Intel Gaudi Device Plugin for Kubernetes

This is a Kubernetes device plugin implementation that enables the registration of the Intel® Gaudi® AI accelerator in a container cluster for compute workload. With the appropriate hardware and this plugin deployed in your Kubernetes cluster, you can run jobs on the Gaudi device.

The Intel Gaudi device plugin for Kubernetes is a DaemonSet that allows you to automatically:

• Enable the registration of Gaudi devices in your Kubernetes cluster.

• Keep track of device health.

Prerequisites

• Intel Gaudi software drivers loaded on the system. For more details, refer to Installation Guide.

• Kubernetes version listed in the Support Matrix.

Deploying Intel Gaudi Device Plugin for Kubernetes

1. Run the device plugin on all the Gaudi nodes by deploying the following DaemonSet using the kubectl create command. Use the associated .yaml file to set up the environment:

   kubectl create -f https://vault.habana.ai/artifactory/docker-k8s-device-plugin/habana-k8s-device-plugin.yaml
   

   Note

   kubectl requires access to a Kubernetes cluster to implement its commands. To check the access to kubectl command, run $ kubectl get pod -A.

2. Check the device plugin deployment status by running the following command:

   kubectl get pods -n habana-system
   

   Expected result:

   NAME                                       READY   STATUS    RESTARTS   AGE
   habanalabs-device-plugin-daemonset-qtpnh   1/1     Running   0          2d11h
   

Running Gaudi Jobs Example

You can create a Kubernetes job that acquires a Gaudi device by using the resource.limits field. Below is an example using Intel Gaudi’s PyTorch container image.

1. Run the job:

   cat <<EOF | kubectl apply -f -
   
   apiVersion: batch/v1
   kind: Job
   metadata:
      name: habanalabs-gaudi-demo
   spec:
      template:
         spec:
            hostIPC: true
            restartPolicy: OnFailure
            containers:
             - name: habana-ai-base-container
               image: vault.habana.ai/gaudi-docker/1.17.1/ubuntu22.04/habanalabs/pytorch-installer-2.3.1:latest
               workingDir: /root
               command: ["hl-smi"]
               securityContext:
                  capabilities:
                     add: ["SYS_NICE"]
               resources:
                  limits:
                     habana.ai/gaudi: 1
                     memory: 409Gi
                     hugepages-2Mi: 95000Mi
   EOF
   

2. Check the pod status:

   kubectl get pods
   

3. Retrieve the name of the pod and see the results:

   kubectl logs <pod-name>
   

Note

After setting up your Kubernetes cluster, use Prometheus Metric Exporter to collect the Gaudi device metrics.

Running Gaudi MNIST Training Job Example

Below is an example of training a MNIST PyTorch model using Intel Gaudi’s PyTorch container image.

1. Create a mnist.yaml: file.

   apiVersion: batch/v1
   kind: Job
   metadata:
     name: mnist-demo
   spec:
     template:
       spec:
         hostIPC: true
         restartPolicy: OnFailure
         containers:
           - name: mnist
             image: vault.habana.ai/gaudi-docker/1.17.1/ubuntu22.04/habanalabs/pytorch-installer-2.3.1:latest
             command: ["/bin/bash", "-c"]
             args:
               - >-
                 git clone --branch 1.17.1 https://github.com/HabanaAI/Model-References.git /Model-References;
                 MODEL_PATH=/Model-References/PyTorch/examples/computer_vision/hello_world;
                 cd $MODEL_PATH;
   
                 MNIST_CMD="python mnist.py \
                   --batch-size=64 \
                   --epochs=1 \
                   --lr=1.0 \
                   --gamma=0.7 \
                   --hpu";
   
                 mpirun -np 8 \
                   --allow-run-as-root \
                   --bind-to core \
                   --map-by ppr:4:socket:PE=6 \
                   -rank-by core --report-bindings \
                   --tag-output \
                   --merge-stderr-to-stdout --prefix $MPI_ROOT \
                   $MNIST_CMD;
             securityContext:
               capabilities:
                 add: ["SYS_NICE"]
             resources:
               limits:
                 habana.ai/gaudi: 8
                 memory: 409Gi
                 hugepages-2Mi: 95000Mi
               requests:
                 habana.ai/gaudi: 8
                 memory: 409Gi
                 hugepages-2Mi: 95000Mi
   

2. Run the job:

   kubectl apply -f mnist.yaml
   

3. Check the pod status:

   kubectl get pods
   

4. Retrieve the name of the pod and see the results:

   kubectl logs <pod-name>