Feature/#79 rename data science sandbox to exasol ai lab (#147)

* Updated error_code_config and changes file * Updated git reference in exasol/ds/sandbox/templates/ci_code_build.jinja.yaml * Updated default password * Update documentation TBD: images not yet updated * Renamed AMI prefix * Updated names of VM images * Replaced name of GitHub repository in additional places * Updated additional documentation * Updated links in pyproject.toml * Updated project description in pyproject.toml * Updated name of docker image * Additional renamings * Renamed poetry package * Updated DEFAULT_CHANGE_SET_PREFIX * Updated AWS asset tag * Renamed AWS Bucket prefix (folder in bucket) * Replaced short tag XAL by XAIL * Release notes, S3: Change title and removed column "S3 URI" * Updated references in developer guide * Renamed default filename for Secure Configuration Storage * Replaced path in AWS bucket by jinja variable and renamed to ai-lab * Removed file setup_db.ipynb
exasol · Jan 30, 2024 · 372172c · 372172c
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diff --git a/README.md b/README.md
@@ -1,4 +1,4 @@
-# Exasol Data Science Sandbox
+# Exasol AI-Lab
 
 ## Overview
 
@@ -7,7 +7,7 @@ enabling users to try out data science algorithms in Jupyter notebooks connected
 
 ## Where to find the VM images
 
-The release process will automatically store the links to the images in the [release notes](https://github.com/exasol/data-science-sandbox/releases/latest), as there will be a specific AMI per release.
+The release process will automatically store the links to the images in the [release notes](https://github.com/exasol/ai-lab/releases/latest), as there will be a specific AMI per release.
 Please check the user guide about details of the image.
 
 ## Links

diff --git a/aws-code-build/ci/buildspec_release.yaml b/aws-code-build/ci/buildspec_release.yaml
@@ -3,7 +3,7 @@ version: 0.2
 env:
   shell: bash
   variables:
-    DEFAULT_PASSWORD: "dss"
+    DEFAULT_PASSWORD: "ai-lab"
     ASSET_ID: ""
     AWS_USER_NAME: "release_user"
     MAKE_AMI_PUBLIC_OPTION: "--no-make-ami-public"

diff --git a/doc/changes/changes_0.1.0.md b/doc/changes/changes_0.1.0.md
@@ -1,12 +1,12 @@
-# data-science-sandbox 0.1.0, released t.b.d.
+# ai-lab 0.1.0, released t.b.d.
 
 Code name: Initial release
 
 ## Summary
 
-Initial release of the data-science-sandbox. It provides the creation of an Amazon Machine Image (AMI) and virtual machine images for a specific version of the data-science-sanbox-release project.
+Initial release of the Exasol AI-Lab. It provides the creation of an Amazon Machine Image (AMI), virtual machine images, and a docker image for a specific version of the AI-Lab project.
 
-## Data-Science-Sandbox-Release
+## AI-Lab-Release
 
 Version: 0.1.0
 
@@ -31,6 +31,7 @@ Version: 0.1.0
 * #137: Set Jupyter lab default URL to AI-Lab start page
 * #75: Changed default port of Jupyter server to 49494
 * #145: Add Docket Test Library to prepare Notebook tests
+* #255: Renamed data science sandbox to exasol-ai-lab
 
 ## Bug Fixes
 

diff --git a/doc/developer_guide/ci.md b/doc/developer_guide/ci.md
@@ -26,7 +26,7 @@ The CodeBuild will take about 20 minutes to complete.
 
 To run these tests locally please use
 
- ```shell 
- export DSS_RUN_CI_TEST=true; poetry run test/codebuild/test_ci.py 
+```shell 
+export DSS_RUN_CI_TEST=true; poetry run test/codebuild/test_ci.py 
 ```
 
diff --git a/doc/developer_guide/commands.md b/doc/developer_guide/commands.md
@@ -1,6 +1,6 @@
 # Commands
 
-The commands offered by the DSS CLI can be organized into three groups:
+The AI-Lab CLI offers commands in the following three groups:
 
 | Group                | Usage                                   |
 |----------------------|-----------------------------------------|
@@ -14,7 +14,7 @@ The following commands are used during the release AWS Codebuild job:
 * `create-vm`: Create a new AMI and VM images.
 * `update-release`: Update release notes of an existing Github release.
 * `start-release-build`: Start the release on AWS codebuild.
-* `create-docker-image`: Create a Docker image for data-science-sandbox and deploy it to hub.docker.com/exasol/data-science-sandbox.
+* `create-docker-image`: Create a Docker image for ai-lab and deploy it to hub.docker.com/exasol/ai-lab.
 
 Script `start-release-build`:
 * Is usually called from github workflow `release_droid_upload_github_release_assets.yml`.

diff --git a/doc/developer_guide/developer_guide.md b/doc/developer_guide/developer_guide.md
@@ -1,4 +1,4 @@
-# Data Science Sandbox Developer Guide
+# Exasol AI-Lab Developer Guide
 
 ## Overview
 
@@ -28,8 +28,7 @@ bash install.sh
 
 ## Design Goals
 
-The Data Science Sandbox (DSS) uses AWS as backend, because it provides the possibility to run the whole workflow during
-a ci-test.
+The Exasol AI-Lab (XAIL) uses AWS as backend, because it provides the possibility to run the whole workflow during a ci-test.
 
 This project uses
 

diff --git a/doc/developer_guide/testing.md b/doc/developer_guide/testing.md
@@ -10,9 +10,9 @@ Creating a docker image is quite time-consuming, currently around 7 minutes. In
 docker image in the tests in `integration/test_create_dss_docker_image.py` 
 simply add CLI option `--dss-docker-image` when calling `pytest`:
 
- ```shell  
-  poetry run pytest --dss-docker-image exasol/data-science-sandbox:0.1.0 
-  ```
+```shell  
+poetry run pytest --dss-docker-image exasol/ai-lab:0.1.0 
+```
 
 #### Executing tests involving AWS resources
 

diff --git a/doc/user_guide/ami_usage.md b/doc/user_guide/ami_usage.md
@@ -24,7 +24,7 @@ __Important__: The AMI is currently only available in the AWS region `eu-central
  5. Launch the EC2 instance:
      - In the navigation bar on the left select "Instances"
      - Click button "Launch instances"
-     - At field "Application and OS Images" select the AMI id of the sandbox (found in the [release notes](https://github.com/exasol/data-science-sandbox/releases/latest))
+     - At field "Application and OS Images" select the AMI id of the sandbox (found in the [release notes](https://github.com/exasol/ai-lab/releases/latest))
      - Select an appropriate instance type (at least "t2.small" or similar)
      - Choose your key pair
      - Choose the security group which your created in step 3.

diff --git a/doc/user_guide/docker/docker_usage.md b/doc/user_guide/docker/docker_usage.md
@@ -4,7 +4,7 @@ Using Exasol AI-Lab Docker Edition requires some specific prerequisites but also
 
 ## Need to Know About Docker Images and Containers
 
-Exasol AI-Lab Docker Edition is published as a so-called _Docker Image_ on [Docker Hub](https://hub.docker.com/r/exasol/data-science-sandbox).
+Exasol AI-Lab Docker Edition is published as a so-called _Docker Image_ on [Docker Hub](https://hub.docker.com/r/exasol/ai-lab).
 
