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HOW-TO

Run a benchmark

see README for basic commands.

Custom configuration

Default configuration properties are all defined and described in the resources/config.yaml file.

To override those configurations, create your custom config.yaml file under the user_dir . The application will automatically load this custom file and apply it on top of the defaults.

Example of config.yaml:

---
input_dir: ~/data   # change the default input directory (where data files are loaded and/or downloaded).

frameworks:
  definition_file:  # this allows to add custom framework definitions (in {user}/frameworks.yaml) on top of the default ones.
    - '{root}/resources/frameworks.yaml'
    - '{user}/frameworks.yaml'

benchmarks:
  definition_dir:  # this allows to add custom benchmark definitions (under {user}/benchmarks) to the default ones.
    - '{user}/benchmarks'
    - '{root}/resources/benchmarks'
  constraints_file: # this allows to add custom constraint definitions (in {user}/constraints.yaml) on top of the default ones.
    - '{root}/resources/constraints.yaml'
    - '{user}/constraints.yaml'

aws:
  resource_files:  # this allows to automatically upload custom config + frameworks to the running instance (benchmark files are always uploaded).
    - '{user}/config.yaml'
    - '{user}/frameworks.yaml'
    - '{user}/extensions'

  use_docker: true  # you can decide to always use the prebuilt docker images on AWS.

Note: configurations support the following placeholders:

  • {input}: replaced by the value of config input_dir. Folder from which datasets are loaded (and/or downloaded) by default. Defaults to ~/.openml/cache, but can also be overridden in a custom config.yaml or at the command line using -i or --indir.
  • {output}: replaced by the value of config output_dir. Folder where all outputs (results, logs, predictions...) will be stored. Defaults to ./results, but can also be overridden in a custom config.yaml or at the command line using -o or --outdir.
  • {user}: replaced by the value of config user_dir. Folder containing customizations (config.yaml, benchmark definitions, framework definitions...). Defaults to ~/.config/automlbenchmark, but can be overridden at the command line using -u or --userdir.
  • {root}: replaced by the value of config root_dir. The root folder of the automlbenchmark application: this is detected at runtime.

Run a framework with different (hyper-)parameters

Framework definitions accept a params dictionary for pass-through parameters, i.e. parameters that are directly accessible from the exec.py file in the framework integration executing the AutoML training.

Example:

In the definition below, the n_estimators and verbose params are passed directly to the RandomForestClassifier

RandomForest_custom:
  extends: RandomForest
  params:
    n_estimators: 2000
    verbose: true

NOTE: by convention, param names starting with _ are filtered out (they are not passed to the classifier/regressor) but are used for custom logic in the exec.py.

Example:

In the definition below, the _n_jobs param is handled by custom code in RandomForest/exec.py: here it overrides the default n_jobs automatically calculated by the application (using all assigned cores).

RandomForest_custom:
  extends: RandomForest
  params:
    n_estimators: 2000
    _n_jobs: 1

Add a benchmark

In this section, benchmark means a suite of datasets that can be used to feed any of the available frameworks, in combination with a set of constraints (time limit, cpus, memory) enforced by the application.

A benchmark definition will then consist in a datasets definition and a constraints definition.

Each dataset must contain a training set and a test set. There can be multiple training/test splits, in which case each split is named a fold, so that the same dataset can be benchmarked multiple times using a different fold.

Datasets definition

A dataset definition consists in a yaml file listing all the task/datasets that will used for the complete benchmark, or as an OpenML suite.

Default dataset definitions are available under folder resources/benchmarks.

Each task/dataset must have a name that should be unique (ignoring case) in the given definition file, it will also be used as an identifier, for example in the results.

This name can also be used on the command line (-t or --task argument) when we just want to execute a subset of the benchmark, often in combination with a specific fold (-f or --fold argument):

python runbenchmark.py randomforest validation -t bioresponse
python runbenchmark.py randomforest validation -t bioresponse eucalyptus
python runbenchmark.py randomforest validation -t bioresponse -f 0
python runbenchmark.py randomforest validation -t bioresponse eucalyptus -f 0 1 2

Example:

Following the custom configuration, it is possible to override and/or add custom benchmark definitions by creating for example a mybenchmark.yaml file in your user_dir/benchmarks.

The benchmark can then be tested and then executed using the 1h4c constraint:

python runbenchmark.py randomforest mybenchmark
python runbenchmark.py randomforest mybenchmark 1h4c

OpenML datasets

OpenML datasets are verified and annotated datasets, making them easy to consume. However, the application doesn't directly consume those datasets today as the split between training data and test data is not immediately available. For this we use OpenML tasks.

OpenML tasks

OpenML tasks provide ready to use datasets, usually split in 10-folds: for each fold, we have 1 training set and a test set.

The automlbenchmark application can directly consume those tasks using the following definition:

- name: bioresponse
  openml_task_id: 9910

where openml_task_id allows accessing the OpenML task at https://www.openml.org/t/{openml_task_id} (in this example: https://www.openml.org/t/9910).

