# 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

```json
{
  "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

```bash
# 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

```bash
# 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

```bash
# 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

```bash
# 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

```bash
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

```bash
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

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

### Deploy Ollama

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