Run Your First Experiment in Determined

In this tutorial, we’ll show you how to integrate a training example with the Determined environment. We’ll run our experiment on a local training environment requiring only a single CPU or GPU.


This tutorial is recommended as an introduction for model developers who are new to Determined AI.


Our goal is to integrate the PyTorch MNIST training example into Determined in four steps:

  • Download and extract the files

  • Set up our training environment

  • Run the experiment

  • View the experiment in our browser


Download the Files

To get started, we’ll first download and extract the files we need and cd into the directory.

  • Download the mnist_pytorch.tgz file.

  • Open a terminal window, extract the files, and cd into the mnist_pytorch directory:

tar xzvf mnist_pytorch.tgz
cd mnist_pytorch

Set Up Your Training Environment

To start your experiment, you’ll need a Determined cluster. If you are new to Determined AI (Determined), you can install the Determined library and start a cluster locally:

pip install determined

# If your machine has GPUs:
det deploy local cluster-up

# If your machine does not have GPUs:
det deploy local cluster-up --no-gpu

Run the Experiment

To run the experiment, enter the following command:

det experiment create const.yaml . -f

A notification displays letting you know the experiment has started.

Preparing files (.../mnist_pytorch) to send to master...
Created experiment xxx

View the Experiment

To view the experiment progress in your browser:

  • Enter the following URL: http://localhost:8080/.

This is the cluster address for your local training environment.

  • Accept the default determined username, leave the password empty, and click Sign In.

Next Steps

In four simple steps, we’ve successfully configured our training environment in Determined to start training the PyTorch MNIST example.

In this article, we learned how to run an experiment on a local, single CPU or GPU. To learn how to change your configuration settings, including how to run a distributed training job on multiple GPUs, visit the Quickstart for Model Developers.