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Training runs should generally succeed without intervention. If a run fails, it usually indicates an underlying issue worth investigating.

Common failures

What you see on failure

  • An error message on the training run
  • A description of the failure condition
  • A retry button

When to reach out

If training fails unexpectedly, contact support. Failures usually indicate something worth looking into — the team can help diagnose whether it’s a data issue, a recipe mismatch, or a platform problem.

Talk to an engineer

Meet with our team to discuss training failures or get help with your approach.

Training succeeded?

Model trained and eval scores look good? Deploy it to a dedicated GPU and start serving traffic.

Deploy your model

Ship your trained model to production.