1. Vertex AI for Distributed Deep Learning Jobs


    To use Vertex AI to run distributed deep learning jobs, you will interact with various components of Vertex AI's ecosystem, such as datasets, models, endpoints, and training jobs. The focus here is to create the necessary infrastructure for running distributed training jobs. For this purpose, you would typically define the training job configuration, including machine types and the number of replicas for distributed training.

    In Pulumi's Python SDK, you will set up these resources and then execute a training job with the specified configuration. The code below demonstrates how to define a simple Vertex AI Training Job using the Google Cloud provider in Pulumi.

    Firstly, make sure you have already configured your Google Cloud credentials for Pulumi. This usually involves having the gcloud CLI installed and being authenticated, or setting the appropriate environment variables with your credentials.

    Here is a Pulumi program that sets up a Vertex AI CustomJob for running a distributed deep learning job using TensorFlow. We would specify a worker pool configuration appropriate for a distributed TensorFlow job.

    import pulumi import pulumi_gcp as gcp # Create a Vertex AI custom job for distributed training. custom_job = gcp.vertex.AIJob( "distributed-training-job", display_name="my-distributed-training-job", training_task_definition="gs://google-cloud-aiplatform/schema/trainingjob/definition/custom_task_1.0.0.yaml", project=gcp.config.project, # Assuming default project is set in Pulumi. location=gcp.config.region, # Assuming default region is set in Pulumi. training_task_inputs={ "worker_pool_specs": [ { # Main worker pool "machine_spec": { "machine_type": "n1-standard-4", # For GPU-based training you can specify 'accelerator_type' and 'accelerator_count'. }, "replica_count": "1", # One "master" node. "container_spec": { "image_uri": "gcr.io/my-project/my-tensorflow-training-container:latest", # Replace with your training container image. "args": [], # Add necessary command-line arguments for your training application. }, }, { # Secondary worker pool "machine_spec": { "machine_type": "n1-standard-4", # Omitting the accelerator for the secondary worker pool. }, "replica_count": "2", # Number of "worker" nodes for distributing training. "container_spec": { "image_uri": "gcr.io/my-project/my-tensorflow-training-container:latest", # Same image for workers. "args": [], # Command-line arguments can be the same or different based on your job's needs. }, } ] }, ) # Export the id of the training job. pulumi.export("training_job_id", custom_job.id)

    In the above program, we are defining a custom_job that specifies the configuration for distributed deep learning training. We use a YAML definition for a custom training job and provide two worker pool specifications:

    • The main worker pool has one replica; it's like the "master" node in a distributed training context.
    • The secondary worker pool has two replicas, which are the "worker" nodes where the actual training will be distributed.

    Each worker pool has a machine_spec that defines the machine type for training. You can also specify the number of GPUs needed by providing accelerator_type and accelerator_count. For this example, we're assuming we're not using GPUs.

    The container_spec specifies the Docker container that will be used to run the training. This container should have everything needed to run your training job, including TensorFlow and any other dependencies.

    Please replace my-project, my-region, and my-tensorflow-training-container:latest with your respective Google Cloud project ID, region, and the URI of your container image in the Google Container Registry.

    After defining the job, we export the job ID, which can be useful for querying the job status or for logging and monitoring purposes.

    Finally, this approach allows customizing the training job to your specific need and scales the complexity with the requirements of the distributed training job.