# Docker Tutorial

`spMetaTME` is distributed with a Docker environment to ensure that all dependencies (Python, R for domain clustering, PyTorch Geometric) are exactly as needed. This is the simplest way to get up and running if you have Docker and an NVIDIA GPU.

## Prerequisites

*   **Docker Installed**: Follow instructions on [Docker's website](https://docs.docker.com/get-docker/).
*   **NVIDIA Container Toolkit (Optional but recommended)**: For using GPUs inside Docker. See [instructions here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html).

---

## Building the Image

If you want to build the `spmetatme` image from source:

1.  **Clone the repository**:
    ```bash
    git clone https://github.com/SurajRepo/spmetatme.git
    cd spmetatme
    ```

2.  **Build the Docker image**:
    ```bash
    docker build -t spmetatme:latest .
    ```

---

## Basic Usage

The Docker image uses the `spmetatme` CLI as its entry point. You can run any subcommand directly.

### Get Help
```bash
docker run --rm spmetatme --help
```

### Inference Help
```bash
docker run --rm spmetatme infer --help
```

---

## Working with Local Data

Since the container has its own isolated file system, you must **mount your local directory** to the container's `/app` directory to analyze your files. Select --model-species `human` or `mouse` based on your data. and --metabolic-model based on the model you want to use.

### Windows (PowerShell/CMD)
Use the `%CD%` variable to mount your current folder:
```bash
docker run --rm -it --gpus all -v "%CD%:/app" spmetatme infer --input data/my_sample.h5ad --model-species human --metabolic-model breast_cancer
```

### Linux / Mac
Use the `$(pwd)` variable to mount your current folder:
```bash
docker run --rm -it --gpus all -v $(pwd):/app spmetatme infer --input data/my_sample.h5ad --model-species human --metabolic-model breast_cancer
```

### Understanding the Flags:
*   `--gpus all`: Enables GPU acceleration inside the container (highly recommended).
*   `-v "%CD%:/app"`: Binds your local folder to the container's work directory. Data and results are shared.
*   `-it`: Enables interactive mode so you can see live logs.
*   `--rm`: Automatically removes the container after it stops.

---

## Windows Helper: `run_docker.cmd`

For Windows users, we provide a pre-configured script (`run_docker.cmd`) in the repository to simplify these commands.

**Example Inference**:
```batch
run_docker.cmd infer --input data/CID4535.h5ad --model-species human --metabolic-model breast_cancer
```

**Example Pretraining**:
```batch
run_docker.cmd pretrain --training-list data/*.h5ad --metabolic-model breast_cancer --repo-id MyRepo/v1
```

---

## Advanced: Accessing the Container Shell

If you want to explore the container manually:
```bash
docker run --rm -it --gpus all -v "%CD%:/app" --entrypoint bash spmetatme
```
Once inside, you can run `spmetatme` or standard Python commands manually.