1. Kubernetes for Distributed Deep Learning with Ray


    Distributed deep learning tasks can be greatly enhanced by utilizing Kubernetes for managing and scaling the compute workloads. In this use case, we are going to focus on setting up a Kubernetes cluster which can serve as the underlying infrastructure for running distributed deep learning workloads with Ray, an open-source distributed computing system.

    Ray provides a simple, universal API for building distributed applications. When it comes to deep learning and machine learning workloads, Ray has specific libraries like Ray SGD and Ray Tune, which make it an excellent choice for scaling such tasks. However, Ray isn’t specific to just deep learning and can be used for a wide array of distributed computing tasks.

    For our Kubernetes cluster on which Ray will be deployed, we can choose from several cloud providers such as AWS, GCP, Azure, etc. In this example, let's take Google Kubernetes Engine (GKE) as our cloud provider, as it offers deep integration with Kubernetes and is a robust platform for running containerized applications.

    Here's how you can create a GKE cluster using Pulumi with Python:

    1. Pulumi Configuration: To start with Pulumi, you need to have a Pulumi account and have the Pulumi CLI installed on your machine. Please configure your access to Google Cloud Platform by setting up the necessary credentials.

    2. Setting Up the Python Environment: Ensure you have Python installed and create a new virtual environment for your project:

      python -m venv venv source venv/bin/activate
    3. Installing Pulumi GCP Package: Install the necessary Pulumi GCP package which allows you to work with GCP:

      pip install pulumi_gcp
    4. Creating a Pulumi Project: Once the setup is ready, you can create a new Pulumi project:

      pulumi new python

      Follow the prompts to set up your project. This will create necessary configurations and boilerplate files.

    5. Writing the Pulumi Program: In the __main__.py file, you can define the infrastructure for the Kubernetes cluster.

    Below is a detailed explanation followed by the Python program which creates a GKE cluster that can later be configured to run Ray for distributed deep learning:

    import pulumi import pulumi_gcp as gcp # Create a GKE cluster cluster = gcp.container.Cluster("ray-cluster", initial_node_count=3, node_version="latest", min_master_version="latest", node_config={ "machineType": "n1-standard-4", # Choose machine types based on your workload needs "oauth_scopes": [ "https://www.googleapis.com/auth/compute", "https://www.googleapis.com/auth/devstorage.read_only", "https://www.googleapis.com/auth/logging.write", "https://www.googleapis.com/auth/monitoring" ], }, ) # Export the Cluster name and the Kubeconfig kubeconfig = pulumi.Output.all(cluster.name, cluster.endpoint, cluster.master_auth).apply(lambda args: """ apiVersion: v1 clusters: - cluster: certificate-authority-data: {cert} server: https://{endpoint} name: {name} contexts: - context: cluster: {name} user: {name} name: {name} current-context: {name} kind: Config preferences: {{}} users: - name: {name} user: auth-provider: config: cmd-args: config config-helper --format=json cmd-path: gcloud expiry-key: '{{.credential.token_expiry}}' token-key: '{{.credential.access_token}}' name: gcp """.format(name=args[0], endpoint=args[1], cert=args[2]['cluster_ca_certificate'])) pulumi.export("cluster_name", cluster.name) pulumi.export("kubeconfig", kubeconfig)

    This Pulumi program will create a GKE cluster:

    • pulumi_gcp.container.Cluster: This class is used to create the GKE cluster. It is equivalent to running gcloud container clusters create command but as code.
    • Node Configuration: The node_config parameter is a dictionary that defines the properties of worker nodes in the cluster such as machine type and OAuth scopes necessary for the nodes to access GCP services.
    • kubeconfig Output: After the cluster is created, we generate a kubeconfig that can be used to interact with the Kubernetes cluster using kubectl or other Kubernetes management tools.
    • pulumi.export(): Exports the cluster name and kubeconfig as stack outputs. This can be used for further interaction with the cluster after it has been provisioned.

    With this setup, you now have a Kubernetes cluster that can be used to deploy Ray or other frameworks and workloads. You would need to follow Ray’s deployment guides to get Ray set up on your new Kubernetes cluster.