repro: locked Ego-Exo and robot environment snapshots, installation and training recipes
9947c3c verified | 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. | |
| ```bash | |
| 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) | |
| ```bash | |
| 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. | |