---

name: "training" description: "Launch, resume, and troubleshoot OpenFlamingo training runs." disable-model-invocation: true metadata: disco-role: operating license: MIT

training

Use this sub-skill for OpenFlamingo training and fine-tuning tasks: configuring model and data flags, generating launch commands, selecting DDP or FSDP, resuming checkpoints, and diagnosing runtime failures.

Read first

• CLI reference
• Data formats
• Training workflows
• Troubleshooting
• Safe command builder

What this sub-skill covers

• Vision encoder, language model, tokenizer, and cross-attention setup.
• LAION and MMC4 WebDataset inputs.
• DDP, Slurm-style launches, Horovod, and FSDP caveats.
• Precision, gradient checkpointing, loss weighting, and checkpointing.
• W&B logging, offline mode, and restart behavior.

Typical flow

1. Confirm the LAION and MMC4 shard layouts and sample counts.
2. Pick DDP or FSDP and decide whether to use --dataset_resampled.
3. Generate a launch command with scripts/build_train_command.py.
4. Run the printed command from the repository root or another shell environment that can resolve the training entrypoint path.
5. Monitor run_name/checkpoint_<epoch>.pt and resume with --resume_from_checkpoint when needed.

Guardrails

• Do not launch full training from this skill without explicit user intent and the required data and model weights.
• If the language model uses tied input/output embeddings, prefer DDP or freeze LM embeddings when using FSDP.
• Keep LAION and MMC4 batch counts aligned; the training loop asserts equal batches per epoch.
• If the shard metadata is missing, supply explicit sample counts for both datasets.