1. Kubernetes Workflows for Distributed Machine Learning Training


    In order to set up distributed machine learning (ML) training on Kubernetes, you'll need to define a workflow that involves multiple components. This might include:

    • Kubernetes Cluster: You'll need an existing Kubernetes cluster where you can schedule training jobs.
    • Persistent Storage: Many ML workflows require access to large datasets. Kubernetes supports persistent volumes that can be used for this purpose.
    • Training Jobs: Kubernetes Jobs can be used to run the ML training tasks. For distributed training, each job might run a segment of the training data.
    • Horizontal Pod Autoscaler (HPA): Optionally, to automatically scale the number of Pods in a deployment or replica set based on observed CPU utilization or custom metrics.
    • Machine Learning Frameworks: Popular ML frameworks like TensorFlow, PyTorch, or MPI (Message Passing Interface) can be set up to run in distributed mode across multiple nodes.

    Below is a basic Pulumi program in Python that sets up a simple Kubernetes Job for machine learning training tasks. The example assumes that you have a Kubernetes cluster set up and kubectl configured to connect to it. This program will not cover the setup of the ML framework itself or the specifics of distributed training, as these are highly dependent on the framework and model you're working with.

    This program will:

    1. Create Kubernetes PersistentVolumeClaim to provide storage that the ML training jobs can use to access datasets.
    2. Define a Kubernetes Job to run the training tasks.
    3. Set up ConfigMap to share configuration across training Pods (e.g., hyperparameters).
    import pulumi import pulumi_kubernetes as k8s # Create a Kubernetes PersistentVolumeClaim for dataset storage persistent_volume_claim = k8s.core.v1.PersistentVolumeClaim( "ml-data-pvc", metadata=k8s.meta.v1.ObjectMetaArgs( name="ml-data", ), spec=k8s.core.v1.PersistentVolumeClaimSpecArgs( access_modes=["ReadWriteOnce"], # Typical for single node training jobs resources=k8s.core.v1.ResourceRequirementsArgs( requests={ "storage": "100Gi" # Request 100 GiB of storage }, ), ) ) # Define a Kubernetes Job for ML training training_job = k8s.batch.v1.Job( "ml-training-job", metadata=k8s.meta.v1.ObjectMetaArgs( name="ml-training", ), spec=k8s.batch.v1.JobSpecArgs( template=k8s.core.v1.PodTemplateSpecArgs( metadata=k8s.meta.v1.ObjectMetaArgs( labels={"job": "ml-training"}, ), spec=k8s.core.v1.PodSpecArgs( containers=[k8s.core.v1.ContainerArgs( name="trainer", image="your-ml-training-container-image", # Replace with your training container image args=["--epochs", "10"], # Example arguments for the training application volume_mounts=[k8s.core.v1.VolumeMountArgs( mount_path="/data", name="data-volume", )], )], restart_policy="Never", volumes=[k8s.core.v1.VolumeArgs( name="data-volume", persistent_volume_claim=k8s.core.v1.PersistentVolumeClaimVolumeSourceArgs( claim_name=persistent_volume_claim.metadata.name, ), )], ), ), backoff_limit=1, # How many times to retry the job upon failure ), ) # Create a ConfigMap with training configuration data (e.g., hyperparameters) config_map = k8s.core.v1.ConfigMap( "ml-config", metadata=k8s.meta.v1.ObjectMetaArgs( name="ml-hyperparameters", ), data={ "learning_rate": "0.01", "batch_size": "32", }, ) # Export the PersistentVolumeClaim and Job names pulumi.export("persistent_volume_claim", persistent_volume_claim.metadata.name) pulumi.export("training_job", training_job.metadata.name) pulumi.export("config_map", config_map.metadata.name)
    • The PersistentVolumeClaim named ml-data-pvc is a request for storage in the cluster. It asks for 100 GiB of space which can be used by your ML training jobs to store datasets or model checkpoints.

    • The Job named ml-training-job is a Kubernetes Job that would run your training container image. Be sure to replace your-ml-training-container-image with the name of your actual Docker image.

    • The ConfigMap named ml-config contains configuration data that can be used by the training application. In this case, it's holding values for learning_rate and batch_size, but you can add any other configurations your training might need.

    You would usually run your actual ML training application within the container specified in training_job. This application would access the cluster resources to perform computation. The extent and complexity of the setup would depend on whether you need multi-node and/or multi-GPU training, fault tolerance, streaming input data, etc.

    You can create the resources in your Kubernetes cluster by running this Pulumi program. Then, use kubectl or your cluster dashboard to monitor the state of the Job and access logs from the training process.