1. Distributed TensorFlow Training on Kubernetes with Rook-Ceph


    To set up a distributed TensorFlow training environment on Kubernetes with Rook-Ceph for storage, we'll walk through a few steps. Kubernetes will orchestrate the containers required for TensorFlow training, and Rook-Ceph will provide a distributed file system that is accessed by these containers for input/output operations.

    Here are the general steps we need to follow:

    1. Set up a Kubernetes cluster.
    2. Install Rook and configure Ceph as the storage backend within the Kubernetes cluster.
    3. Deploy the TensorFlow training job with the necessary Kubernetes resources like Deployments, Services, and Persistent Volumes that bind to the Ceph storage.

    We will be using the following Pulumi resources for this setup:

    • Kubernetes Cluster: You can choose any cloud provider like AWS, Azure, or GCP to create a Kubernetes cluster. For this example, we will use Google Kubernetes Engine (GKE) for simplicity.

      • google_native.container.v1.Cluster: Represents a GKE cluster.
    • Rook-Ceph: Since this is a collection of Kubernetes resources like Custom Resource Definitions (CRDs), deployments, and operators, we will apply these YAML files using Pulumi's Kubernetes resources.

      • kubernetes.yaml.ConfigGroup: Represents a group of Kubernetes resources defined in YAML files.
    • TensorFlow Training Job: This will also be a set of Kubernetes resources including a deployment that will manage our distributed TensorFlow training pods and a service to expose necessary endpoints.

      • kubernetes.apps.v1.Deployment: Represents a Kubernetes Deployment for running TensorFlow training pods.
      • kubernetes.core.v1.Service: Represents a Kubernetes Service to expose TensorFlow's distributed training endpoints.

    Let's now set up a Pulumi program that accomplishes this setup.

    import pulumi import pulumi_kubernetes as k8s from pulumi_google_native.container import v1 as gke # Step 1: Create a GKE cluster # Replace the project and other configuration parameters with your own details. cluster_name = "tensorflow-training-cluster" gke_cluster = gke.Cluster(cluster_name, parent="projects/my-gcp-project/locations/us-central1", initialNodeCount=3, nodeConfig=gke.NodeConfigArgs( machineType="n1-standard-1" ) ) # Step 2: Install Rook and configure Ceph in the GKE cluster # Rook-Ceph YAML files are applied using ConfigGroup. # For Rook-Ceph, you usually have a set of YAML files that you would apply to your # Kubernetes cluster. With Pulumi, you need to refer to these files or inline the # required configurations directly within the code. # For this example, let’s assume we have rook-ceph YAML files in a directory `/path/to/rook-ceph-yamls`. rook_ceph = k8s.yaml.ConfigGroup( "rook-ceph", files=["/path/to/rook-ceph-yamls/*.yaml"], opts=pulumi.ResourceOptions(provider=gke_cluster), ) # Step 3: Deploy the TensorFlow job with a persistent volume backed by Ceph. # This is an example configuration. Replace it with the actual job spec and # persistent volume configurations that your TensorFlow job requires. # TensorFlow job ConfigMap with training script tf_config_map = k8s.core.v1.ConfigMap( "tf-job-script", metadata={"name": "tf-job-script"}, data={"training.py": "s = 'Hello, TensorFlow!'\nprint(s)"}, opts=pulumi.ResourceOptions(provider=gke_cluster), ) # TensorFlow job deployment tf_deployment = k8s.apps.v1.Deployment( "tf-job-deployment", spec={ "selector": { "matchLabels": {"app": "tensorflow"} }, "replicas": 2, "template": { "metadata": { "labels": {"app": "tensorflow"} }, "spec": { "containers": [{ "name": "tensorflow", "image": "tensorflow/tensorflow:latest", "command": ["python", "/scripts/training.py"], "volumeMounts": [{ "mountPath": "/scripts", "name": "script-volume" }] }], "volumes": [{ "name": "script-volume", "configMap": { "name": tf_config_map.metadata["name"] } }] } } }, opts=pulumi.ResourceOptions(provider=gke_cluster, depends_on=[rook_ceph]), ) # Export the GKE cluster's name and endpoint pulumi.export('cluster_name', gke_cluster.name) pulumi.export('cluster_endpoint', gke_cluster.endpoint)

    In the code above, replace "my-gcp-project" with your GCP project ID, and adjust the location and the size of the nodes according to your requirements. The rook-ceph directory should contain all necessary YAML files to deploy Rook-Ceph in your Kubernetes cluster.

    You'll need to create the TensorFlow job specifications and persistent volume claims as per your training job's requirement and ensure that they are configured to use the Ceph storage provided by Rook.

    For a real-world scenario, you might have to add other configurations such as node pools, network policies, and more sophisticated resource definitions for your TensorFlow job. Furthermore, keep in mind that the paths to Rook configuration files, TensorFlow training scripts, and Docker images in the code samples are placeholders that you should replace with actual paths.

    What I've outlined above is a simplified representation to get you started with the setup. The actual implementation might require more nuanced error handling, more complex configuration, and consideration of security best practices.