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+# PyTorch
+
+[English](./README.md) | [中文](./README.zh.md)
+
+This service deploys PyTorch with CUDA support, Jupyter Lab, and TensorBoard for deep learning development.
+
+## Services
+
+- `pytorch`: PyTorch container with GPU support, Jupyter Lab, and TensorBoard.
+
+## Prerequisites
+
+**NVIDIA GPU Required**: This service requires an NVIDIA GPU with CUDA support and the NVIDIA Container Toolkit installed.
+
+### Install NVIDIA Container Toolkit
+
+**Linux:**
+
+```bash
+distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
+curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
+curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
+sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
+sudo systemctl restart docker
+```
+
+**Windows (Docker Desktop):**
+
+Ensure you have WSL2 with NVIDIA drivers installed and Docker Desktop configured to use WSL2 backend.
+
+## Environment Variables
+
+| Variable Name              | Description                | Default Value                   |
+| -------------------------- | -------------------------- | ------------------------------- |
+| PYTORCH_VERSION            | PyTorch image version      | `2.6.0-cuda12.6-cudnn9-runtime` |
+| JUPYTER_ENABLE_LAB         | Enable Jupyter Lab         | `yes`                           |
+| JUPYTER_TOKEN              | Jupyter access token       | `pytorch`                       |
+| NVIDIA_VISIBLE_DEVICES     | GPUs to use                | `all`                           |
+| NVIDIA_DRIVER_CAPABILITIES | Driver capabilities        | `compute,utility`               |
+| GPU_COUNT                  | Number of GPUs to allocate | `1`                             |
+| JUPYTER_PORT_OVERRIDE      | Jupyter Lab port           | `8888`                          |
+| TENSORBOARD_PORT_OVERRIDE  | TensorBoard port           | `6006`                          |
+
+Please modify the `.env` file as needed for your use case.
+
+## Volumes
+
+- `pytorch_notebooks`: Jupyter notebooks and scripts.
+- `pytorch_data`: Training data and datasets.
+
+## Usage
+
+### Start the Service
+
+```bash
+docker-compose up -d
+```
+
+### Access Jupyter Lab
+
+Open your browser and navigate to:
+
+```text
+http://localhost:8888
+```
+
+Login with the token specified in `JUPYTER_TOKEN` (default: `pytorch`).
+
+### Verify GPU Access
+
+In a Jupyter notebook:
+
+```python
+import torch
+
+print(f"PyTorch version: {torch.__version__}")
+print(f"CUDA available: {torch.cuda.is_available()}")
+print(f"CUDA version: {torch.version.cuda}")
+print(f"Number of GPUs: {torch.cuda.device_count()}")
+
+if torch.cuda.is_available():
+    print(f"GPU name: {torch.cuda.get_device_name(0)}")
+```
+
+### Example Training Script
+
+```python
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Define a simple model
+model = nn.Sequential(
+    nn.Linear(784, 128),
+    nn.ReLU(),
+    nn.Linear(128, 10)
+).to(device)
+
+# Create dummy data
+x = torch.randn(64, 784).to(device)
+y = torch.randint(0, 10, (64,)).to(device)
+
+# Training
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters())
+
+output = model(x)
+loss = criterion(output, y)
+loss.backward()
+optimizer.step()
+
+print(f"Loss: {loss.item()}")
+```
+
+### Access TensorBoard
+
+TensorBoard port is exposed but needs to be started manually:
+
+```python
+from torch.utils.tensorboard import SummaryWriter
+writer = SummaryWriter('/workspace/runs')
+```
+
+Then start TensorBoard:
+
+```bash
+docker exec pytorch tensorboard --logdir=/workspace/runs --host=0.0.0.0
+```
+
+Access at: `http://localhost:6006`
+
+## Features
+
+- **GPU Acceleration**: CUDA support for fast training
+- **Jupyter Lab**: Interactive development environment
+- **TensorBoard**: Visualization for training metrics
+- **Pre-installed**: PyTorch, CUDA, cuDNN ready to use
+- **Persistent Storage**: Notebooks and data stored in volumes
+
+## Notes
+
+- GPU is required for optimal performance
+- Recommended: 8GB+ VRAM for most deep learning tasks
+- The container installs Jupyter and TensorBoard on first start
+- Use `pytorch/pytorch:*-devel` for building custom extensions
+- For multi-GPU training, adjust `GPU_COUNT` and use `torch.nn.DataParallel`
+
+## License
+
+PyTorch is licensed under the BSD-style license.