Sevleete/lehome-submission

LeHome Challenge Submission

This image bundles a self-contained evaluation runner: an internal model-server, the simulation assets, and a websocket bridge that drives scripts/eval. After loading the image, a single docker run invocation evaluates the four garment categories end-to-end and writes per-category logs to a host-mounted directory.

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1. Prerequisites (organizer host)

• Linux x86_64 host with a CUDA-capable NVIDIA GPU (tested on RTX 4090, ≥24GB).
• NVIDIA driver ≥ 535 (verified with nvidia-smi).
• Docker (≥ 24.x).
• nvidia-container-toolkit installed and Docker configured to use it:

  sudo apt install -y nvidia-container-toolkit
  sudo nvidia-ctk runtime configure --runtime=docker
  sudo systemctl restart docker
  # Sanity check:
  docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi
  

• About 40 GB free disk for the loaded image.

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2. Download the image tarball

wget https://huggingface.co/datasets/Sevleete/lehome-submission/resolve/main/lehome-submission-v1.tar.gz

(Roughly 25–35 GB. The HuggingFace download supports resume; if interrupted, just rerun the same wget command.)

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3. Load the image into Docker

docker load -i lehome-submission-v1.tar.gz
docker images | grep lehome-submission   # expect: lehome-submission   v1   ...

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4. Run the evaluation

A single command runs the full evaluation suite (4 categories × 10 episodes):

mkdir -p eval_results
docker run --rm --gpus all \
    -v $PWD/eval_results:/workspace/eval_results \
    lehome-submission:v1

Inside the container the entrypoint script:

1. Starts the bundled model-server on 127.0.0.1:9000 (background process).

2. Waits up to 600 s for the server to accept connections.

3. For each garment category in [top_long, top_short, pant_long, pant_short], runs

   xvfb-run -a python -m scripts.eval \
       --policy_type openpi_ws \
       --policy_path ws://127.0.0.1:9000 \
       --garment_type <category> \
       --num_episodes 10 \
       --enable_cameras --device cpu --headless
   

4. Saves per-category logs to /workspace/eval_results/eval_<category>.log, plus server.log.

Total wall-clock time on a single RTX 4090 host is roughly 2–4 hours.

4a. Override the official evaluation Assets

The official challenge Assets/ directory shipped inside this image only contains the public garment set (Seen_0..9 plus Unseen_0,1). Organizers holding the held-out Unseen_2..9 (or any other private assets) can override the bundled Assets/ by bind-mounting a host directory:

docker run --rm --gpus all \
    -v /path/to/official/Assets:/opt/lehome-challenge/Assets \
    -v $PWD/eval_results:/workspace/eval_results \
    lehome-submission:v1

The eval driver reads the per-category garment list from Assets/objects/Challenge_Garment/Release/<Category>/<Category>.txt, so whichever .txt is in the mounted Assets/ controls which garments get rolled out. No code changes inside the image are required.

4b. Override the per-garment episode count

NUM_EPISODES defaults to 10. To run a different count (e.g. 5 for a quick smoke test, or 20 for full statistics):

docker run --rm --gpus all \
    -e NUM_EPISODES=5 \
    -v $PWD/eval_results:/workspace/eval_results \
    lehome-submission:v1

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5. Collect results

After the run finishes:

ls eval_results/
# eval_top_long.log
# eval_top_short.log
# eval_pant_long.log
# eval_pant_short.log
# server.log

Each eval_*.log ends with a per-garment success-rate summary. The companion all_data.txt in this submission contains the corresponding numbers we obtained on our local machine.

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6. What's inside the image

Path	Contents
/opt/lehome-challenge/	Official lehome-challenge:latest base image (uv venv at .venv/, isaac sim, scripts)
/opt/lehome-challenge/Assets/	Simulation assets (scenes, garments, robots)
/opt/lehome-challenge/scripts/eval_policy/openpi_ws_policy.py	Websocket bridge policy that forwards observations to the model-server
/workspace/model_server/	Internal model-server with bundled weights (loaded automatically at container start)
/workspace/start_eval.sh	Entrypoint launched by CMD — see step 4 above
xvfb	Installed via apt to satisfy pynput's import-time X11 requirement under headless mode

The two Python environments (lehome simulator vs. model-server) are isolated: the simulator uses the uv venv under /opt/lehome-challenge/.venv, while the model-server uses its own pixi env under /workspace/model_server/.pixi/envs/default. Communication between them is exclusively via the local websocket on port 9000.

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7. Troubleshooting

Symptom	Likely cause	Fix
could not select device driver "" with capabilities: [[gpu]]	--gpus flag works only when nvidia-container-toolkit is installed and Docker daemon restarted	See "Prerequisites"
Container exits with server didn't start in 600s	GPU OOM, missing driver, or weights not found at expected path	Inspect eval_results/server.log for the underlying traceback
pynput: this platform is not supported ... display ":0"	The xvfb-run wrapper was bypassed. The bundled start_eval.sh already wraps every eval call in xvfb-run -a	Make sure you do not override --entrypoint or pass a custom command
Slow eval / sim hangs	--device cpu is required by the official challenge protocol; sim-side compute is CPU-only by design	Expected; the GPU is used by the model-server only

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8. Notes on training pipeline

• All training and dataset preprocessing was performed offline on a separate workstation; this image only contains the inference path needed for evaluation.
• The model-server runs the trained checkpoint at garment_v1/final/ (path inside the image) — no additional weight downloads are required at runtime.

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9. Source code

(Optional, for organizers who want to debug failed runs.)

If a source-code link is provided alongside this submission in the Google form, note that the source code is for reference only — the evaluation procedure above does not pull anything from it. Everything needed to reproduce the reported numbers is already inside the docker image.