TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs). TensorFlowJob is a Kubernetes Custom Resource and Operator for TensorFlow jobs

Deploy the TensorflowJob operator

Before you can use the tfjob runtime, you need to make sure that TensorflowJob operator and the CRD (custom resource definition) are deployed in your cluster.

Enable the operator

To be able to schedule distributed jobs with the TensorflowJob operator, you need to enable the operator in your deployment config.

You need to enable the operator in Polyaxon CE deployment or Polyaxon Agent deployment:

  tfjob: true

Create a component with the tfjob runtime

Once you have the TensorflowJob operator running on a Kubernetes namespace managed by Polyaxon, you can check the specification for creating components with the tfjob runtime:

version: 1.1
kind: component
  kind: tfjob

Run the distributed job

Running components with the tfjob runtime is similar to running any other component:

polyaxon run -f manifest.yaml -P ...

View a running operation on the dashboard

After running an operation with this component, you can view it on the Dashboard:

polyaxon ops dashboard


polyaxon ops dashboard -p [project-name] -uid [run-uuid] -y

Stop a running operation

To stop a running operation with this component:

polyaxon ops stop


polyaxon ops stop -p [project-name] -uid [run-uuid]

Run the job using the Python client

To run this component using Polyaxon Client:

from polyaxon.client import RunClient

client = RunClient(...)
client.create_from_polyaxonfile(polyaxonfile="path/to/file", ...)