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Training fine-tunes π0.5 on your robot’s demos, on our GPUs. One command trains it, and one more turns it into a deployment you can run.

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What you need

  • The latest CLI: uv tool upgrade dreamscale.
  • Credits on your account: you see the price and approve it before anything starts.
  • A LeRobot dataset (v2.x or v3.0), in a local folder or on Hugging Face.

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1. Train

From Hugging Face: dreamscale train hf://your-org/towel-fold. The CLI checks the dataset, drops faulty episodes, and shows the price and time. Say yes and it uploads, starts training and shows progress on one line. It ends with Ready. Ctrl-C stops watching, not training: dreamscale train show tr_... picks it back up. The defaults choose the base model and training length for your data. To set a budget instead, add --max-usd 50. For a private repo, run dreamscale integrations hugging-face setup once.

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2. Deploy it

The build takes a few minutes and prints the command that runs it on your robot. To rename it or keep it warm, see Deployments.

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Manage runs

The dashboard’s Training page shows the same runs.