Polyaxon allows to schedule Tensorflow experiments and Tensorflow distributed experiments, and supports tracking metrics, outputs, and models natively.

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:

operators:
  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
run:
  kind: tfjob
  ...

For more details about the specification for creating tfjob runtime, please check please check this section.

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

or

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

or

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", ...)