diffusion_policy/AGENTS.md

Agent Notes

Purpose

~/diffusion_policy is the Diffusion Policy training repo. The main workflow here is Hydra-driven training via train.py, with the canonical PushT image experiment configured by image_pusht_diffusion_policy_cnn.yaml.

Top Level

• diffusion_policy/: core code, configs, datasets, env runners, workspaces.
• data/: local datasets, outputs, checkpoints, run logs.
• train.py: main training entrypoint.
• eval.py: checkpoint evaluation entrypoint.
• image_pusht_diffusion_policy_cnn.yaml: canonical single-seed PushT image config from the README path.
• .venv/: local uv-managed virtualenv.
• .uv-cache/, .uv-python/: local uv cache and Python install state.
• README.md: upstream instructions and canonical commands.

Canonical PushT Image Path

• Entrypoint: python train.py --config-dir=. --config-name=image_pusht_diffusion_policy_cnn.yaml
• Dataset path in config: data/pusht/pusht_cchi_v7_replay.zarr
• README canonical device override: training.device=cuda:0

Data

• PushT archive currently present at data/pusht.zip
• Unpacked dataset used by training: data/pusht/pusht_cchi_v7_replay.zarr

Local Compatibility Adjustments

• diffusion_policy/env_runner/pusht_image_runner.py now uses SyncVectorEnv instead of AsyncVectorEnv. Reason: avoid shared-memory and semaphore failures on this host/session.
• diffusion_policy/gym_util/sync_vector_env.py has local compatibility changes:
  • added reset_async
  • seeded reset_wait
  • updated concatenate(...) call order for the current gym API

Environment Expectations

• Use the local uv env at .venv
• Verified local Python: 3.9.25
• Verified local Torch stack: torch 2.8.0+cu128, torchvision 0.23.0+cu128
• Other key installed versions verified in .venv:
  • gym 0.23.1
  • hydra-core 1.2.0
  • diffusers 0.11.1
  • huggingface_hub 0.10.1
  • wandb 0.13.3
  • zarr 2.12.0
  • numcodecs 0.10.2
  • av 14.0.1
• Important note: this shell currently reports torch.cuda.is_available() == False, so always verify CUDA access in the current session before assuming GPU is usable.

Logging And Outputs

• Hydra run outputs: data/outputs/...
• Per-run files to check first:
  • .hydra/overrides.yaml
  • logs.json.txt
  • train.log
  • checkpoints/latest.ckpt
• Extra launcher logs may live under data/run_logs/

Practical Guidance

• Inspect with rg, sed, and existing Hydra output folders before changing code.
• Prefer config overrides before code edits.
• On this host, start from these safety overrides unless revalidated:
  • logging.mode=offline
  • dataloader.num_workers=0
  • val_dataloader.num_workers=0
  • task.env_runner.n_envs=1
  • task.env_runner.n_test_vis=0
  • task.env_runner.n_train_vis=0
• If a run fails, inspect .hydra/overrides.yaml, then logs.json.txt, then train.log.
• Avoid driver or system changes unless the repo-local path is clearly blocked.