Check Bimanual training data

Run this before loading model weights. Supply a completed local Bimanual bundle containing manifest.json, samples.json, encoded assets and resources/. The command reads the bundle; it does not create training data or change files.

python -m cosmos_framework.scripts.validate_bimanual_data --bundle /data/bimanual

The static check uses only the Python standard library. It checks sample counts, unique identities, transform references, source-row mappings, and the presence of local assets, normalization statistics and tokenizer files. Missing files and paths outside the bundle fail, including symlinks that escape it. Success reports passed: true and materialized: false; media has not been decoded.

With the Framework training dependencies installed, check one packed sample through the native stage loader:

python -m cosmos_framework.scripts.validate_bimanual_data \
  --bundle /data/bimanual --materialize --stage 0

Choose the stage you intend to train (0, 1, 2 or 3). The native loader selects the bundle's robot source and materializes its user-selected time windows. Window length is not fixed by this loader; each sample must preserve the native T+1 observation / T action alignment. It runs the existing decoding, stage-specific rendering, action transforms, tokenization and packing. The check uses seed 42, zero worker processes and a one-sample batch limit; Stage 0 retains its 400,000-token ceiling. It does not alter the training recipe.

Success reports materialized: true, the batch's sample/token counts and tensor shapes/dtypes. Non-finite tensors fail. Inspect additional representative windows before training. No model checkpoint or optimizer is required.

The versioned single-source robot example and its checksum are published under examples/training. Use its layout as a reference when constructing a user-owned bundle. The example is intentionally small and validates integration, not convergence or dataset quality. Framework does not convert raw robot data into this bundle.

For released-model inference inputs, use tools/check_bimanual_mixed_inputs.py instead; an inference request is not a training bundle.