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

# 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

# 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

# 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

# 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

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

# 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

• Quick Start Guide