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PyTorch Operator

Kubeflow PyTorch-Job Training Operator

PyTorch is a Python package that provides two high-level features:

  • Tensor computation (like NumPy) with strong GPU acceleration

  • Deep neural networks built on a tape-based autograd system

You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed. More information at https://github.com/kubeflow/pytorch-operator **or the PyTorch site https://pytorch.org/

Quick Start

As usual, let's deploy PyTorch with one single line command

k3ai apply pytorch-op

Test You PyTorch-Job installation

We will use the MNISE example from the Kubeflow PyTorch-Job repo at https://github.com/kubeflow/pytorch-operator/tree/master/examples/mnist****

As usual, we want to avoid complexity so we re-worked a bit the sample and make it way much more easier.

Step 1

You'll see tha in the example a container need to be created before running the sample, we merged the container commands directly in the YAML file so now it's one-click job.

For CPU only

kubectl apply -f - << EOF
apiVersion: "kubeflow.org/v1"
kind: "PyTorchJob"
metadata:
  name: "pytorch-dist-mnist-gloo"
  namespace: kubeflow
spec:
  pytorchReplicaSpecs:
    Master:
      replicas: 1
      restartPolicy: OnFailure
      template:
        metadata:
          annotations:
            sidecar.istio.io/inject: "false"
        spec:
          containers:
            - name: pytorch
              image: pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime
              command: ['sh','-c','pip install tensorboardX==1.6.0 && mkdir -p /opt/mnist/src && cd /opt/mnist/src && curl -O https://raw.githubusercontent.com/kubeflow/pytorch-operator/master/examples/mnist/mnist.py && chgrp -R 0 /opt/mnist && chmod -R g+rwX /opt/mnist && python /opt/mnist/src/mnist.py']
              args: ["--backend", "gloo"]

    Worker:
      replicas: 1
      restartPolicy: OnFailure
      template:
        metadata:
          annotations:
            sidecar.istio.io/inject: "false"
        spec:
          containers:
            - name: pytorch
              image: pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime
              command: ['sh','-c','pip install tensorboardX==1.6.0 && mkdir -p /opt/mnist/src && cd /opt/mnist/src && curl -O https://raw.githubusercontent.com/kubeflow/pytorch-operator/master/examples/mnist/mnist.py && chgrp -R 0 /opt/mnist && chmod -R g+rwX /opt/mnist && python /opt/mnist/src/mnist.py']
              args: ["--backend", "gloo"]
EOF

If you have GPU enabled you may run it this way

Step 2

Check if pod are deployed correctly with

It should ouput something like this

Step 3

Check logs result of your training job

You should observe an output similar to this (since we are using 1 Master and 1 worker in this case)

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