Alternatively, you can run the benchmark on a single OpenML task without writing a benchmark definition:

python runbenchmark.py randomforest openml/t/59

File datasets

It is also possible to benchmark your own datasets, as soon as they follow some requirements:

  • The data files should be in one of the currently supported format: ARFF, CSV (ideally with header).
  • Each dataset must contain at least one file for training data and one file for test data.
  • If the dataset is represented as an archive (.zip, .tar, .tgz, .tbz) or a directory, then the data files must follow this naming convention to be detected correctly:
    • if there's only one file for training and one for test, they should be named {name}_train.csv and {name}_test.csv (in case of CSV files).
    • if there are multiple folds, they should follow a similar convention: {name}_train_0.csv, {name}_test_0.csv``, {name}_train_1.csv, {name}_test_1.csv, ...

Example:

Then the datasets can be declared in the benchmark definition file as follow:

---

- name: example_csv
  dataset:
    train: /path/to/data/ExampleTraining.csv
    test:  /path/to/data/ExampleTest.csv
    target: TargetColumn
  folds: 1

- name: example_multi_folds
  dataset:
    train: 
      - /path/to/data/ExampleTraining_0.csv
      - /path/to/data/ExampleTraining_1.csv
    test:  
      - /path/to/data/ExampleTest_0.csv
      - /path/to/data/ExampleTest_1.csv
    target: TargetColumn
  folds: 2

- name: example_dir   # let's assume that the data folder contains 2 files: example_train.arff and example_test.arff
  dataset: 
    path: /path/to/data
    target: TargetColumn
  folds: 1

- name: example_dir_multi_folds   # let's assume that the data folder contains 6 files: example_train_0.arff, ..., example_train_2.arff, example_test_0.arff, ...
  dataset: 
    path: /path/to/data
    target: TargetColumn
  folds: 3

- name: example_archive  # let's assume that archive contains the same files as for example_dir_multi_folds
  dataset:
    path: /path/to/archive.zip
    target: TargetColumn
  folds: 3

- name: example_csv_http
  dataset:
    train: https://my.domain.org/data/ExampleTraining.csv
    test:  https://my.domain.org/data/ExampleTest.csv
    target: TargetColumn
  folds: 1

- name: example_archive_http
  dataset:
    path: https://my.domain.org/data/archive.tgz
    target: TargetColumn
  folds: 3

- name: example_autodetect
  dataset: /path/to/data/folder

- name: example_relative_to_input_dir
  dataset: "{input}/data/folder"

- name: example_relative_to_user_dir
  dataset:
    train: "{user}/data/train.csv"
    test: "{user}/data/test.csv"

Note:

  • the naming convention is required only when path is pointing to a directory or an archive. If the files are listed explicitly, there's no constraint on the file names.
  • the target attribute is optional but recommended, otherwise the application will try to autodetect the target: 0. looking for a column named target or class. 0. using the last column as a fallback.
  • the folds attribute is also optional but recommended for those datasets as the default value is folds=10 (default amount of folds in openml datasets), so if you don't have that many folds for your custom datasets, it is better to declare it explicitly here.
  • Remote files are downloaded to the input_dir folder and archives are decompressed there as well, so you may want to change the value of this folder in your custom config.yaml file or specify it at the command line with the -i or --indir argument (by default, it points to the ~/.openml/cache folder).

OpenML suites

OpenML suites are a collection of OpenML tasks, for example https://www.openml.org/s/218. You can run the benchmark on an openml suite directly, without defining the benchmark in a local file:

python runbenchmark.py randomforest openml/s/218

You can define a new OpenML suite yourself, for example through the Python API. This openml-python tutorial explains how to build your own suite. An advantage of using an OpenML suite is that sharing it is easy as the suite and its datasets can be accessed through APIs in many programming languages.

Constraints definition

Now that we have defined a list of datasets, we also need to enforce some constraints on the autoML training.

Default constraint definitions are available in resources/constraint.yaml. When no constraint is specified at the command line, the test constraint definition is used by default.

THe application supports the following constraints:

  • folds (default=10): tell the number of tasks that will be created by default for each dataset of the benchmark. For example, if all datasets support 10 folds, setting a constraint folds: 2 will create a task only for the first 2 folds by default.
  • max_runtime_seconds (default=3600): maximum time assigned for each individual benchmark task. This parameter is usually passed directly to the framework: if it doesn't respect the constraint, the application will abort the task after 2 * max_runtime_seconds. In any case, the real task running time is always available in the results.
  • cores (default=-1): amount of cores used for each automl task. If <= 0, it will try to use all cores.
  • max_mem_size_mb (default=-1): amount of memory assigned to each automl task. If <= 0, then the amount of memory is computed from os available memory.
  • min_vol_size_mb (default=-1): minimum amount of free space required on the volume. If <= 0, skips verification. If the requirement is not fulfilled, a warning message will be printed, but the task will still be attempted.

Example:

Following the custom configuration, it is possible to override and/or add constraints by creating the following constraints.yaml file in your user_dir:

---

test:
  folds: 1
  max_runtime_seconds: 120

1h16c:
  folds: 10
  max_runtime_seconds: 3600
  cores: 16

1h32c:
  folds: 10
  max_runtime_seconds: 3600
  cores: 32

4h16c:
  folds: 10
  max_runtime_seconds: 14400
  cores: 16
  min_vol_size_mb: 65536

8h16c:
  folds: 10
  max_runtime_seconds: 28800
  cores: 16
  min_vol_size_mb: 65536

The new constraints can now be passed on the command line when executing the benchmark:

python runbenchmark.py randomforest validation 1h16c

Add an AutoML framework

Adding an AutoML framework consist in several steps:

  1. create a Python module that will contain everything related to the integration of this framework.
  2. define the framework in a Framework definition file.
  3. write some integration code
  • to download/setup the framework dynamically: by convention, this is done by a setup.sh script defined in the module.
  • to run the framework using the data and constraints/parameters provided by the benchmark application: by convention, this is done by an exec.py script in the module, but it may require more files depending on the framework, for example if it runs on Python or R, Java...

Framework definition

The framework definition consists in an entry in a yaml file with the framework name and some properties

  1. to describe the framework and define which version will be used: project, version.
  2. to indicate the Python module with the integration code: module or extends.
  3. to pass optional parameters to the framework and/or the integration code: params.

Default framework definitions are defined in file resources/frameworks.yaml.

Following the custom configuration, it is possible to override and/or add a framework definitions by creating a frameworks.yaml file in your user_dir.

See for example the examples/custom/frameworks.yaml:

---

GradientBoosting:
  module: extensions.GradientBoosting
  version: '0.19.2'
  project: https://scikit-learn.org/stable/modules/ensemble.html#gradient-boosting
  params:
    n_estimators: 500

Stacking:
  module: extensions.Stacking
  version: '0.22.1'
  project: https://scikit-learn.org/stable/modules/ensemble.html#stacking
  params:
    _rf_params: {n_estimators: 200}
    _gbm_params: {n_estimators: 200}
    _linear_params: {penalty: elasticnet, loss: log}
#    _svc_params: {tol: 1e-3, max_iter: 1e5}
#    _final_params: {penalty: elasticnet, loss: log} # sgd linear
    _final_params: {max_iter: 1000}  # logistic/linear

autosklearn_latest:
  extends: autosklearn
  version: latest
  description: "this will use master branch from the autosklearn repository instead of the fixed version"

autosklearn_mybranch:
  extends: autosklearn
  version: mybranch
  description: "this will use mybranch branch from the autosklearn repository instead of the fixed version"

autosklearn_oldgen:
  extends: autosklearn
  version: "0.7.1"
  description: "this will use the latest autosklearn version from the old generation"

H2OAutoML_nightly:
  module: frameworks.H2OAutoML
  setup_cmd: 'LATEST_H2O=`curl http://h2o-release.s3.amazonaws.com/h2o/master/latest` && pip install --no-cache-dir -U "http://h2o-release.s3.amazonaws.com/h2o/master/${{LATEST_H2O}}/Python/h2o-3.29.0.${{LATEST_H2O}}-py2.py3-none-any.whl"'
  version: 'nightly'

H2OAutoML_custom:
  extends: H2OAutoML
  params:
    nfolds: 3
    stopping_tolerance: 0.05

This example shows

  • the definitions for 2 new frameworks: GradientBoosting and Stacking.
    • Those definitions (optionally) externalize some parameters (e.g. n_estimators): the params property always appears in json format in the results, so that we can clearly see what has been tuned when analyzing the results later.
    • Note that the module is case sensitive and should point to the module containing the integration code.
    • The application will search for modules from the sys path, which includes the application root_dir and the user_dir:
      • that's why the default frameworks use module: frameworks.autosklearn for example,
      • and the example above can use module: extensions.GradientBoosting because those examples must be run by setting the user_dir to examples/config, e.g.

        python runbenchmark.py gradientboosting -u examples/custom.

  • a custom definition (H2OAutoML_nightly) for the existing frameworks.H2OAutoML module, allowing to reuse the module for a dynamic version of the module:
    • the setup_cmd is executed after the default setup of the module, so it can be used to make additional setup. To customize the setup, it is possible to use:
      • setup_args: my_version (only if the setup.sh in the framework module supports new arguments).
      • setup_cmd (as shown in this example).
      • setup_script: my_additional_setup.sh.
  • 2 custom definitions (H2OAutoML_blending and H2OAutoML_custom) simply extending the existing H2OAutoML definition (therefore inheriting from all its properties, including the module one), but overriding the params property, thus allowing to provide multiple "flavours" of the same framework.