 In order to use such an image you need two components
 * Docker client
@@ -101,7 +101,7 @@ The following command will
 docker run \
   --volume ${VOLUME}:/root/notebooks \
   --publish ${LISTEN_IP}:49494:49494 \
-  exasol/data-science-sandbox:${VERSION}
+  exasol/ai-lab:${VERSION}
 ```
 
 Additional options
@@ -121,15 +121,15 @@ docker run \
   --volume ${VOLUME}:/root/notebooks \
   --volume /var/run/docker.sock:/var/run/docker.sock \
   --publish ${LISTEN_IP}:49494:49494 \
-  exasol/data-science-sandbox:${VERSION}
+  exasol/ai-lab:${VERSION}
 ```
 
 ## Connecting to Jupyter Service
 
 When starting AI-Lab as Docker container the command line will display a welcome message showing connection instructions and a reminder to change the default password:
 
 ```
-$ docker run --publish 0.0.0.0:$PORT:49494 exasol/data-science-sandbox:$VERSION
+$ docker run --publish 0.0.0.0:$PORT:49494 exasol/ai-lab:$VERSION
 Server for Jupyter has been started successfully.
 
 You can connect with http://<host>:<port>
@@ -141,7 +141,7 @@ port to the same port then you can connect with http://localhost:49494.
 │ │├─┘ ││├─┤ │ ├┤   └┬┘│ ││ │├┬┘   ││ │├─┘└┬┘ │ ├┤ ├┬┘  ├─┘├─┤└─┐└─┐││││ │├┬┘ ││ │
 └─┘┴  ─┴┘┴ ┴ ┴ └─┘   ┴ └─┘└─┘┴└─  └┘└─┘┴   ┴  ┴ └─┘┴└─  ┴  ┴ ┴└─┘└─┘└┴┘└─┘┴└──┴┘ o
 
-The default password is "dss".
+The default password is "ai-lab".
 To update the password as user root run
     /root/jupyterenv/bin/jupyter-lab server password
 ```

diff --git a/doc/user_guide/editions.md b/doc/user_guide/editions.md
@@ -9,7 +9,7 @@ Exasol AI-Lab is available in the following editions:
 | Docker Edition          | Docker Image                                                             |
 
 Each of the editions is associated with an _image_ in a specific format which
-* Is linked in the [release notes](https://github.com/exasol/data-science-sandbox/releases/latest) for download
+* Is linked in the [release notes](https://github.com/exasol/ai-lab/releases/latest) for download
 * Contains all necessary dependencies
 * Provides a running Jupyterlab instance which is automatically started when booting or running the image
 
@@ -24,9 +24,9 @@ Recommendations
 
 ### AMI Edition
 
-The ID of the AMI (Amazon Machine Image) is mentioned in the [release notes](https://github.com/exasol/data-science-sandbox/releases/latest) and can be used to start an EC2-instance in your AWS account.
+The ID of the AMI (Amazon Machine Image) is mentioned in the [release notes](https://github.com/exasol/ai-lab/releases/latest) and can be used to start an EC2-instance in your AWS account.
 
-The naming scheme is: "_Exasol-Data-Science-Sandbox-${VERSION}_", e.g. "_Exasol-Data-Science-Sandbox-5.0.0_"
+The naming scheme is: "_Exasol-AI-Lab-${VERSION}_", e.g. "_Exasol-AI-Lab-5.0.0_"
 
 See also [User Guide for AI-Lab AMI Edition](ami_usage.md).
 
@@ -51,6 +51,6 @@ See also [User Guide for AI-Lab VM Edition](vm_usage.md).
 
 ### Docker Edition
 
-The Docker image is published to DockerHub at https://hub.docker.com/r/exasol/data-science-sandbox.
+The Docker image is published to DockerHub at https://hub.docker.com/r/exasol/ai-lab.
 
 See also [User Guide for AI-Lab Docker Edition](docker/docker_usage.md).
diff --git a/doc/user_guide/user_guide.md b/doc/user_guide/user_guide.md
@@ -1,4 +1,4 @@
-# Data Science Sandbox User Guide
+# Exasol AI-Lab User Guide
 
 ## Overview
 
@@ -29,7 +29,7 @@ AI-Lab can automatically launch such an instance on demand. However when using A
 Username: **ubuntu**
 
 At the first login to the sandbox (image or AMI) you will be prompted to change your password.
-The default password is: **dss**
+The default password is: **ai-lab**
 
 However, we suggest to use ssh-keys for the connection. When you use the AWS AMI, this will work automatically. When using the VM images, you need to deploy your ssh-keys. After you enabled ssh-keys, we recommend to disable ssh password authentication:
 ```shell
@@ -47,7 +47,7 @@ Root location
 |---------------------|------------------------------------------|
 | Virtual environment | location `/$ROOT/jupyterenv`             |
 | Location notebooks  | location `/$ROOT/notebooks`              |
-| Password            | `dss`                                    |
+| Password            | `ai-lab`                                 |
 | Http Port           | `49494` (or the port you forwared it to) |
 
 Exasol strongly recommends to change the Jupyter password as soon as possible. Details about how to do that will be shown in the login screen.

diff --git a/doc/user_guide/vm_usage.md b/doc/user_guide/vm_usage.md
@@ -11,7 +11,7 @@
 
 ### Step-by-step
 
-1. Download the `VMDK` file from the [release notes](https://github.com/exasol/data-science-sandbox/releases/latest).
+1. Download the `VMDK` file from the [release notes](https://github.com/exasol/ai-lab/releases/latest).
 2. Open Boxes
 3. Create a new VM: Click the + Button
 4. Choose: "Create virtual machine from file"<br />

diff --git a/error_code_config.yml b/error_code_config.yml
@@ -1,3 +1,3 @@
 error-tags:
-  DSS:
+  XAIL:
     highest-index: 0
diff --git a/exasol/ds/sandbox/cli/commands/create_docker_image.py b/exasol/ds/sandbox/cli/commands/create_docker_image.py
@@ -58,10 +58,10 @@ def create_docker_image(
         log_level: str,
 ):
     """
-    Create a Docker image for data-science-sandbox.  If option
-    ``--publish`` is specified then deploy the image to the Docker registry
-    using the specified user name and reading the password from environment
-    variable ``PASSWORD_ENV``.
+    Create a Docker image for ai-lab.  If option ``--publish`` is
+    specified then deploy the image to the Docker registry using the specified
+    user name and reading the password from environment variable
+    ``PASSWORD_ENV``.
     """
     def registry_password():
         if registry_user is None:

diff --git a/exasol/ds/sandbox/lib/asset_id.py b/exasol/ds/sandbox/lib/asset_id.py
@@ -1,5 +1,5 @@
 class AssetId:
-    def __init__(self, asset_id: str, stack_prefix="EC2-DATA-SCIENCE-SANDBOX-", ami_prefix="Exasol-Data-Science-Sandbox"):
+    def __init__(self, asset_id: str, stack_prefix="EC2-DATA-SCIENCE-SANDBOX-", ami_prefix="Exasol-AI-Lab"):
         self._asset_id = asset_id
         self._stack_prefix = stack_prefix
         self._ami_prefix = ami_prefix
@@ -23,4 +23,4 @@ def stack_prefix(self):
     def __repr__(self):
         return self._asset_id
 
-    BUCKET_PREFIX = "data_science_sandbox"
+    BUCKET_PREFIX = "ai_lab"
diff --git a/exasol/ds/sandbox/lib/asset_printing/print_assets.py b/exasol/ds/sandbox/lib/asset_printing/print_assets.py
@@ -156,41 +156,38 @@ def print_s3_objects(aws_access: AwsAccess, asset_id: Optional[AssetId], printin
     else:
         prefix = ""
 
-    table_printer = printing_factory.create_table_printer(title=f"S3 Objects (Bucket={vm_bucket} Prefix={prefix})")
+    table_printer = printing_factory.create_table_printer(title=f"VM Images, Prefix={prefix})")
 
     table_printer.add_column("Key", no_wrap=True)
     table_printer.add_column("Size", no_wrap=True)
-    table_printer.add_column("S3 URI", no_wrap=False)
     table_printer.add_column("URL", no_wrap=False)
 
     # How the filtering works:
     # 1. The VM are stored under following location in the S3 Bucket: $BUCKET_PREFIX/$AssetId/name.$VM_FORMAT
-    #    For example "data_science_sandbox/5.0.0/export-ami-01be860e6a6a98bf8.vhd"
+    #    For example "ai_lab/5.0.0/export-ami-01be860e6a6a98bf8.vhd"
     # 2. Because S3 list_s3_object does not support wildcards,
     #    we need to implement our own wildcard implementation here.
-    #    We call list_s3_object with the standard prefix (e.g. "data_science_sandbox"),
+    #    We call list_s3_object with the standard prefix (e.g. "ai_lab"),
     #    which returns ALL stored vm objects.
     # 3. If no filter is given (asset_id == None), "prefix" will be empty, and we return all s3 objects
     # 4. If the variable "prefix" is not empty, we need to ensure that it ends with a wildcard, so that the matching
     #    works correctly.
-    # => Assume that a filter is given  "5.0.0". Variable prefix would be "data_science_sandbox/5.0.0".
+    # => Assume that a filter is given  "5.0.0". Variable prefix would be "ai_lab/5.0.0".
 
     s3_objects = aws_access.list_s3_objects(bucket=vm_bucket, prefix=AssetId.BUCKET_PREFIX)
 
     if s3_objects is not None and len(prefix) > 0:
         if prefix[-1] != "*":
             prefix = f"{prefix}*"
         s3_objects = [s3_object for s3_object in s3_objects if fnmatch.fnmatch(s3_object.key, prefix)]
-    s3_bucket_uri = "s3://{bucket}/{{object}}".format(bucket=vm_bucket)
     https_bucket_url = "https://{url_for_bucket}/{{object}}".format(url_for_bucket=url_for_bucket)
 
     if s3_objects is not None:
         for s3_object in s3_objects:
             obj_size = humanfriendly.format_size(s3_object.size)
             key = s3_object.key
-            s3_uri = s3_bucket_uri.format(object=key)
             https_url = https_bucket_url.format(object=urllib.parse.quote(key))
-            table_printer.add_row(key, obj_size, s3_uri, https_url)
+            table_printer.add_row(key, obj_size, https_url)
 
     table_printer.finish()
 

diff --git a/exasol/ds/sandbox/lib/aws_access/deployer.py b/exasol/ds/sandbox/lib/aws_access/deployer.py
@@ -28,7 +28,7 @@
     "ChangeSetResult", ["changeset_id", "changeset_type"])
 
 
-DEFAULT_CHANGE_SET_PREFIX="dss-ci-setup-deploy-"
+DEFAULT_CHANGE_SET_PREFIX="ai-lab-ci-setup-deploy-"
 
 
 class Deployer(object):

diff --git a/exasol/ds/sandbox/lib/dss_docker/create_image.py b/exasol/ds/sandbox/lib/dss_docker/create_image.py
@@ -18,8 +18,9 @@
 from exasol.ds.sandbox.lib.setup_ec2.host_info import HostInfo
 from exasol.ds.sandbox.lib.setup_ec2.run_install_dependencies import run_install_dependencies
 
-DEFAULT_ORG_AND_REPOSITORY = "exasol/data-science-sandbox"
-DSS_VERSION = version("exasol-data-science-sandbox")
+DEFAULT_ORG_AND_REPOSITORY = "exasol/ai-lab"
+# name of the project as specified in file pyproject.toml
+DSS_VERSION = version("exasol-ai-lab")
 _logger = get_status_logger(LogType.DOCKER_IMAGE)
 
 
@@ -94,7 +95,7 @@ def _ansible_run_context(self) -> AnsibleRunContext:
             "docker_container": self.container_name,
         }
         return AnsibleRunContext(
-            playbook="dss_docker_playbook.yml",
+            playbook="ai_lab_docker_playbook.yml",
             extra_vars=extra_vars,
         )
 
@@ -144,7 +145,7 @@ def _commit_container(
             container: DockerContainer,
             facts: AnsibleFacts,
     ) -> DockerImage:
-        _logger.debug(f'DSS facts: {get_fact(facts)}')
+        _logger.debug(f'AI-Lab facts: {get_fact(facts)}')
         _logger.info("Committing changes to docker container")
         virtualenv = get_fact(facts, "jupyter", "virtualenv")
         port = get_fact(facts, "jupyter", "port")

diff --git a/exasol/ds/sandbox/lib/export_vm/rename_s3_objects.py b/exasol/ds/sandbox/lib/export_vm/rename_s3_objects.py
@@ -14,7 +14,7 @@ def build_image_source(prefix: str, export_image_task_id: str, vm_image_format:
 
 def build_image_destination(prefix: str, asset_id: AssetId, vm_image_format: VmDiskImageFormat) -> str:
     img_format = vm_image_format.value.lower()
-    return "{bucket_prefix}exasol-data-science-sandbox-{asset_id}.{img_format}".format(
+    return "{bucket_prefix}exasol-ai-lab-{asset_id}.{img_format}".format(
         bucket_prefix=prefix,
         asset_id=str(asset_id),
         img_format=img_format)
@@ -26,7 +26,7 @@ def rename_image_in_s3(aws_access: AwsAccess, export_image_task: ExportImageTask
     """
     Renames the resulting S3 object of an export-image-task.
     The source objects always have the format "$export-image-task-id.$format".
-    The destination objects always have the format "exasol-data-science-sandbox-{asset_id}.{img_format}"
+    The destination objects always have the format "exasol-ai-lab-{asset_id}.{img_format}"
     The bucket and prefix in bucket do not change.
     :param aws_access: Access proxy to Aws
     :param export_image_task: The export image task which is expected to be completed successfully.

diff --git a/exasol/ds/sandbox/lib/github_release_access.py b/exasol/ds/sandbox/lib/github_release_access.py
@@ -70,5 +70,5 @@ def upload(self, archive_path: str, label: str, release_id: int, content_type: s
     @property
     def _get_repo(self) -> Repository:
         gh = Github(self._gh_token)
-        gh_repo = gh.get_repo("exasol/data-science-sandbox")
+        gh_repo = gh.get_repo("exasol/ai-lab")
         return gh_repo
diff --git a/exasol/ds/sandbox/lib/logging.py b/exasol/ds/sandbox/lib/logging.py
@@ -21,7 +21,7 @@ class LogType(Enum):
 
 
 def get_status_logger(log_type: LogType) -> logging.Logger:
-    return logging.getLogger(f"edss-{log_type.value}")
+    return logging.getLogger(f"ai-lab-{log_type.value}")
 
 
 def set_log_level(level: str):

diff --git a/exasol/ds/sandbox/lib/setup_release_codebuild/release_codebuild.py b/exasol/ds/sandbox/lib/setup_release_codebuild/release_codebuild.py
@@ -2,6 +2,7 @@
 from exasol.ds.sandbox.lib.logging import get_status_logger, LogType
 from exasol.ds.sandbox.lib.render_template import render_template
 from exasol.ds.sandbox.lib.vm_bucket.vm_dss_bucket import find_vm_bucket
+from exasol.ds.sandbox.lib.asset_id import AssetId
 
 RELEASE_CODE_BUILD_STACK_NAME = "DATA-SCIENCE-SANDBOX-RELEASE-CODEBUILD"
 
@@ -13,6 +14,7 @@ def run_setup_release_codebuild(aws_access: AwsAccess) -> None:
     yml = render_template(
         "release_code_build.jinja.yaml",
         vm_bucket=find_vm_bucket(aws_access),
+        path_in_bucket=AssetId.BUCKET_PREFIX,
         dockerhub_secret_arn=secret_arn,
     )
     aws_access.upload_cloudformation_stack(yml, RELEASE_CODE_BUILD_STACK_NAME)

diff --git a/exasol/ds/sandbox/lib/tags.py b/exasol/ds/sandbox/lib/tags.py
@@ -1,4 +1,4 @@
-DEFAULT_TAG_KEY = "exa_dss_id"
+DEFAULT_TAG_KEY = "exa_ai_lab_id"
 
 
 def create_default_asset_tag(value: str) -> list: