1. GCP AI Platform for Model Training and Deployment

    Python

    To use the Google Cloud Platform (GCP) AI Platform with Pulumi for model training and deployment, you'll typically follow these steps:

    1. Train a Machine Learning Model: You will first need to train your machine learning model using GCP AI Platform Jobs. Pulumi enables you to define and manage your training jobs as infrastructure code.

    2. Create a Model: After training, you'll create a model resource which serves as a container for your different versions of trained models.

    3. Create a Model Version: You'll then create a version of your model which points to the artifacts created by your training job.

    4. Deploy the Model: Once you have a trained model version, you deploy it to an AI Platform endpoint for serving predictions.

    Let's build out the Pulumi code that represents these steps:

    import pulumi import pulumi_gcp as gcp # Step 1: Define ML Training Job # Here you fill in 'your_training_application' with the specifics of your ML application, # filepath to the training application, and any other relevant parameters. training_job = gcp.ml.EngineTrainingJob("training-job", region="us-central1", # we use the us-central1 as an example, change to your preferred region job_id="my_training_job", training_input=gcp.ml.EngineTrainingJobTrainingInputArgs( package_uris=["gs://your-bucket/your_training_application"], python_module="trainer.task", region="us-central1", job_dir="gs://your-bucket/job-output", scale_tier="BASIC", args=[ # add arguments for training job for your application ], master_type="standard-p100", ), labels={"purpose": "example-training-job"} ) # Step 2: Create a Machine Learning Model Container # The container to register different model versions model = gcp.ml.EngineModel("example-model", name="example_model", description="This is an example ML model", regions=["us-central1"], labels={"purpose": "example-model"} ) # Step 3: Create a Model Version # After the training job completes, create a version that points to the output of the job model_version = gcp.ml.EngineModelVersion("example-model-version", name="v1", description="Version 1 of the example model", model=model.id, deployment_uri="gs://your-bucket/job-output/model", runtime_version="2.1", machine_type="n1-standard-4", labels={"version": "v1"} ) # Step 4: Deploy the Model # Once the version is created, deploy it to an endpoint for serving predictions endpoint = gcp.ml.EngineModelEndpoint("example-endpoint", name="example_endpoint", description="This is an example endpoint for ML predictions", # Deploying the model version to this endpoint deployed_models=[gcp.ml.EngineModelEndpointDeployedModelArgs( model=model.id, model_version=model_version.id, service_account="service-account@example.iam.gserviceaccount.com", )], labels={"endpoint": "example"} ) # Export the endpoint's name for easy retrieval pulumi.export("endpoint_name", endpoint.name)

    In this code:

    • Step 1: We define a gcp.ml.EngineTrainingJob resource, this is the job that'll train our model. You will have to adjust the package_uris, python_module, job_dir, and args parameters according to the specifics of your training application and GCP storage bucket.

    • Step 2: After training our model, we create a gcp.ml.EngineModel resource to serve as a container for our model versions. It effectively groups all versions under a logical entity.

    • Step 3: We then create a gcp.ml.EngineModelVersion resource which is a particular trained instance of our model, pointing to training job outputs.

    • Step 4: We deploy our trained model version to an endpoint through gcp.ml.EngineModelEndpoint. This allows us to obtain predictions from our model.

    All model-related infrastructure is codified using Pulumi, ensuring that your training jobs, models, versions, and endpoints are versioned, reproducible, and easy to manage.

    Remember to replace placeholder values such as bucket paths and service account details with actual values for your GCP project. The provided configuration assumes that you have the necessary GCP rights and that you've set up the Pulumi GCP provider.

    For more information on using each of these resources, refer to the Pulumi GCP documentation.


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