The frameworks defined in this example can then be used like any other framework as soon as both the framework module and the definition file are made available to the application: in our case, this is done by the creation of the integration modules under examples/custom/extensions and by exposing the definitions in examples/custom/frameworks.yaml thanks to the entry in examples/custom/config.yaml:

frameworks:
  definition_file:  # this allows to add custom framework definitions (in {user}/frameworks.yaml) on top of the default ones.
    - '{root}/resources/frameworks.yaml'
    - '{user}/frameworks.yaml'

By pointing the user_dir to examples/custom, our config.yaml is also loaded, and we can use the new frameworks:

python runbenchmark.py gradientboosting -u examples/custom
python runbenchmark.py stacking -u examples/custom
python runbenchmark.py h2oautoml_blending -u examples/custom

Note:

By default, when generating a docker image, the image name is created as automlbenchmark/{framework}:{version}-{branch} with the framework name in lowercase, and branch being the branch of the automlbenchmark app (usually stable). However, it is possible to customize this image name as follow:

MyFramework:
  version: 1.0
  module: extensions.MyFramework
  docker:
    author: my_docker_repo
    image: my_image
    tag: my_tag

which will result in the docker image name my_docker_repo/my_image:my_tag-{branch}, with branch still being the branch of the application.

Framework integration

If the framework definition allows to use the new framework from the application, the (not so) hard part is to integrate it.

There are already several frameworks already integrated under frameworks directory (+ the examples under examples/custom), so the best starting point when adding a new framework is to first look at the existing ones.

Among the existing frameworks, we can see different type of integrations:

  • trivial integration: these are frameworks running on Python and using dependencies (numpy, sklearn) already required by the application itself. These are not really AutoML toolkits, but rather integrations using sklearn to provide a reference when analyzing the results: cf. constantpredictor, DecisionTree.
  • Python API integration: these are frameworks that can be run directly from Python: cf. autosklearn, H2OAutoML, TPOT, RandomForest, TunedRandomForest.
    • contrary to the trivial integration, those require a setup phase.
    • Most of them currently run using the same dependencies as the application, which is not recommended due to potential version conflicts (especially with sklearn). This was not a major constraint with the first frameworks implemented, but now, those integrations can and will be slightly changed to run in their dedicated virtual environment, using their own dependencies: cf. RandomForest and examples/custom/extensions/Stacking for examples.
  • non-Python frameworks: those frameworks typically run in R or Java and don't provide any Python API. The integration is then still done by spawning the R or Java process from the exec.py: cf. AutoWEKA, autoxgboost.

Recommended structure

By convention, the integration is done using the following structure:

frameworks/autosklearn/
|-- __init__.py
|-- exec.py
|-- requirements.txt
`-- setup.sh

Please note however, that this structure is not a requirement, the only requirement is the contract exposed by the integration module itself, i.e. by the __init__.py file.

A simple __init__.py would look like this:

from amlb.utils import call_script_in_same_dir


def setup(*args, **kwargs):
    call_script_in_same_dir(__file__, "setup.sh", *args, **kwargs)


def run(*args, **kwargs):
    from .exec import run
    return run(*args, **kwargs)

where we see that the module should expose (only run is actually required) the following functions:

  • setup (optional): called by the application to setup the given framework, usually by simply running a setup.sh script that will be responsible for potentially creating a local virtual env, downloading and installing the dependencies. The setup function can also receive the optional setup_args param from the framework definition as an argument.
  • run: called by the benchmark application to execute a task against the framework, using the selected dataset and constraints. We will describe the parameters in detail below, for now, just note that by convention, we just load the exec.py file from the module and delegate the execution to its run function.
  • docker_commands (optional): called by the application to collect docker instructions that are specific to the framework. If the framework requires a setup phase, then the string returned by this function should at least ensure that the setup is also executed during the docker image creation, that's one reason why it is preferable to do all the setup in a setup.sh script, to allow the docker support above.

Frameworks with Python API

Frameworks requiring a dedicated virtual env

For frameworks with Python API, we may worry about version conflicts between the packages used by the application (e.g. sklearn, numpy, pandas) and the ones required by the framework.

In this case, the integration is slightly different as you can see with the RandomForest integration allowing to use any version of sklearn.

This is the basic structure after the creation of the dedicated Python virtual environment during setup:

frameworks/RandomForest/
|-- __init__.py
|-- exec.py
|-- requirements.txt
|-- setup.sh
`-- venv/
    `-- (this local virtual env is created by the frameworks/shared/setup.sh)

Noticeable differences with a basic integration:

  • the venv is created in setup.sh by passing the current dir when sourcing the shared/setup.sh script: . $HERE/../shared/setup.sh $HERE.
  • the run function in __init__py prepares the data (in the application environment) before executing the exec.py in the dedicated venv. The call to run_in_venv is in charge of serializing the input, calling exec.py and deserializing + saving the results from exec.
  • exec.py, when calls in the subprocess (function __main__), calls call_run(run) which deserializes the input (dataset + config) and passes it to the run function that just need to return a result object.

Note:

As the serialization/deserialization of numpy arrays can be costly for very large datasets, it is recommended to use dataset serialization only if the framework itself doesn't support loading datasets from files.

