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docs/source/en/user_guide/getting_started/container_deployment.md

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+# Container Deployment
+
+This document describes how to deploy MotrixLab using Docker containers to simplify environment configuration and enable rapid deployment.
+
+## Prerequisites
+
+-   **Docker** and **Docker Compose**: [Installation Docs](https://docs.docker.com/engine/install/)
+-   **NVIDIA Container Toolkit** (for GPU support): [Installation Docs](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation)
+-   **NVIDIA GPU**: Graphics card supporting CUDA 12.8
+
+## Quick Start
+
+### 1. Clone the Repository
+
+```bash
+git clone https://github.com/Motphys/MotrixLab.git
+
+cd MotrixLab/docker
+```
+
+### 2. Start with Docker Compose
+
+We provide a complete `docker-compose.yml` configuration file that supports one-click startup of training and TensorBoard visualization services.
+
+```bash
+# Start training and TensorBoard services
+docker compose up -d
+```
+
+This will start the following services:
+
+-   **motrixlab-training**: Training container that executes reinforcement learning training tasks
+-   **motrixlab-tensorboard**: TensorBoard visualization service accessible via browser at http://localhost:6006
+
+### 3. Configure Training Parameters
+
+You can customize training configurations through environment variables:
+
+```bash
+# Set training backend (jax or torch)
+export MOTRIX_TRAIN_BACKEND=jax
+
+# Set number of parallel environments
+export MOTRIX_NUM_ENVS=2048
+
+# Set training environment name
+export MOTRIX_ENV=cartpole
+
+# Start services
+docker compose up -d
+```
+
+| Environment Variable   | Default    | Description                        |
+| :--------------------- | :--------- | :--------------------------------- |
+| `MOTRIX_TRAIN_BACKEND` | `jax`      | Training backend: `jax` or `torch` |
+| `MOTRIX_NUM_ENVS`      | `2048`     | Number of parallel environments    |
+| `MOTRIX_ENV`           | `cartpole` | Training environment name          |
+
+### 4. Monitor Training Progress
+
+Training logs are automatically saved to the Docker Volume `motrixlab-data`. You can view them through:
+
+```bash
+# View training container logs
+docker logs -f motrixlab-training
+
+# Access TensorBoard
+# Open in browser: http://localhost:6006
+```
+
+### 5. Stop Services
+
+```bash
+# Stop all services
+docker compose down
+
+# Stop services and remove data volumes
+docker compose down -v
+```
+
+## Advanced Usage
+
+### Building Docker Images
+
+If you need to customize the image, you can build from source:
+
+```bash
+# Execute from project root directory
+cd docker
+docker build -t motphys/motrixlab:latest .
+```
+
+### Running Single Container
+
+If you only want to run the training container without using Docker Compose:
+
+```bash
+docker run --gpus all \
+    -v $(pwd)/runs:/root/motrixlab/runs \
+    motphys/motrixlab:latest \
+    scripts/train.py --train-backend jax --num-envs 2048 --env cartpole
+```
+
+### Persisting Training Results
+
+The default configuration uses the Docker Volume `motrixlab-data` to save training results. You can mount it to a host directory:
+
+```bash
+# Modify volumes configuration in docker-compose.yml
+volumes:
+  - ./runs:/root/motrixlab/runs
+```
+
+## Image Details
+
+Our Docker image is based on {bdg-primary-line}`NVIDIA CUDA 12.8.1` runtime environment and comes pre-installed with the following components:
+
+-   **UV Package Manager**: Fast, reliable dependency management
+-   **MotrixLab**: Complete reinforcement learning training framework
+-   **SKRL**: Reinforcement learning library supporting both JAX and PyTorch backends
+-   **TensorBoard**: Training process visualization tool
+-   **MotrixSim**: High-performance physics simulation engine
+
+Image layer build process:
+
+1.  **Base Environment**: NVIDIA CUDA 12.8.1 Runtime + Ubuntu 24.04
+2.  **System Dependencies**: Install UV package manager and necessary system tools
+3.  **Python Dependencies**: Use UV cache mechanism for fast Python package installation
+4.  **Project Code**: Copy MotrixLab source code and complete dependency installation
+
+## Troubleshooting
+
+### GPU Not Available
+
+If the container cannot access the GPU:
+
+```bash
+# Check NVIDIA Docker Runtime
+docker run --rm --gpus all nvidia/cuda:12.8.1-base-ubuntu24.04 nvidia-smi
+
+# Verify NVIDIA Container Toolkit is correctly installed
+which nvidia-container-cli
+```
+
+### Insufficient Storage Space
+
+Clean Docker cache and unused images:
+
+```bash
+# Clean build cache
+docker builder prune
+
+# Delete unused images
+docker image prune -a
+
+# Clean all unused resources
+docker system prune -a
+```
+
+## Performance Optimization
+
+### Accelerate Builds with UV Cache
+
+The Dockerfile uses UV's cache mount feature, which can significantly speed up rebuilds:
+
+```bash
+# Rebuild using UV cache
+docker build --cache-from motphys/motrixlab:latest -t motphys/motrixlab:latest .
+```
+
+### GPU Resource Allocation
+
+You can specify the number of GPUs to use in `docker-compose.yml`:
+
+```yaml
+deploy:
+    resources:
+        reservations:
+            devices:
+                - driver: nvidia
+                  device_ids: ["0", "1"] # Use GPU 0 and 1
+                  capabilities: [gpu]
+```
+
+## Next Steps
+
+-   Check out the [Quick Start Tutorial](hello_motrixlab.md) to learn basic MotrixLab usage
+-   Read [Training Examples](../demo/cartpole.md) for more training tasks
+-   Explore [Basic Framework](../tutorial/basic_frame.md) for in-depth understanding of the framework architecture