> ## Documentation Index
> Fetch the complete documentation index at: https://docs.gp.scale.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Custom Images

> Push Docker images to the SGP registry and use them in training jobs.

Each SGP workspace has a Docker-compatible registry. You push images to it and reference them in job configs; Train takes care of getting the right image onto the right cloud backend.

***

## Push an Image

<Steps>
  <Step title="Authenticate">
    Log in with your SGP credentials:

    ```bash theme={null}
    docker login docker-registry.your-sgp-deployment-url \
      --username $SGP_ACCOUNT_ID \
      --password $SGP_API_KEY
    ```
  </Step>

  <Step title="Build and tag">
    ```bash theme={null}
    docker build -t my-training-job:v1 .
    docker tag my-training-job:v1 docker-registry.your-sgp-deployment-url/my-training-job:v1
    ```
  </Step>

  <Step title="Push">
    ```bash theme={null}
    docker push docker-registry.your-sgp-deployment-url/my-training-job:v1
    ```
  </Step>

  <Step title="Reference in your job config">
    Use the registry URL directly in your job config. Train resolves it automatically at submission:

    ```json theme={null}
    "image_uri": "docker-registry.your-sgp-deployment-url/my-training-job:v1"
    ```

    Train ensures the exact image you pushed is what runs, even if the tag is updated later.
  </Step>
</Steps>

## Multi-Architecture Builds

Cloud training instances run `linux/amd64`. If you're building on Apple Silicon, cross-compile explicitly:

```bash theme={null}
docker buildx build \
  --platform linux/amd64 \
  -t docker-registry.your-sgp-deployment-url/my-training-job:v1 \
  --push .
```

<Warning>
  Pushing a `linux/arm64` image to an `amd64` compute instance will cause the job to fail at startup with an exec format error.
</Warning>

***

## Example Dockerfile

```dockerfile theme={null}
FROM pytorch/pytorch:2.2.0-cuda12.1-cudnn8-runtime

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY train.py .

CMD ["python3", "train.py"]
```

Your training script accesses data through `STORAGE_MOUNT_*_PATH` env vars, with no cloud-specific paths needed:

```python focus={3-4} theme={null}
import os
import torch

data_dir = os.environ["STORAGE_MOUNT_0_PATH"]
output_dir = os.environ["STORAGE_MOUNT_1_PATH"]

# ... training loop ...
torch.save(model.state_dict(), f"{output_dir}/model.pt")
```

***

## Next Steps

* **[Storage Mounts](/docs/capabilities/training/storage-mounts)**: Configure data and output directories
* **[Getting Started](/docs/capabilities/training/getting-started-with-train)**: Full job submission examples per backend