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Getting Started

This section takes you from installing the client to an evaluated policy and a checkpoint on your own disk. You need Python 3.11 or newer and a Simulo account; you can create the account during simulo login. You don’t need a GPU: training and evaluation run on Simulo’s cloud GPUs.

The path has nine steps:

  1. Install the simulo package and sign in.
  2. Create a training app with simulo create. It trains a cartpole from the Simulo global catalog, so it runs unchanged.
  3. Check the robot in its task with simulo run preview.py. Read every finding and fix failed checks.
  4. Train with simulo run train.py and watch the logs.
  5. List the policy the job made and download its best checkpoint.
  6. Evaluate that checkpoint with simulo run eval.py --policy policy_x:best, read the success report, and inspect a recorded failure.
  7. Continue training from that checkpoint, or export it to an ONNX bundle.
  8. Play the checkpoint back with simulo run play.py --policy policy_x:best to watch it or record it.
  9. Add tags when you submit jobs so related runs are easy to find.

The Quickstart covers the same steps on one page. The pages after it cover each step in more detail.