour `python` et `rds` pour `R`. Le fichier YAML définit une seule étape appelée `train` qui exécute un script appelé `train.py` ou `train.R` dans l'image Docker `machine-learning:v1`. Le script prend un fichier d'ensemble de données d'entrée, spécifié par un paramètre appelé `dataset`, et génère un fichier de modèle entraîné vers un artefact de sortie appelé `model.pkl` ou `model.rds` selon le langage utilisé. @@ -235,7 +235,7 @@ $ argo delete workflow-xxx # suprimer un flux de travail #### 6. Récupérer le modèle entraîné -Une fois la pipeline terminé, vous pouvez récupérer les données de sortie à l'aide de la commande argo logs ou en affichant les artefacts de sortie à l'aide de la CLI, c'est-à-dire accéder au répertoire que vous avez spécifié dans votre script et localiser le fichier `model.pkl` ou `model.rds`. L'extrait de code suivant, extrait du [script de formation ci-dessus](#1-ecrivez-un-script-pour-entrainer-votre-modele), indique au langage de programmation respectif où enregistrer le modèle entraîné. +Une fois le pipeline terminé, vous pouvez récupérer les données de sortie à l'aide de la commande argo logs ou en affichant les artefacts de sortie à l'aide de la CLI, c'est-à-dire accéder au répertoire que vous avez spécifié dans votre script et localiser le fichier `model.pkl` ou `model.rds`. L'extrait de code suivant, extrait du [script de formation ci-dessus](#1-ecrivez-un-script-pour-entrainer-votre-modele), indique au langage de programmation respectif où enregistrer le modèle entraîné. === "Python"

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