1. Kubernetes for Distributed Tensorflow Training Jobs


    To set up Kubernetes for running distributed Tensorflow training jobs, you will need to define a Kubernetes Job resource. This resource will manage the creation of Pods that carry out the computations required for the training job. Typically, distributed Tensorflow tasks consist of multiple components like parameter servers and worker nodes, which can be modeled as different containers within the Pod or as separate Pods within the Job.

    In the example below, you will see how to define a simple Kubernetes Job that could be adapted for a distributed Tensorflow training job. We will create a Kubernetes Job with two containers: one for a parameter server and one for a worker. This is a simplified setup and for real-world cases, you might have multiple parameter servers and workers.

    In a real distributed Tensorflow setup, you would also need to configure shared volumes for data input and model output, set up inter-container communication, and manage dependencies.

    Here's a Python program using Pulumi to set up such a Kubernetes Job:

    import pulumi import pulumi_kubernetes as k8s # Create a Kubernetes Job for Tensorflow distributed training def create_tf_job(name, image, replicas): return k8s.batch.v1.Job( name, metadata=k8s.meta.v1.ObjectMetaArgs( name=name, ), spec=k8s.batch.v1.JobSpecArgs( template=k8s.core.v1.PodTemplateSpecArgs( metadata=k8s.meta.v1.ObjectMetaArgs( name=name, ), spec=k8s.core.v1.PodSpecArgs( containers=[ k8s.core.v1.ContainerArgs( name="parameter-server", image=image, command=["python", "-m", "tensorflow_parameter_server"] ), k8s.core.v1.ContainerArgs( name="worker", image=image, command=["python", "-m", "tensorflow_worker"] ) ], restart_policy="OnFailure" ), ), backoff_limit=4, ) ) # Initializes a Kubernetes Job with the specified number of replicas tf_job = create_tf_job( name="tf-distributed-job", image="tensorflow/tensorflow:latest-gpu", replicas=1 ) # Export the Job name pulumi.export('tf_job_name', tf_job.metadata['name'])

    In this program:

    • We define a function create_tf_job that sets up a Kubernetes Job resource.
    • Each Job contains two containers, one for the parameter server and another for the worker. This is where you define the Docker image to use and the commands to run when starting containers.
    • The restart_policy of "OnFailure" allows the containers to restart if the training script fails.
    • backoff_limit is the number of times Kubernetes will retry the Job before considering it failed.
    • We use pulumi.export to output the name of the created Job which can be useful for querying Job status later using kubectl.
    • The image used in this example is the official TensorFlow GPU image from Docker Hub. You'll want to replace this with the image that contains your training code.
    • In real-world scenarios, the commands will be more complex and would typically launch a script that configures and runs the TensorFlow job, including setting up distributed training with a TensorFlow cluster.

    This program is a starting point. For a production system, you would need to configure shared volumes, resource requests/limits (for CPU and memory), node selectors, and potentially a more sophisticated coordinator for the training job.

    Next steps would include setting up your Python environment to interact with a Kubernetes cluster, installing Pulumi, and running this code to deploy the Job to your cluster. For more detailed information about the Job resource, please refer to the Pulumi Kubernetes API documentation for Job.