# Docker Images Reference

Pre-built Docker images commonly used with Clore.ai GPU rentals.

## Official NVIDIA Images

### CUDA Base Images

| Image                                         | Size  | Use Case          |
| --------------------------------------------- | ----- | ----------------- |
| `nvidia/cuda:12.8.0-base-ubuntu22.04`         | \~2GB | Minimal CUDA      |
| `nvidia/cuda:12.8.0-runtime-ubuntu22.04`      | \~3GB | CUDA runtime      |
| `nvidia/cuda:12.8.0-devel-ubuntu22.04`        | \~4GB | CUDA development  |
| `nvidia/cuda:11.8.0-base-ubuntu22.04`         | \~2GB | Older CUDA        |
| `nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04` | \~5GB | CUDA 11.8 + cuDNN |

```bash
# Recommended for most workloads
nvidia/cuda:12.8.0-base-ubuntu22.04
```

## PyTorch Images

| Image                                            | Size   | PyTorch | CUDA |
| ------------------------------------------------ | ------ | ------- | ---- |
| `pytorch/pytorch:2.7.1-cuda12.8-cudnn9-runtime`  | \~8GB  | 2.7.1   | 12.8 |
| `pytorch/pytorch:2.7.1-cuda12.8-cudnn9-devel`    | \~10GB | 2.7.1   | 12.8 |
| `pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime`  | \~5GB  | 2.0.1   | 11.7 |
| `pytorch/pytorch:1.13.1-cuda11.6-cudnn8-runtime` | \~5GB  | 1.13.1  | 11.6 |

```bash
# Recommended for ML training
pytorch/pytorch:2.7.1-cuda12.8-cudnn9-devel
```

## TensorFlow Images

| Image                                      | Size  | TensorFlow | CUDA |
| ------------------------------------------ | ----- | ---------- | ---- |
| `tensorflow/tensorflow:2.14.0-gpu`         | \~6GB | 2.14.0     | 11.8 |
| `tensorflow/tensorflow:2.13.0-gpu`         | \~6GB | 2.13.0     | 11.8 |
| `tensorflow/tensorflow:2.12.0-gpu-jupyter` | \~7GB | 2.12.0     | 11.8 |

```bash
# Recommended for TensorFlow
tensorflow/tensorflow:2.14.0-gpu
```

## Hugging Face Images

| Image                                     | Size   | Use Case                  |
| ----------------------------------------- | ------ | ------------------------- |
| `huggingface/transformers-pytorch-gpu`    | \~10GB | Transformers + PyTorch    |
| `huggingface/transformers-tensorflow-gpu` | \~10GB | Transformers + TensorFlow |

## Inference Servers

| Image                                                  | Use Case              |
| ------------------------------------------------------ | --------------------- |
| `vllm/vllm-openai:latest`                              | vLLM inference server |
| `ghcr.io/huggingface/text-generation-inference:latest` | HF TGI                |
| `nvcr.io/nvidia/tritonserver:23.10-py3`                | Triton Inference      |

## Development Environments

| Image                         | Description            |
| ----------------------------- | ---------------------- |
| `jupyter/pytorch-notebook`    | Jupyter + PyTorch      |
| `jupyter/tensorflow-notebook` | Jupyter + TensorFlow   |
| `cschranz/gpu-jupyter`        | Full GPU Jupyter stack |

## Using Custom Images

You can use any public Docker image. For private images, use the CCR (Custom Container Registry) feature.

### Build and Push

```bash
# Build your image
docker build -t myuser/my-ml-image:v1 .

# Push to Docker Hub
docker push myuser/my-ml-image:v1
```

### Use with Clore

```python
order_data = {
    "renting_server": server_id,
    "type": "on-demand",
    "currency": "CLORE-Blockchain",
    "image": "myuser/my-ml-image:v1",  # Your custom image
    "ports": {"22": "tcp"},
    "env": {"NVIDIA_VISIBLE_DEVICES": "all"},
    "ssh_password": "YourPassword123!"
}
```

## Example Dockerfile

```dockerfile
# Custom ML training image
FROM pytorch/pytorch:2.7.1-cuda12.8-cudnn9-devel

# Install dependencies
RUN pip install --upgrade pip && \
    pip install transformers datasets accelerate bitsandbytes wandb

# Install system tools
RUN apt-get update && apt-get install -y \
    git \
    vim \
    htop \
    && rm -rf /var/lib/apt/lists/*

# Set working directory
WORKDIR /workspace

# Default command
CMD ["/bin/bash"]
```

## Image Selection Guide

### For Training

| Workload             | Recommended Image                             |
| -------------------- | --------------------------------------------- |
| PyTorch training     | `pytorch/pytorch:2.7.1-cuda12.8-cudnn9-devel` |
| TensorFlow training  | `tensorflow/tensorflow:2.14.0-gpu`            |
| LLM fine-tuning      | `pytorch/pytorch:2.7.1-cuda12.8-cudnn9-devel` |
| Distributed training | `pytorch/pytorch:2.7.1-cuda12.8-cudnn9-devel` |

### For Inference

| Workload          | Recommended Image                               |
| ----------------- | ----------------------------------------------- |
| LLM serving       | `vllm/vllm-openai:latest`                       |
| General inference | `nvidia/cuda:12.8.0-runtime-ubuntu22.04`        |
| API server        | `pytorch/pytorch:2.7.1-cuda12.8-cudnn9-runtime` |

### For Development

| Workload          | Recommended Image                      |
| ----------------- | -------------------------------------- |
| Jupyter notebooks | `cschranz/gpu-jupyter`                 |
| General dev       | `nvidia/cuda:12.8.0-devel-ubuntu22.04` |

## Tips

1. **Use specific tags** - Avoid `:latest` for reproducibility
2. **Choose runtime vs devel** - Runtime is smaller, devel includes compilers
3. **Match CUDA versions** - Ensure your code is compatible with the CUDA version
4. **Consider size** - Smaller images deploy faster

## See Also

* [API Quick Reference](/reference/api-reference.md)
* [Quick Start Guide](/getting-started/quick-start.md)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://dev.clore.ai/reference/docker-images.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.