Let’s look at an example of how you can use Polyaxon for running deep learning experiments.

Deploy local cluster

This example assumes a functional Polyaxon Deployment. If you have Polyaxon already deployed and running, you can skip this section and proceed to create a project. Otherwise, this section will help you deploy a local Polyaxon cluster with default values.

Note: Minikube is not meant to be a production environment.

Before you can deploy Polyaxon, make sure you have the following:

Deploy Polyaxon with default config values on Minikube:

polyaxon admin deploy -t minikube

Wait for all pods to reach the running state:

kubectl get pods -n polyaxon

Expose Polyaxon UI on your localhost:

polyaxon port-forward -t minikube

Tip: You can learn more about how to customize your Polyaxon Deployment in the setup section.

Create a project

You can create a project using Polyaxon UI or with Polyaxon CLI

This example uses a public Github repo for hosting the project and the Polyaxonfile manifests, similar results can be achieved using a local folder or other platforms, e.g. GitLab, Bitbucket, ...

Start an experiment

Let's run a first experiment

$ polyaxon run --url=https://raw.githubusercontent.com/polyaxon/polyaxon-quick-start/master/experimentation/simple.yml -l

For more details about this command please run polyaxon run --help, or check the command reference

The -l flag indicates that we want to stream the logs after starting the experiment.

Start a Tensorboard

Let's start a tensorboard to see the results:

$ polyaxon run --hub tensorboard:single-run -P uuid=UUID -w


Let's check the results on the dashboard as well

$ polyaxon dashboard -y

For more details about this command please run polyaxon dashboard --help, or check the command reference

We can see that Polyaxon has logged some information automatically about our run:


Please check the runs dashboard and the visualization section for more details.


You've trained your first experiments with Polyaxon, visualized the results in Tensorboard and tracked metrics, with two commands.

Behind the scene a couple of things have happened:

  • You synced your GitHub project and used the last commit.
  • You ran a container with a custom image and command to train a model.
  • You persisted your logs and outputs.
  • You visualized the results using Polyaxon's native dashboard and Tensorboard.

To gain a deeper understanding of what happened and how Polyaxon can help you iterate faster with your experimentation, please check the next section of this tutorial