add docker feature to the project

.dockerignore

@@ -0,0 +1,13 @@
+.git
+.venv
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+*.so
+*.egg-info/
+dist/
+build/
+assets/
+example/
+lingbot-map_paper.pdf

Dockerfile

@@ -0,0 +1,42 @@
+FROM spxiong/pytorch:2.11.0-py3.10.19-cuda13.0.2-ubuntu22.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+WORKDIR /workspace
+
+ARG UBUNTU_MIRROR=https://mirrors.aliyun.com/ubuntu
+
+RUN set -eux; \
+    find /etc/apt -type f -name "*.list" -print0 | xargs -0 -r sed -i "s|http://archive.ubuntu.com/ubuntu|${UBUNTU_MIRROR}|g"; \
+    find /etc/apt -type f -name "*.list" -print0 | xargs -0 -r sed -i "s|https://archive.ubuntu.com/ubuntu|${UBUNTU_MIRROR}|g"; \
+    find /etc/apt -type f -name "*.list" -print0 | xargs -0 -r sed -i "s|http://security.ubuntu.com/ubuntu|${UBUNTU_MIRROR}|g"; \
+    find /etc/apt -type f -name "*.list" -print0 | xargs -0 -r sed -i "s|https://security.ubuntu.com/ubuntu|${UBUNTU_MIRROR}|g"; \
+    find /etc/apt -type f -name "*.list" -print0 | xargs -0 -r sed -i "/jammy-backports/d"; \
+    for i in 1 2 3; do \
+      apt-get -o Acquire::Retries=5 -o Acquire::http::Timeout=30 update && \
+      apt-get -o Acquire::Retries=5 -o Acquire::http::Timeout=30 install -y --no-install-recommends --fix-missing \
+        ca-certificates \
+        git \
+        ffmpeg \
+        libgl1 \
+        libglib2.0-0 \
+        libgomp1 \
+        libsm6 \
+        libxext6 \
+        libxrender1 \
+      && break; \
+      sleep 5; \
+    done; \
+    rm -rf /var/lib/apt/lists/*
+
+COPY pyproject.toml README.md LICENSE.txt ./
+COPY lingbot_map ./lingbot_map
+COPY demo.py gct_profile.py ./
+
+RUN python -m pip install --upgrade pip
+RUN python -m pip install --no-cache-dir flashinfer-python flashinfer-cubin
+RUN python -m pip install --no-cache-dir flashinfer-jit-cache --index-url https://flashinfer.ai/whl/cu130 --no-deps || true
+RUN python -m pip install -e ".[vis]"
+
+EXPOSE 8080
+
+CMD ["bash"]

README.md

@@ -75,6 +75,69 @@ pip install flashinfer-python -i https://flashinfer.ai/whl/cu128/torch2.9/
 pip install -e ".[vis]"
 ```
 
+## Docker / Docker Compose
+
+If you want a quick, reproducible runtime environment (CUDA + dependencies, with optional FlashInfer), you can use the provided [Dockerfile](Dockerfile) and [docker-compose.yml](docker-compose.yml).
+
+### Prerequisites
+
+- Install Docker Desktop (or Docker Engine on Linux).
+- For GPU support:
+  - Linux: install the NVIDIA driver + [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
+  - Windows: use the WSL2 backend and make sure `nvidia-smi` works inside WSL
+
+### Build and enter the container
+
+```bash
+docker compose build
+docker compose run --rm --service-ports lingbot-map
+```
+
+Inside the container (default working directory is `/workspace`, and the repo is mounted via a volume), run the demo:
+
+```bash
+python demo.py --model_path /path/to/lingbot-map-long.pt \
+    --image_folder example/church --mask_sky
+```
+
+Open `http://localhost:8080` in your browser (compose maps `8080:8080`).
+
+### Exit and cleanup
+
+```bash
+exit
+docker compose down
+```
+
+(Optional) This compose persists HuggingFace and Torch caches in named volumes: `hf_cache`, `torch_cache`. To remove them:
+
+```bash
+docker volume rm lingbot-map_hf_cache lingbot-map_torch_cache
+```
+
+### Adjust for your CUDA version
+
+The current [Dockerfile](Dockerfile) uses a base image and FlashInfer wheel for **CUDA 13.0** (`cuda13.0.2` / `cu130`). If your machine/driver environment is better matched with CUDA 12.x (more common), update both of the following accordingly:
+
+1) Update the CUDA version in the Docker base image (`FROM ...cudaXX...` on line 1 of `Dockerfile`)
+
+2) Update the FlashInfer wheel index URL (the `--index-url https://flashinfer.ai/whl/cuXXX` line in `Dockerfile`)
+
+Example: switch to **CUDA 12.8** (illustrative; choose a base image and FlashInfer wheel that match your actual PyTorch/CUDA setup)
+
+```diff
+-FROM spxiong/pytorch:2.11.0-py3.10.19-cuda13.0.2-ubuntu22.04
++FROM spxiong/pytorch:2.11.0-py3.10.19-cuda12.8.0-ubuntu22.04
+-RUN python -m pip install --no-cache-dir flashinfer-jit-cache --index-url https://flashinfer.ai/whl/cu130 --no-deps || true
++RUN python -m pip install --no-cache-dir flashinfer-jit-cache --index-url https://flashinfer.ai/whl/cu128 --no-deps || true
+```
+
+
+Notes:
+
+- In `docker-compose.yml`, `gpus: all` exposes all visible GPUs to the container. If you want to use only one GPU, limit the exposed device(s) (syntax varies across Docker/Compose versions).
+- You may need to change the Ubuntu apt mirror depending on your network environment. `Dockerfile` supports the `UBUNTU_MIRROR` build arg (defaults to Aliyun mirrors).
+
 # 📦 Model Download
 
 | Model Name | Huggingface Repository | ModelScope Repository | Description |

docker-compose.yml

@@ -0,0 +1,24 @@
+services:
+  lingbot-map:
+    build:
+      context: .
+    image: lingbot-map:dev
+    working_dir: /workspace
+    volumes:
+      - .:/workspace
+      - hf_cache:/root/.cache/huggingface
+      - torch_cache:/root/.cache/torch
+    environment:
+      - HF_HOME=/root/.cache/huggingface
+      - TORCH_HOME=/root/.cache/torch
+    ports:
+      - "8080:8080"
+    gpus: all
+    shm_size: "32gb"
+    stdin_open: true
+    tty: true
+    command: bash
+
+volumes:
+  hf_cache:
+  torch_cache: