Collaborative Version Control for Machine Learning Projects.

Question

Pulumi · Accepted Answer

When setting up collaborative version control for machine learning projects, one important consideration is creating a centralized place where data scientists and developers can share, version, and manage their code, datasets, models, and experiments. For Microsoft Azure users, integrating services like Azure Machine Learning Workspaces, Projects, and Experiments, along with Git-based version control systems, can be very effective.

Azure Machine Learning Workspaces provide a centralized place for managing machine learning assets within Azure. It's also possible to tie this into a version control system such as Git for collaborative coding.

The Python program below demonstrates how to set up an Azure Machine Learning Workspace and a Project which enables collaborative machine learning development in a version-controlled environment using Pulumi. The program also shows how to integrate a Git repository for version control, although the repository itself must exist prior to running this program and is therefore referred to by a placeholder URL.

```python
import pulumi
import pulumi_azure_native.machinelearningservices as machinelearningservices
import pulumi_azure_native.machinelearningexperimentation as machinelearningexperimentation

# Set up configuration variables
# Make sure to replace `existing-git-repo-url` with the URL of your actual Git repository.
git_repo_url = "https://github.com/your-organization/your-ml-project.git"
my_resource_group = "my-resource-group"
my_workspace_name = "my-ml-workspace"

# Create an Azure Machine Learning Workspace in your specified resource group.
ml_workspace = machinelearningservices.Workspace(
    "mlWorkspace",
    resource_group_name=my_resource_group,
    location="eastus",
    sku=machinelearningservices.SkuArgs(
        name="Basic",  # S3 tier is used for basic Machine Learning workspaces
    ),
    description="Workspace for our collaborative ML project",
)

# Create a Machine Learning Project inside the workspace for collaborative workflows.
ml_project = machinelearningexperimentation.Project(
    "mlProject",
    resource_group_name=my_resource_group,
    location=ml_workspace.location,
    workspace_name=my_workspace_name,
    account_name=ml_workspace.name,
    projectName="collaborative-ml-project",
    friendlyName="CollaborativeMLProject",
    gitrepo=git_repo_url,  # Link to the Git repository for version control
)

pulumi.export("workspace_name", ml_workspace.name)
pulumi.export("project_name", ml_project.name)
```

In the program above, we begin by importing the required Pulumi modules for Azure Machine Learning and Machine Learning Experimentation. We're creating two main resources:

1. An Azure Machine Learning Workspace, which acts as a container for the machine learning training and deployment artifacts, such as models, datasets, and scripts. The workspace resource is created using `machinelearningservices.Workspace`.

2. An Azure Machine Learning Project, which provides a more granular container within the workspace. It enables the management of machine learning code and collaboration with other developers and data scientists. This is done using the `machinelearningexperimentation.Project` resource.

Both resources require you to specify the `resource_group_name` and `location`. The location should match the region in which your resource group is deployed. The workspace SKU is set to `Basic` for this example, although it can be modified according to your pricing tier and resource requirements.

The `gitrepo` parameter is where you specify the URL of your Git repository. This allows Azure to access the version-controlled code for your Machine Learning Projects. Make sure to replace the placeholder `existing-git-repo-url` with your actual Git repository URL.

Lastly, we export the names of the created workspace and project so that you can easily reference them, e.g., when setting up your Continuous Integration/Continuous Deployment (CI/CD) pipelines or when accessing these resources from Azure's portal or CLI tools.

With this setup, you can manage your machine learning projects with Git-based version control, collaborate with peers, and keep track of changes throughout the project lifecycle within the Azure cloud environment.