README.md

metadata

license: other
license_name: observed-training-environments-and-reproduction-recipes
license_link: >-
  https://huggingface.co/datasets/diffusion-gif/training-environments/blob/main/README.md

Ego-Exo / robot training reproduction environments

This package contains actual retained environment snapshots, exact observed package-version constraints, role-specific installation requirements, source commit locks, portable training command generators and preflight checks. It is not a claim that a clean GPU reinstall or an eight-GPU training/metric reproduction was completed on this machine.

Four separate profiles — do not mix them

Profile	Python	Torch / CUDA	Transformers	Purpose
egoexo-generator	3.11.15	2.9.1+cu130 / 13.0	4.54.0	Wan/VACE HOI, caption Global/Spatial generation training
egoexo-captioner-phiseg	3.12.14	2.10.0+cu128 / 12.8	4.57.6	Qwen3-VL-8B/ms-swift4.4.2 captioner + PhiSeg
robot-sft-grpo	3.12.13	2.11.0+cu130 / 13.0	5.9.0	Original 27B mix6 SFT / verl0.9.1 + vLLM0.24.0 GRPO
robot-data-cpu	3.12.13	no Torch required	not required	data restoration, validation and CPU tests

The 27B checkpoint nickname is Qwen3.8; its actual architecture is qwen3_5. It requires the 5.x Transformers stack. Do not use the generator's Transformers 4.54 environment for that model or mix vLLM0.19/Torch2.10 with vLLM0.24/Torch2.11.

Create a NEW environment

Choose/create the exact listed Python interpreter first (Conda explicit package URLs with MD5 are supplied for the observed Conda profiles). Never install into an existing training environment by mistake.

python tools/install_environment.py --profile robot-sft-grpo --python /path/to/python3.12.13 --prefix /path/to/new_env
# inspect the plan; then add --execute
/path/to/new_env/bin/python tools/verify_environment.py --profile robot-sft-grpo --gpu-basic-check

requirements.observed-full.lock.txt captures the retained inventory; requirements.core.in selects training-role dependencies while using that inventory as constraints. The CPU profile additionally provides a resolved hash-locked install file. Full GPU clean installation has not been revalidated here. FlashAttention is a post-Torch build step (--flash-attn --cuda-home /path/to/cuda13), not an uncontrolled build-isolation install before Torch exists.

CUDA and platform pitfalls actually found

• This host has driver580.126.20 / one SM12.0 GPU. Three GPU profiles passed a tiny BF16 CUDA tensor operation; that is not eight-GPU model training.
• The system toolkit here is nvcc12.8, even when Torch's wheel runtime is13.0. Use a matching CUDA13 devel toolkit for torch-cu130 source builds and FlashAttention/GDN kernels. A driver reporting CUDA13 is not an nvcc installation.
• Historical robot GRPO uses TP4 and eight GPUs; one-GPU runs are not equivalent.
• The retained Decord0.6.0 wheel has an incorrect internal cp36 tag although its ctypes library imports on Python3.11/3.12. Its original pip-check platform warning is recorded, not hidden. Use the official py3 Linux wheel; validate import/video decoding for your host before training. Do not classify that warning as a proven numerical-model failure or claim all fresh wheel installations passed.
• The uv-created robot env has no pip module. uv pip check --python ENV/bin/python passed all256 installed packages; python -m pip check alone is not a valid dependency-health test of that original no-pip environment.

Get exact source / models / old training data

Use SOURCE_LOCK.json and checkout those commits, not moving main branches. Some source repositories remain private: owner authorization is required. For Ego-Exo use code/core, the supplied source overlay where needed, and the public weight bundle's sharded-Wan compatibility/attachment tool. DiffSynth Wan code is pinned; unrelated local Flux experiments/images are excluded.

Download the already published TRAINING_DATA_INDEX.json: it distinguishes legacy mix6 training (16,174 train / 3,228 heldout), later joint data, and Any2Ego original versus common filtered CSVs. Original training files/NPZ are protected in the pinned private vault. Large Ego-Exo original RGB/mask media are NOT fully backed up remotely and require original-source permission. No tokens are bundled.

Portable training recipes (dry-run by default)

python tools/run_training_recipe.py --recipe robot-sft --code-root /your/trick_prediction --python /new_env/bin/python --data-root /your/legacy_data --model-root /your/base27b --output /your/new_run --gpus 8
python tools/run_training_recipe.py --recipe egoexo-spatial --code-root /your/exo-ego/code/core --python /new_env/bin/python --data-root /your/ego_data --model-root /your/any2ego_bundle --diffsynth-root /your/DiffSynth-Studio --output /your/new_run --gpus 8
# Only add --execute after data/model/version preflight. Outputs must be new.

These are safe, explicit entrypoint recipes rather than evidence of convergence. The historical Spatial snapshot used teacher captions; switching to newly generated GRPO captions is a different experiment. Old 27B checkpoints were not trained on new fixed-EEF joint labels. SFT warm-start does not restore absent optimizer/RNG state.

Verification boundaries

Recorded: package versions/ABI, CPU dependency hash-lock resolution, source revisions, core imports, tiny GPU operations, standalone command dry-runs and syntax checks. Not completed: fresh full GPU installation, eight-GPU distributed training, all optional native source builds, model metrics, or the numerical identity of an unrecorded original training-launch environment. See VALIDATION_REPORT.json.