executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
trainingPlan OpenFold training, fine-tuning, validation, distillation, checkpoint, DeepSpeed, and distributed commands safely.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when the task is to plan or troubleshoot OpenFold training, fine-tuning, distributed execution, DeepSpeed configuration, checkpoint resume, validation, distillation, or train_openfold.py command construction.
references/training-cli-reference.md for required positionals, key flags, validation rules, and safe command-building conventions.references/training-workflows.md for initial training, fine-tuning, resume, validation, distillation, logging, checkpoints, and experiment config overrides.references/distributed-and-deepspeed.md for GPU/node strategy, --seed, MPI, Slurm handoff, DeepSpeed JSON, BF16/A100 guidance, and precision conflicts.references/troubleshooting.md for missing caches/indexes, runtime imports, GPU impossibility, logging, DeepSpeed, and checkpoint failures.scripts/build_training_command.py to assemble a validated dry-run command; it prints shell-quoted argv and never launches training.scripts/build_deepspeed_config.py to generate a deterministic DeepSpeed config JSON without importing OpenFold or DeepSpeed.templates/experiment_config.json when the user needs a flattened --experiment_config_json override.../data-preparation/.../installation-assets/.../model-apis/.../inference/.--seed; OpenFold rejects distributed training without it.--precision bf16 or bf16-mixed depending on the installed Lightning version and local policy) on A100-class hardware and do not combine DeepSpeed with Lightning precision 16.attn_core_inplace_cuda are missing, document the environment gap instead of claiming full CLI verification.