This means that, in the __init__.py instead of implementing run as:

X_train_enc, X_test_enc = impute(dataset.train.X_enc, dataset.test.X_enc)
data = dict(
    train=dict(
        X_enc=X_train_enc,
        y_enc=dataset.train.y_enc
    ),
    test=dict(
        X_enc=X_test_enc,
        y_enc=dataset.test.y_enc
    )
)

return run_in_venv(__file__, "exec.py",
                   input_data=data, dataset=dataset, config=config)

it could simply expose the dataset paths (the application avoids loading the data if not explicitly needed by the framework):

data = dict(
    target=dict(name=dataset.target.name),
    train=dict(path=dataset.train.path),
    test=dict(path=dataset.test.path)
)
return run_in_venv(__file__, "exec.py",
                   input_data=data, dataset=dataset, config=config)

Other Frameworks

Integration of frameworks without any Python API is done in similar way, for example:

frameworks/AutoWEKA/
|-- __init__.py
|-- exec.py
|-- requirements.txt
|-- setup.sh
`-- lib/
    `-- (this is where the framework dependencies go, usually created by setup.sh)

or

frameworks/ranger/
|-- __init__.py
|-- exec.R
|-- exec.py
|-- requirements.txt
`-- setup.sh

Here are the main differences:

  • the setup phase is identical, but if at runtime, some executable file or library is required that need to be installed locally (as opposed to globally: for example, R or java executable are usually installed globally), we just recommend to put everything under the integration module (for example in lib and/or bin subfolders as for AutoWEKA). This is also true for some Python frameworks (cf. hyperoptsklearn integration for example, where the modules are loaded from frameworks/hyperoptsklearn/lib/hyperopt-sklearn).
  • the framework is then executed by building a command manually in exec.py, runming it in a subprocess, and finally collecting the results generated by the subprocess. For example, in autoxgboost/exec.py: 
    with Timer() as training:
    run_cmd(r"""Rscript --vanilla -e "source('{script}'); run('{train}', '{test}', target.index = {target_index}, '{output}', {cores}, time.budget = {time_budget})" """.format(
        script=os.path.join(here, 'exec.R'),
        train=dataset.train.path,
        test=dataset.test.path,
        target_index=dataset.target.index+1,
        output=config.output_predictions_file,
        cores=config.cores,
        time_budget=config.max_runtime_seconds
    ), _live_output_=True)
    Here, the exec.R script is also responsible to save the predictions in the expected format.

Add a default framework

Is called "default framework" an AutoML framework whose integration is available on master branch under the frameworks folder, and with a simple definition in resources/frameworks.yaml.

NOTE: There are a few requirements when integrating a new default framework:

  • The code snippet triggering the training should use only defaults (no AutoML hyper parameters), plus possibly a generic **kwargs in order to support params section in custom framework definitions. In other words, one of the requirements for being included in the benchmark is that the framework is submitted without any tweaks to default settings. This is to prevent submissions (systems) from overfitting or tuning to the benchmark.
  • There must be a way to limit the runtime of the algorithm (a maximum runtime parameter).
  • Exceptions:
    • the problem type ("classification", "regression", "binary", "multiclass"): this is available through config.type or dataset.type.
    • information about data, for example the column types: available through the dataset object.
    • time, cpu and memory constraints: those must be provided by the benchmark application through the config object.
    • the objective function: provided by config.metric (usually requires a translation for a given framework).
    • seed: provided by config.seed
    • paths to folders (output, temporary...): if possible, use config.output_dir or a subfolder (see existing integrations).
  • The default framework definition in resources/frameworks.yaml shouldn't have any params section: this params section is intended for custom definitions, not default ones.
good_framework:
   version: "0.0.1"
   project: "http://go.to/good_framework"

bad_framework:
   version: "0.0.1"
   project: "http://go.to/bad_framework"
   params: 
     enable_this: true
     use: ['this', 'that']

Using the instructions above:

  1. verify that there is an issue created under https://github.com/openml/automlbenchmark/issues for the framework you want to add, or create one.
  2. create a private branch for your integration changes.
  3. create the framework module (e.g. MyFramework) under frameworks folder.
  4. define the module (if possible without any params) in resources/frameworks.yaml.
  5. try to setup the framework:

    python runbenchmark.py myframework -s only

  6. fixes the framework setup until it works: the setup being usually a simple setup.sh script, you should be able to test it directly without using the application.
  7. try to run simple test against one fold using defaults (test benchmark and test constraints) with the -Xtest_mode that will trigger additional validations:

    python runbenchmark.py myframework -f 0 -Xtest_mode

  8. fix the module integration code until the test produce all results with no error (if the integration generated an error, it is visible in the results).
  9. if this works, validate it against the validation dataset using one fold:

    python runbenchmark.py myframework validation 1h4c -f 0 -Xtest_mode

  10. if this works, try to run it in docker to validate the docker image setup:

    python runbenchmark.py myframework -m docker

  11. if this works, try to run it in aws:

    python runbenchmark.py myframework -m aws

  12. create a pull request, and ask a review from authors of automlbenchmark: they'll also be happy to help you during this integration.

Add a custom framework

You may want to integrate a framework without wanting to make this publicly available.

In this case, as we've seen above, there's always the possibility to integrate your framework in a custom user_dir.

