1. Pre-configured Environments for Deep Learning on GCP.


    Creating a pre-configured environment for deep learning on Google Cloud Platform (GCP) typically involves setting up instances with adequate hardware resources, such as GPUs or TPUs, and ensuring the necessary software and libraries are installed. In our case, we will use Pulumi to define an environment suitable for deep learning tasks.

    The provided registry results point to resources such as gcp.notebooks.Environment for configuring a Cloud AI Platform Notebook environment and google-native.tpu/v2alpha1.QueuedResource for utilizing Tensor Processing Units (TPUs) for large-scale machine learning workloads.

    Our Pulumi program will define an instance in AI Platform Notebooks with a pre-installed deep learning container image that includes libraries like TensorFlow or PyTorch, which are frequently used in deep learning projects. Additionally, we may set up a TPU resource along with necessary permissions.

    Here is a step-by-step breakdown of the Pulumi program:

    1. Import the GCP and Pulumi Libraries: The program starts by importing the required packages for GCP and Pulumi.

    2. Create the AI Platform Notebook Environment: We then create an environment specifically for AI Platform Notebooks, choosing a container image pre-configured with deep learning libraries.

    3. Set Up TPU Resources (Optional): If we need to utilize TPUs, we can declare them using the google-native.tpu/v2alpha1.QueuedResource resource. This step is optional and can be included if the user desires TPU acceleration for their workloads.

    4. Exports: The program will then export any necessary endpoints or resource identifiers for the user to interact with their environment, such as the URL to access the created AI Platform Notebook.

    Let's dive into the program:

    import pulumi import pulumi_gcp as gcp # Set up a Cloud AI Platform Notebook environment pre-configured for deep learning. deep_learning_env = gcp.notebooks.Environment("deep-learning-env", # You can select a specific image that is suitable for your deep learning requirements. # This example assumes that there is a deep learning image provided by Google. containerImage=gcp.notebooks.EnvironmentContainerImageArgs( repository="gcr.io/deeplearning-platform-release", tag="tf-gpu.2-6", # This is an example tag for a Tensorflow 2.6 GPU image. ), # Alternatively, use vmImage for non-container-based environments. vmImage=gcp.notebooks.EnvironmentVmImageArgs( project="deeplearning-platform-release", imageFamily="tf-latest-gpu", # This is an example for a family with the latest Tensorflow with GPU support. ), # Location where you want to create the environment. It should be in the same region as the resources you are going to use. location="us-central1", description="Deep Learning Environment with TensorFlow and GPU support" ) # (Optional) Provision TPUs to accelerate deep learning training jobs. # This can be used in conjunction with the notebook environment if your jobs require TPUs. # Uncomment the code below if TPUs are needed. # tpu_resource = google_native.tpu.v2alpha1.QueuedResource("tpu-resource", # acceleratorType="v2-8", # Type of TPU (e.g., v2-8, v3-8) # runtimeVersion="2.6.0", # TPU runtime version, choose one compatible with your software stack # location="us-central1", # The location where to deploy the TPU # reservationName="my-reservation", # Reservation name for the TPU resource # project=pulumi.config.require("gcp:project"), # Make sure to set the GCP project in Pulumi config # queueingPolicy=google_native.tpu.v2alpha1.QueuedResourceQueueingPolicyArgs( # validAfterTime="2024-01-01T00:00:00Z", # Define the availability of the TPU resource # ), # tpu=google_native.tpu.v2alpha1.QueuedResourceTpuArgs( # acceleratorConfig=google_native.tpu.v2alpha1.QueuedResourceTpuAcceleratorConfigArgs( # type="v2-8", # Confirm the TPU type # ), # nodeSpec=... # Define specifications for the TPU node as needed # ), # ) # Export the URL where the AI Platform Notebook can be accessed. pulumi.export("notebook_url", pulumi.Output.concat( "https://console.cloud.google.com/ai-platform/notebooks/instances/", deep_learning_env.name, "?project=", deep_learning_env.project ) ) # (Optional) Export the TPU resource name if created. # Uncomment the line below if TPUs are being provisioned. # pulumi.export("tpu_resource_name", tpu_resource.name)

    This program will provision a ready-to-use deep learning environment on GCP. You might need to adjust the image tags or the families to match the specific versions of the frameworks or libraries you wish to use.

    Bear in mind that managing cloud resources often incurs costs. The prices and availability of resources like GPUs and TPUs vary by region and demand. Make sure to check the current GCP pricing and quota limits before proceeding.