1. Scalable ML Workflows Orchestration using Buildkite


    When you want to orchestrate machine learning (ML) workflows, especially at a scale that requires automation and collaboration, you need a continuous integration (CI) and deployment (CD) system that can handle complex pipeline definitions, automation, and integration with various tools and cloud services. Buildkite is one such platform that allows you to run scalable and flexible CI/CD pipelines defined in code.

    Below is a Pulumi Python program that sets up Buildkite pipelines for orchestrating ML workflows. It uses resources from the Buildkite provider that are relevant for creating and managing Buildkite organizations, pipelines, and teams.

    1. Organization: In Buildkite, everything starts with an organization. It’s your company or team's shared space where pipelines run.

    2. Pipelines: These are used to define your build, testing, and deployment workflows. It could consist of a series of steps that Buildkite agents execute.

    3. Teams: They allow you to manage permissions for users and pipelines.

    4. Agents: These are the workers that run your builds. They can be hosted on your infrastructure, providing you the flexibility to run on various platforms with different compute sizes depending on your ML workload.

    In the following Pulumi program, we'll create a Buildkite organization, a couple of teams, and define a pipeline with steps that could be tailored to run your ML workflows. The specific details of the ML workflow steps (like scripts or Docker images to use) would depend on your particular use case and tools.

    import pulumi import pulumi_buildkite as buildkite # Configure the Buildkite provider with your specific Buildkite API Access Token. # This assumes your Pulumi configuration is already set up with the token. # Make sure your access token has the required permissions to create organizations, # teams, and pipelines. # Create a new Buildkite Organization. In this case, we'll simulate that it's already existing. # Typically, you would create an organization through the Buildkite UI, and manage it using Pulumi afterward. organization = buildkite.Organization.get('your-organization-name', 'organization-id') # Define a team for the Data Scientists. team_data_scientists = buildkite.Team("data-scientists-team", name="Data Scientists", privacy="VISIBLE", default_team=False, default_member_role="MEMBER", members_can_create_pipelines=False, organization=organization.name, ) # Define a team for the ML Engineers. team_ml_engineers = buildkite.Team("ml-engineers-team", name="ML Engineers", privacy="VISIBLE", default_team=False, default_member_role="MAINTAINER", # ML Engineers may need more permissions members_can_create_pipelines=True, organization=organization.name, ) # Define a pipeline for an ML workflow. # This is where you would define your build steps, environment variables, etc. ml_workflow_pipeline = buildkite.Pipeline("ml-workflow-pipeline", name="ML Workflow", repository="git://github.com/your/repo.git", # Your repository containing the ML workflow definitions steps="steps:\n - label: ':python: Run ML Training'\n command: 'python train_model.py'\n - wait\n - label: ':docker: Build Docker Image'\n command: 'docker build -t ml-model .'", # Here you define the steps to run ML training, etc. organization=organization.name, ) # Export the URL of the Buildkite pipeline, so it can be accessed easily after deployment. pulumi.export("pipeline_url", ml_workflow_pipeline.web_url)


    • We start by importing dependencies. Buildkite resources are imported from the pulumi_buildkite library.
    • We get the reference of the existing Buildkite organization using its name and ID, assuming the organization has already been created in Buildkite.
    • We create two teams, one for data scientists and the other for ML engineers, specifying their roles and visibility within the organization.
    • We then define a pipeline that reflects the ML workflow. In the steps definition, you would put the actual commands your pipeline should execute. The example includes placeholders for a model training step and a Docker image build step.
    • Finally, we export the URL of the Buildkite pipeline, which can be used to access the pipeline once it's up and running.

    You can extend the pipeline steps according to the complexity of your ML workflows, potentially involving testing, parallel steps, artifact uploads, model deployment steps, and integration with external data sources or services.

    It’s important to note that the actual CI/CD workflow, especially for ML, would be more complex and necessitate the use of specialized tooling for things like dataset management, model training, versioning, and serving. The Pulumi program structure would stay largely the same, but the details inside each step would be specific to your chosen workflow and tooling stack.