mnist Model Training Example: Run MPIJob on Multi-node Cluster

The below is an mnist example for model training on Amazon EKS.

Running Multi-Gaudi Workloads

According to Kubernetes’ backoff policy, if a failure occurs, such as the worker pods are not running, the job is automatically restarted. This is useful for resuming long-running training from a checkpoint if an error causes the job to crash. For more information, refer to Kubernetes backoff failure policy.

Below is an example of a MPIJob on a MNIST model on 16 Gaudi devices.

1. Create mpijob-mnist.yaml file. Make sure to set the number of Gaudi nodes in Worker -> replicas:

apiVersion: kubeflow.org/v2beta1
kind: MPIJob
metadata:
  name: mnist-run
spec:
  slotsPerWorker: 8
  runPolicy:
    cleanPodPolicy: Running
  mpiReplicaSpecs:
    Launcher:
      replicas: 1
      template:
        spec:
          containers:
            - image: vault.habana.ai/gaudi-docker/1.15.1/ubuntu22.04/habanalabs/pytorch-installer-2.2.0:latest
              name: mnist-launcher
              command: ["/bin/bash", "-c"]
              args:
                - >-
                  HOSTSFILE=$OMPI_MCA_orte_default_hostfile;
                  MASTER_ADDR="$(head -n 1 $HOSTSFILE | sed -n s/[[:space:]]slots.*//p)";

                  NUM_NODES=$(wc -l < $HOSTSFILE);
                  CARDS_PER_NODE=8;
                  N_CARDS=$((NUM_NODES*CARDS_PER_NODE));

                  SETUP_CMD="git clone --branch 1.15.1 https://github.com/HabanaAI/Model-References.git /Model-References";
                  $SETUP_CMD;
                  mpirun --npernode 1 \
                    --tag-output \
                    --allow-run-as-root \
                    --prefix $MPI_ROOT \
                    $SETUP_CMD;

                  MODEL_PATH=/Model-References/PyTorch/examples/computer_vision/hello_world;
                  MNIST_CMD="python $MODEL_PATH/mnist.py \
                    --batch-size=64 \
                    --epochs=1 \
                    --lr=1.0 \
                    --gamma=0.7 \
                    --hpu";

                  cd $MODEL_PATH;
                  mpirun -np ${N_CARDS} \
                    --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 $MPI_ROOT \
                    -x MASTER_ADDR=$MASTER_ADDR \
                    $MNIST_CMD;
    Worker:
      replicas: 2
      template:
        spec:
          hostIPC: true
          containers:
            - image: vault.habana.ai/gaudi-docker/1.15.1/ubuntu22.04/habanalabs/pytorch-installer-2.2.0:latest
              name: mnist-worker
              resources:
                limits:
                  habana.ai/gaudi: 8
                  memory: 409Gi
                  hugepages-2Mi: 95000Mi
                  vpc.amazonaws.com/efa: 4
                requests:
                  habana.ai/gaudi: 8
                  memory: 409Gi
                  hugepages-2Mi: 95000Mi
                  vpc.amazonaws.com/efa: 4

Note

PyTorch uses shared memory buffers to communicate between processes. By default, Docker containers are allocated 64MB of shared memory. When using more than one HPU, this allocation can be insufficient. Setting hostIPC: true allows re-using the host’s shared memory space inside the container.

2. Run the job:

   kubectl apply -f mpijob-mnist.yaml
   

3. Check the job status:

   kubectl get pods -A
   

4. Retrieve the name of the created launcher pod and run the following command to see the results:

   kubectl logs <pod-name>