brainstorming
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or planning anything new, in software or out of it (a talk, a business, a renovation).
training-and-evaluationCompose and debug Diffusion Policy Hydra training, evaluation, Ray multirun, checkpoint evaluation, and benchmark logging commands.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when you need to build or debug Diffusion Policy training and evaluation workflows.
train.py.eval.py.ray_train_multirun.py and ray_exec.py.train_*/logs.json.txt from multirun_metrics.py-style logs.scripts/compose_experiment_config.py.train.py run or a Ray multirun.logs.json.txt, checkpoints, eval_log.json, and metrics/logs.json.txt.scripts/summarize_multirun_metrics.py to summarize finished train_* logs offline.BaseWorkspace.run() drives the training/evaluation lifecycle.BaseWorkspace.save_checkpoint() and BaseWorkspace.load_checkpoint() manage checkpoint state.policy.set_normalizer(...) and policy.predict_action(...) are the key policy hooks.env_runner.run(policy) produces rollout metrics and videos.JsonLogger, read_json_log, and TopKCheckpointManager shape the run logs.Read the bundled references for command recipes, config structure, and troubleshooting: