Auto-Scaling GPU Clusters for Deep Learning on Kubernetes

Question

Pulumi · Accepted Answer

Auto-scaling GPU clusters for deep learning on Kubernetes involves creating a scalable infrastructure that can automatically adjust the number of GPU-enabled nodes based on the processing demand. Kubernetes, being a container orchestration system, supports the use of GPU resources, and by leveraging its auto-scaling features, one can create a dynamic environment that is cost-efficient and highly available for deep learning workloads.

To achieve this, you would typically use the following Kubernetes resources:
- **NodePools**: Groups of nodes within a Kubernetes cluster, which can have a specific configuration and size. For GPU workloads, you would create a node pool where each node is equipped with one or more GPUs.
- **Horizontal Pod Autoscaler (HPA)**: A Kubernetes resource that automatically scales the number of pods in a deployment or replica set based on observed CPU utilization (or, with custom metrics, on some other application-provided metrics).
- **Cluster Autoscaler**: A tool that automatically adjusts the size of the Kubernetes cluster when:
  - there are pods that fail to run in the cluster due to insufficient resources.
  - some nodes in the cluster are so underutilized, for an extended period, that their workload could be moved to other, less loaded nodes.

Here is a Pulumi Python program that sets up an auto-scaling GPU cluster for deep learning using Google Kubernetes Engine (GKE) as an example:

```python
import pulumi
from pulumi_gcp import container, compute

# Configurations for the GKE cluster.
project = 'my-project-id'  # Replace with your GCP project ID
region = 'us-central1'  # Replace with your preferred GCP region
cluster_name = 'gpu-cluster'
node_pool_name = 'gpu-node-pool'
gpu_type = 'nvidia-tesla-v100'  # Replace with your preferred GPU type
min_nodes = 1  # Minimum number of nodes in the node pool
max_nodes = 5  # Maximum number of nodes in the node pool

# Create a GKE cluster.
cluster = container.Cluster(cluster_name,
                            initial_node_count=1,
                            location=region,
                            resource_labels={'auto-scaling': 'true'},
                            min_master_version='latest',
                            node_config={
                                'oauthScopes': [
                                    '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'
                                ]
                            },
                            project=project)

# Create a node pool for GPU-enabled nodes.
gpu_node_pool = container.NodePool(node_pool_name,
                                   cluster=cluster.name,
                                   location=cluster.location,
                                   autoscaling={
                                       'minNodeCount': min_nodes,
                                       'maxNodeCount': max_nodes
                                   },
                                   node_config={
                                       'oauthScopes': [
                                           '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'
                                       ],
                                       'guestAccelerators': [{
                                           'type': gpu_type,
                                           'count': 1
                                       }],
                                       # Additional configurations such as disk size, image type etc.
                                   },
                                   initial_node_count=min_nodes,
                                   version='latest',
                                   management={
                                       'autoRepair': True,
                                       'autoUpgrade': True
                                   },
                                   project=project)

# Export the cluster name and kubeconfig.
pulumi.export('cluster_name', cluster.name)
# The kubeconfig section needs to be dynamically generated based on the cluster's endpoint and credentials.
kubeconfig = pulumi.Output.all(cluster.name, cluster.endpoint, cluster.master_auth).apply(
    lambda args: """apiVersion: v1
clusters:
- cluster:
    certificate-authority-data: {ca_certificate}
    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], ca_certificate=args[2]['clusterCaCertificate']))

pulumi.export('kubeconfig', kubeconfig)
```

In this program, we are setting up the following:
- A GKE cluster named `gpu-cluster` that will house our GPU workloads.
- A node pool named `gpu-node-pool` which contains GPU-enabled nodes, suitable for running deep learning workloads.
- The `autoscaling` configuration within the node pool resource to define the minimum and maximum number of nodes.
- The `node_config` within the node pool specifies the GPU type to use and sets the necessary OAuth scopes.
- Finally, we export the `cluster_name` and dynamically generate the `kubeconfig`, which you will use to interact with your Kubernetes cluster.

Remember, this is just an example using Google Kubernetes Engine. Depending on your cloud provider, the specifics of the implementation might differ, but the general approach remains the same. You will need to adjust the program to align with the specific requirements of your cloud provider and the GPU workloads you plan to run.