1. Automating ML Data Preprocessing with GCP Workflows


    In order to automate ML data preprocessing with GCP (Google Cloud Platform) using Workflows, we will leverage Google Cloud's Workflows service. Workflows orchestrate and automate Google Cloud and HTTP-based API services, making it a flexible choice to coordinate tasks like data preprocessing.

    The processing itself can be performed in a number of different services, like Cloud Functions for lightweight processing or Dataflow for heavier data processing jobs. Since the specific data preprocessing tasks aren't specified, we’ll assume you want to orchestrate several hypothetical services, such as a Cloud Storage bucket for data storage, a Cloud Function for data transformation, and AI Platform Jobs for machine learning jobs.

    Here we'll create a Pulumi program in Python that defines:

    1. A Cloud Storage bucket to store training data.
    2. A Cloud Function that can be used to preprocess this data.
    3. A Workflow that orchestrates the preprocessing step with the Cloud Function and assumes the existence of a subsequent step for training.

    Let's walk through the Pulumi Python program:

    • Cloud Storage Bucket: We'll start by defining a Cloud Storage bucket to store our raw training data.

    • Cloud Function: Next, we'll create a Cloud Function which will read data from the bucket, perform preprocessing, and write the processed data back to the bucket, or pass it to a further processing step. We will abstract the details of the Cloud Function code as the logic will depend on the type of preprocessing required.

    • Workflows: Finally, we’ll define a workflow with google-native.workflows/v1.Workflow. The Workflow will be responsible for triggering the Cloud Function and can be extended to also trigger other services needed for model training or additional processing.

    Below is the Pulumi program to set up the above resources:

    import pulumi import pulumi_gcp as gcp # Create a Google Cloud Storage bucket to store raw training data. raw_data_bucket = gcp.storage.Bucket("raw-data-bucket") # Deploy a Cloud Function for data preprocessing. # The cloud function 'source_archive_bucket' and 'source_archive_object' should # refer to a zip file containing your Cloud Function source code and dependencies. # Here we assume such a zip file has been uploaded to another bucket. preprocess_function = gcp.cloudfunctions.Function("preprocess-function", entry_point="preprocess_data", runtime="python37", environment_variables={ "BUCKET_NAME": raw_data_bucket.name, }, source_archive_bucket="source-code-bucket", source_archive_object="preprocess_function.zip", trigger_http=True, available_memory_mb=128 ) # Expose an HTTP URL for the Cloud Function for triggering. invoker = gcp.cloudfunctions.FunctionIamMember("invoker", project=preprocess_function.project, region=preprocess_function.region, cloud_function=preprocess_function.name, role="roles/cloudfunctions.invoker", member="allUsers", ) # Define a Workflow to automate data preprocessing. # Workflow definition in YAML format that specifies execution of the Cloud Function. workflow = gcp.workflows.Workflow("data-preprocessing-workflow", region="us-central1", description="A workflow to orchestrate ML data preprocessing.", source_contents=f""" - init: assign: - project: "{raw_data_bucket.project}" - location: "us-central1" - input_data_bucket: "{raw_data_bucket.name}" - function_name: "{preprocess_function.name}" - preprocess: call: http.get args: url: "https://{location}-{project}.cloudfunctions.net/{function_name}" query: input_bucket: ${{"input_data_bucket"}} result: preprocess_result """ ) pulumi.export("raw_data_bucket_name", raw_data_bucket.name) pulumi.export("preprocess_function_url", invoker.member) pulumi.export("workflow_name", workflow.name)

    In this program, Pulumi will handle provisioning the resources and express the dependencies between them. Here’s the workflow of the orchestration:

    1. Raw data is placed into raw_data_bucket.
    2. When the Workflow is executed, it initiates an HTTP GET request to the Cloud Function's HTTP trigger.
    3. The Cloud Function, referenced by preprocess_function, receives the event, processes the raw data, and outputs the results. (The actual function code is not shown here and must be developed in accordance with your preprocessing logic).
    4. (Not included in this program) Further steps for training can be added to the Workflow, or additional functions can be called as needed.

    Remember, there should be a zip file containing the Cloud Function code hosted somewhere accessible to your cloud environment. Replace "source-code-bucket" and "preprocess_function.zip" with your actual bucket name and file where your Cloud Function's source code is stored.

    Additionally, you may need to customize the Cloud Function's entry_point and the Workflow's source_contents to match the specifics of your ML data preprocessing tasks.

    This program should be run using Pulumi CLI. After setting up Pulumi and configuring access to your GCP account, you can deploy this stack with the pulumi up command. Once the resources are created, they can be managed or updated as needed with Pulumi.

    Note: Ensure that you have appropriate permissions to create and manage GCP resources in your GCP account. The IAM roles and permissions must be configured to allow the Cloud Function invocations and managing Cloud Storage and Workflows resources.