Kubernetes Workflows for Distributed Machine Learning Training

Question

Pulumi · Accepted Answer

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).

```python
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.