Using the instructions above:

  1. define what is (or will be) your custom user_dir for this framework.
  2. ensure it contains a config.yaml, otherwise create one (for example copy this one or examples/custom/config.yaml).
  3. create the framework module somewhere under this user_dir, e.g. {user_dir}/extensions/MyFramework.
  4. define the module in {user_dir}/frameworks.yaml (create the file if needed).
  5. follow the same steps as for a "default" framework to implement the integration: setup, test, ... except that you always need to specify the user_dir, e.g. for testing:

    python runbenchmark.py myframework -f 0 -u {user_dir}

  6. there may be some issues when trying to build the docker image when the framework is in a custom folder, as all the files should be under the docker build context: solving this probably requires a multi-stage build, needs more investigation. For now, if you really need a docker image, you can either build it manually, or simply copy the extensions folder temporarily under automlbenchmark.
  7. even without docker image, you can run the framework on AWS, as soon as the custom config.yaml, frameworks.yaml and extensions folder are made available as AWS resources: cf. again the custom configuration. The application will copy those files to the EC2 instances into a local user_dir and will be able to setup the framework there.

Analyze the results

Except the logs, all the files generated by the application are in easy to process csv format, and they are all generated in a subfolder of the output_dir (by default, ./results) unique for each benchmark run.

For example:

results/randomforest_test_test_local_20200108T202541
|-- predictions
|   |-- randomforest_iris_0.csv
|   |-- randomforest_iris_1.csv
|   |-- randomforest_kc2_0.csv
|   `-- randomforest_kc2_1.csv
`-- scores
    |-- RandomForest_task_iris.csv
    |-- RandomForest_task_kc2.csv
    |-- backup
    |   `-- results_20200108T202604.csv
    `-- results.csv

Results file format

Here is a sample results.csv file from a test run against the RandomForest framework:

id,task,framework,fold,result,mode,version,params,tag,utc,duration,models,seed,info,acc,auc,logloss
openml.org/t/3913,kc2,RandomForest,0,0.863636,local,0.21.3,{'n_estimators': 2000},stable,2020-01-08T20:25:47,2.8,2000,1452956522,,0.773585,0.863636,
openml.org/t/3913,kc2,RandomForest,1,0.864719,local,0.21.3,{'n_estimators': 2000},stable,2020-01-08T20:25:52,2.9,2000,1452956523,,0.886792,0.864719,
openml.org/t/59,iris,RandomForest,0,0.125879,local,0.21.3,{'n_estimators': 2000},stable,2020-01-08T20:25:56,2.6,2000,1452956522,,0.933333,,0.125879
openml.org/t/59,iris,RandomForest,1,0.0280719,local,0.21.3,{'n_estimators': 2000},stable,2020-01-08T20:26:04,5.9,2000,1452956523,,1.0,,0.0280719

which gives in more readable table format:

id task framework fold result mode version params tag utc duration models seed info acc auc logloss
openml.org/t/3913 kc2 RandomForest 0 0.863636 local 0.21.3 {'n_estimators': 2000} stable 2020-01-08T20:25:47 2.8 2000 1452956522 0.773585 0.863636
openml.org/t/3913 kc2 RandomForest 1 0.864719 local 0.21.3 {'n_estimators': 2000} stable 2020-01-08T20:25:52 2.9 2000 1452956523 0.886792 0.864719
openml.org/t/59 iris RandomForest 0 0.125879 local 0.21.3 {'n_estimators': 2000} stable 2020-01-08T20:25:56 2.6 2000 1452956522 0.933333 0.125879
openml.org/t/59 iris RandomForest 1 0.0280719 local 0.21.3 {'n_estimators': 2000} stable 2020-01-08T20:26:04 5.9 2000 1452956523 1.0 0.0280719

Here is a short description of each column:

  • id: a identifier for the dataset used in this result. For convenience, we use the link to the OpenML task by default.
  • task: the task name as defined in the benchmark definition.
  • framework: the framework name as defined in the framework definition.
  • fold: the dataset fold being used for this job. Usually, we're using 10 folds, so the fold varies from 0 to 9.
  • result: the result score, this is the score for the metric that the framework was trying to optimize. For example, for binary classification, the default metrics defined in resources/config.yaml are binary: ['auc', 'acc']; this means that the frameworks should try to optimize auc and the final auc score will become the result value, the other metrics (here acc) are then computed for information.
  • mode: one of local, docker, aws, aws+docker: tells where/how the job was executed.
  • version: the version of the framework being benchmarked.
  • params: if any, a JSON representation of the params defined in the framework definition. This allows to see clearly if some tuning was done for example.
  • tag: the branch tag of the automlbenchmark app that was running the job.
  • utc: the UTC timestamp at the job completion.
  • duration: the training duration: the framework integration is supposed to provide this information to ensure that it takes only into account the time taken by the framework itself. When benchmarking large data, the application can use a significant amount of time to prepare the data: this additional time doesn't appear in this duration column.
  • models: for some frameworks, it is possible to know how many models in total were trained by the AutoML framework.
  • seed: the seed or random state passed to the framework. With some frameworks, it is enough to obtain reproducible results. Note that the seed can be specified at the command line using -Xseed= arg (for example python randomforest -Xseed=1452956522): when there are multiple folds, the seed is then incremented by the fold number.
  • info: additional info in text format, this usually contains error messages if the job failed.
  • acc, auc, logloss metrics: all the metrics that were computed based on the generated predictions. For each job/row, one of them matches the result column, the others are purely informative. Those additional metric columns are simply added in alphabetical order.

Predictions

For each training, the framework integration must generate a predictions file that will be used by the application to compute the scores.

This predictions file:

  • must be saved under the predictions subfolder as shown above.
  • follow the naming convention: {framework}_{task}_{fold}.csv.
  • must be in CSV format with first row as header.
  • for classification problems, the header should look like: *class_labels | predictions | truth, with
    • *class_labels meaning an alphabetically ordered list of class/target labels, each column containing the probabilities for the corresponding class. If the framework doesn't provide those probabilities, then the framework integration should provide pseudo-probabilities, using 1 for the predicted value and 0 for other classes.
    • predictions column containing the predictions of the test predictor data (test.X) by the model trained by the framework,
    • truth being the test target data (test.y).
  • for regression problems, the header should look like predictions | truth, with
    • predictions column containing the predictions of the test predictor data (test.X) by the model trained by the framework,
    • truth being the test target data (test.y).

Examples:

Predictions sample on binary classification (kc2):

in CSV format:

no,yes,predictions,truth
0.965857617846013,0.034142382153998944,no,no
0.965857617846013,0.034142382153998944,no,no
0.5845,0.4155,no,no
0.6795,0.3205,no,no
0.965857617846013,0.034142382153998944,no,no

as table:

no yes predictions truth
0.965857617846013 0.034142382153998944 no no
0.965857617846013 0.034142382153998944 no no
0.5845 0.4155 no no
0.6795 0.3205 no no
0.965857617846013 0.034142382153998944 no no

Predictions sample on multiclass classification (iris):

in CSV format:

Iris-setosa,Iris-versicolor,Iris-virginica,predictions,truth
1.0,0.0,0.0,Iris-setosa,Iris-setosa
0.9715,0.028,0.0005,Iris-setosa,Iris-setosa
1.0,0.0,0.0,Iris-setosa,Iris-setosa
1.0,0.0,0.0,Iris-setosa,Iris-setosa
1.0,0.0,0.0,Iris-setosa,Iris-setosa
0.0,1.0,0.0,Iris-versicolor,Iris-versicolor
0.0,0.976,0.024,Iris-versicolor,Iris-versicolor
0.0,0.994,0.006,Iris-versicolor,Iris-versicolor
0.0,0.9925,0.0075,Iris-versicolor,Iris-versicolor
0.0,0.995,0.005,Iris-versicolor,Iris-versicolor
0.0,0.829,0.171,Iris-versicolor,Iris-virginica
0.0,0.008,0.992,Iris-virginica,Iris-virginica
0.0,0.0005,0.9995,Iris-virginica,Iris-virginica
0.0,0.0015,0.9985,Iris-virginica,Iris-virginica
0.0,0.0395,0.9605,Iris-virginica,Iris-virginica

as table:

Iris-setosa Iris-versicolor Iris-virginica predictions truth
1.0 0.0 0.0 Iris-setosa Iris-setosa
0.9715 0.028 0.0005 Iris-setosa Iris-setosa
1.0 0.0 0.0 Iris-setosa Iris-setosa
1.0 0.0 0.0 Iris-setosa Iris-setosa
1.0 0.0 0.0 Iris-setosa Iris-setosa
0.0 1.0 0.0 Iris-versicolor Iris-versicolor
0.0 0.976 0.024 Iris-versicolor Iris-versicolor
0.0 0.994 0.006 Iris-versicolor Iris-versicolor
0.0 0.9925 0.0075 Iris-versicolor Iris-versicolor
0.0 0.995 0.005 Iris-versicolor Iris-versicolor
0.0 0.829 0.171 Iris-versicolor Iris-virginica
0.0 0.008 0.992 Iris-virginica Iris-virginica
0.0 0.0005 0.9995 Iris-virginica Iris-virginica
0.0 0.0015 0.9985 Iris-virginica Iris-virginica
0.0 0.0395 0.9605 Iris-virginica Iris-virginica

Predictions sample on regression (cholesterol):

in CSV format:

predictions,truth
241.204,207.0
248.9575,249.0
302.278,268.0
225.9215,234.0
226.6995,201.0

as table:

predictions truth
241.204 207.0
248.9575 249.0
302.278 268.0
225.9215 234.0
226.6995 201.0

Extract more information

For some frameworks, it is also possible to extract more detailed information, in the form of artifacts that are saved after the training.

Examples of those artifacts are:

  • logs generated by the framework.
  • models or descriptions of the models trained by the framework.
  • predictions for each of the model trained by the AutoML framework.
  • ...

