Available Docker Images

When renting a GPU server on Clore.ai, you can choose from pre-configured Docker images or use your own custom images.

Clore Official Images

Pre-built images maintained by Clore.ai, optimized for the decentralized GPU marketplace.

General Purpose

Docker Image	Description	Ports	Updated
cloreai/jupyter:ubuntu24.04-v2	Jupyter Lab + SSH on Ubuntu 24.04	22, 8888	Jan 2025 ✅
cloreai/ml-tools:0.1	Jupyter Lab + VS Code Web server	22, 8888	Jul 2023 ⚠️
cloreai/ubuntu20.04-jupyter:latest	Ubuntu 20.04 + SSH + Jupyter	22, 8888	Nov 2022 ⚠️
cloreai/ubuntu-20.04-remote-desktop:1.2	Ubuntu with remote desktop GUI	22, 3389	May 2023 ⚠️
cloreai/torch:2.0.1	PyTorch 2.0.1 + CUDA	22	Jul 2023 ⚠️

AI/ML Inference

Docker Image	Description	Ports	Updated
cloreai/deepseek-r1-8b:latest	DeepSeek R1 8B ready to run	8000	Jan 2025 ✅
cloreai/stable-diffusion-webui:latest	AUTOMATIC1111 SD WebUI	7860	Sep 2023 ⚠️
cloreai/oobabooga:1.5	Text Generation WebUI	7860	Aug 2023 ⚠️

Infrastructure & Mining

Docker Image	Description	Updated
cloreai/monitoring:0.7	Server monitoring agent	Sep 2024 ✅
cloreai/hiveos:0.4	HiveOS integration	Feb 2025 ✅
cloreai/openvpn-proxy:0.2	VPN/proxy forwarding	Feb 2025 ✅
cloreai/proxy:0.2	Port forwarding system	Jan 2024
cloreai/automining:0.1	Auto mining setup	Jun 2023 ⚠️
cloreai/kuzco:latest	Kuzco distributed inference	Jun 2025 ✅
cloreai/partner:nastya-01	Partner integration tools	Apr 2025 ✅

⚠️ Images marked with ⚠️ haven't been updated in over a year. For ML workloads, consider using the community images below which offer newer CUDA and framework versions.

Recommended Community Images

Battle-tested images from the broader AI/ML community with active maintenance and recent versions.

Deep Learning Frameworks

Image	Tag	Description	Use Case	Ports
pytorch/pytorch	2.10.0-cuda13.0-cudnn9-runtime	Latest PyTorch with CUDA 13.0	Deep learning training/inference	8888 (Jupyter)
nvidia/cuda	13.1.1-runtime-ubuntu22.04	NVIDIA CUDA runtime	Custom CUDA applications	-
nvidia/cuda	13.1.1-devel-ubuntu22.04	CUDA development tools	Building CUDA projects	-
tensorflow/tensorflow	2.15.0-gpu	TensorFlow GPU support	TensorFlow workflows	8888 (TensorBoard)

LLM Inference Engines

Image	Tag	Description	Use Case	Ports
vllm/vllm-openai	latest	High-performance LLM serving	Production LLM APIs	8000
ghcr.io/huggingface/text-generation-inference	3.3.5	Hugging Face TGI	Enterprise LLM serving	80
ollama/ollama	latest	Local LLM runner	Local/edge LLM deployment	11434

Image Generation

Image	Tag	Description	Use Case	Ports
goolashe/automatic1111-sd-webui	latest	Stable Diffusion WebUI	AI art generation	7860
sinfallas/comfyui	latest	ComfyUI node-based SD	Advanced image workflows	8188

Development Environments

Image	Tag	Description	Use Case	Ports
jupyter/tensorflow-notebook	latest	Jupyter + TensorFlow	ML experimentation	8888
jupyter/pytorch-notebook	latest	Jupyter + PyTorch	Deep learning research	8888
cschranz/gpu-jupyter	v1.10_cuda-12.9_ubuntu-24.04	GPU-enabled Jupyter	GPU computing	8888

Selecting an Image

Via Web Interface

1. Go to Marketplace → Find a server → Rent

2. In the order form, select Docker Image from dropdown

3. Choose from pre-configured options or enter custom image

4. Configure exposed ports (comma-separated: 8888,7860,8000)

5. Add environment variables if needed

6. Submit order

Via API

{
  "image": "pytorch/pytorch:2.10.0-cuda13.0-cudnn9-runtime",
  "ports": [8888, 8000],
  "env": {
    "JUPYTER_ENABLE_LAB": "yes"
  }
}

Custom Docker Images

Supported Registries

• Docker Hub: username/image:tag

• GitHub Container Registry: ghcr.io/user/image:tag

• NVIDIA NGC: nvcr.io/nvidia/image:tag

• Google Container Registry: gcr.io/project/image:tag

Examples

# PyTorch latest with CUDA 12.1
pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime

# NVIDIA CUDA base
nvidia/cuda:12.2.0-base-ubuntu22.04

# Hugging Face Transformers
huggingface/transformers-pytorch-gpu:4.35.0

# Custom image
your-username/my-ai-model:v1.0

Requirements for Custom Images

• ✅ Must be publicly accessible

• ✅ Should include NVIDIA GPU support for GPU instances

• ✅ Base on CUDA-enabled images for GPU acceleration

• ✅ Include necessary drivers and libraries

• ⚠️ Large images (>10GB) may take longer to download

Port Configuration

Expose ports for your applications to make them accessible from outside:

Port	Common Use	Framework
22	SSH access	System
8888	Jupyter Notebook/Lab	Jupyter
7860	Gradio interfaces	SD WebUI, Gradio apps
8000	API servers	vLLM, FastAPI
3000	Web applications	React, Node.js
8080	HTTP services	General web services
11434	Ollama API	Ollama
8188	ComfyUI interface	ComfyUI

Setting Ports in Order Form

8888,7860,8000

Environment Variables

Pass configuration to your containers:

Common Examples

# Hugging Face token
HF_TOKEN=hf_your_token_here

# Model configuration
MODEL_NAME=meta-llama/Llama-2-7b-chat-hf
CUDA_VISIBLE_DEVICES=0

# Jupyter configuration
JUPYTER_ENABLE_LAB=yes
JUPYTER_TOKEN=your_secure_token

# vLLM configuration
VLLM_WORKER_MULTIPROC_METHOD=spawn

Security Notes

• ❌ Don't put secrets in environment variables

• ✅ Use temporary tokens or API keys

• ✅ Mount secrets as volumes when possible

Persistent Storage

Storage Locations

• /workspace - Usually persistent during rental period

• /root - May be reset on container restart

• /tmp - Temporary storage, not persistent

Best Practices

• Store important data in /workspace

• Use external storage for backups (S3, GCS, etc.)

• Download models to persistent directories

• Use volume mounts for large datasets

Best Practices

Image Selection

1. Use recent tags - Avoid latest in production, prefer versioned tags

2. Choose appropriate base - Match CUDA version with your framework

3. Consider image size - Smaller images download faster

4. Check maintenance - Prefer actively maintained images

Security

1. Expose minimal ports - Only expose ports you need

2. Use authentication - Set tokens for Jupyter/web interfaces

3. Update regularly - Use recent image versions

4. Monitor access - Check who connects to your services

Performance

1. GPU compatibility - Verify CUDA version matches your needs

2. Pre-download models - Include models in custom images for faster startup

3. Optimize containers - Use multi-stage builds, minimize layers

4. Cache management - Leverage Docker layer caching

Troubleshooting

Image Won't Start

# Check if image exists and is public
docker pull your-image:tag

# Verify CUDA compatibility
nvidia-smi
nvcc --version

Common Issues:

• Image doesn't exist or is private

• Incompatible CUDA version

• Insufficient disk space

• Wrong architecture (arm64 vs x86_64)

GPU Not Accessible

# Check GPU availability
nvidia-smi

# Test CUDA in container
python -c "import torch; print(torch.cuda.is_available())"

Solutions:

• Use GPU-compatible base images

• Ensure NVIDIA Container Toolkit is available

• Check CUDA driver compatibility

Can't Access Exposed Ports

1. Wait for container to fully start (check logs)

2. Verify service is running inside container: netstat -tlnp

3. Check if service binds to 0.0.0.0, not 127.0.0.1

4. Confirm port is exposed in order form

Performance Issues

• Use local SSD storage for model weights

• Optimize batch sizes for available GPU memory

• Monitor GPU utilization: nvidia-smi -l 1

• Check CPU/memory usage: htop

Quick Start Examples

Deploy Jupyter with PyTorch

Image: pytorch/pytorch:2.10.0-cuda13.0-cudnn9-runtime
Ports: 8888
Command: jupyter lab --ip=0.0.0.0 --allow-root --no-browser

Deploy vLLM API Server

Image: vllm/vllm-openai:latest
Ports: 8000
Env: MODEL_NAME=microsoft/DialoGPT-medium
Command: python -m vllm.entrypoints.openai.api_server --model $MODEL_NAME --host 0.0.0.0

Deploy Stable Diffusion WebUI

Image: goolashe/automatic1111-sd-webui:latest
Ports: 7860
Command: --listen --api --xformers

Deploy Ollama

Image: ollama/ollama:latest
Ports: 11434
Command: ollama serve
# Then: ollama run llama2 (inside container)