By default, those artifacts are not saved, and all frameworks don't provide the same artifacts, that's why the list of the artifacts that should be extracted can only be specified in the framework definition using the conventional _save_artifacts param:

Examples:

autosklearn_debug:
  extends: autosklearn
  params:
    _save_artifacts: ['models']  # will save models descriptions under the `models` subfolder

H2OAutoML_debug:
  extends: H2OAutoML
  project: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html
  params:
    _save_artifacts: ['leaderboard', 'logs', 'models']  # will save the leaderboard and models under the `models` subfolder, and the H2O logs under `logs` subfolder.

TPOT_debug:
  extends: tpot
  params:
    _save_artifacts: ['models']

Troubleshooting guide

Where are the results?

By default, the results for a benchmark execution are made available in a subfolder under output_dir (if not specified by -o my_results, then this is under {cwd}/results).

This subfolder is named {framework}_{benchmark}_{constraint}_{mode}_{timestamp}.

So that for example:

python runbenchmark.py randomforest

will create a subfolder randomforest_test_test_local_20200108T184305,

and:

python runbenchmark.py randomforest validation 1h4c -m aws

will create a subfolder randomforest_validation_1h4c_aws_20200108T184305.

Then each subfolder contains:

  • a score folder with a results.csv file concatenating the results from all the tasks in the benchmark, as well as potentially other individual results for each task.
  • a predictions folder with the predictions for each task in the benchmark.
  • a logs folder: only if benchmark was executed with -o output_dir argument.
  • possibly more folders if the framework saves additional artifacts.

Also the output_dir contains a results.csv concatenating ALL results from all subfolders.

Where are the logs?

By default the application logs are available under {cwd}/logs if the benchmark is executed without specifying the output_dir, otherwise, they'll be available under the logs subfolder in the benchmark results (see Where are the results?).

The application can collect various logs:

  • local benchmark application logs: those are always collected. For each run, the application generated 2 log files locally:
    • runbenchmark_{timestamp}.log: contains logs for the application only (from DEBUG level).
    • runbenchmark_{timestamp}_full.log: contains logs for the application + other Python libraries (from INFO level); e.g. boto3 logs when running in aws mode.
  • remote application logs: for aws mode only, logs generated on the remote instances are automatically downloaded to the results folder, together with other result artifacts.
  • framework logs (optional): if the framework integration supports it, it is possible to ask for the framework logs by creating a custom framework definition as follow: 
    H2OAutoML:
  extends: H2OAutoML
  params:
    _save_artifacts: ['logs']

Profiling the application

Currently, the application provides a global flag --profiling to activate profiling for some specific methods that can be slow or memory intensive:

python runbenchmark.py randomforest --profiling

All methods/functions are not profiled, but if you need to profile more, you just need to decorate the function with the @profile() decorator (from amlb.utils).

Memory usage

Examples of memory info when using this custom profiling:

[PROFILING] `amlb.datasets.openml.OpenmlDatasplit.data` returned object size: 45.756 MB.
[PROFILING] `amlb.datasets.openml.OpenmlDatasplit.data` memory change; process: +241.09 MB/379.51 MB, resident: +241.09 MB/418.00 MB, virtual: +230.01 MB/4918.16 MB.
...
[PROFILING] `amlb.data.Datasplit.release` executed in 0.007s.
[PROFILING] `amlb.data.Datasplit.release` memory change; process: -45.73 MB/238.80 MB, resident: +0.00 MB/414.60 MB, virtual: +0.00 MB/4914.25 MB.

Methods duration

Examples of method duration info when using this custom profiling:

[PROFILING] `amlb.datasets.openml.OpenmlLoader.load` executed in 7.456s.
...
[PROFILING] `amlb.data.Datasplit.X_enc` executed in 6.570s.

Python library version conflict

see Framework integration

Framework setup is not executed

Try the following:

  • force the setup using the -s only or -s force arg on the command line:
    • -s only or --setup=only will force the setup and skip the benchmark run.
    • -s force or --setup=force will force the setup and run the benchmark immediately.
  • delete the .marker_setup_safe_to_delete from the framework module and try to run the benchmark again. This marker file is automatically created after a successful setup to avoid having to execute it each tine (setup phase can be time-consuming), this marker then prevents auto-setup, except if the -s only or -s force args above are used.

Framework setup fails

If the setup fails, first note that only the following OS are fully supported:

  • Ubuntu 18.04

The setup is created for Debian-based linux environments, and macOS (most frameworks can be installed on macOS, ideally with brew installed, but there may be a few exceptions), so it may work with other Linux environments not listed above (e.g. Debian, Ubuntu 20.04, ...). The best way to run benchmarks on non-supported OS, is to use the docker mode.

If the setup fails on a supported environment, please try the following:

  • force the setup: see above.
  • ensure that the same framework is not set up multiple times in parallel on the same machine:
    • first use python runbenchmark.py MyFramework -s only on one terminal.
    • then you can trigger multiple python runbenchmark.py MyFramework ... (without -s option) in parallel.
  • delete the lib and venv folders, if present, under the given framework folder (e.g. frameworks/MyFramework), and try the setup again.