Ubuntu Environments

Available Ubuntu configurations for Spheron GPU instances.

Available Versions

Version	Support Until	Best For
Ubuntu 20.04 LTS	2025	Legacy applications, older dependencies
Ubuntu 22.04 LTS	2027	Production workloads (most stable)
Ubuntu 24.04 LTS	2029	Latest features, experimental projects

Configuration Options

Base Images

Base / LTS Base - Minimal Ubuntu installation
+ NVIDIA 550/570 - NVIDIA drivers pre-installed
+ CUDA X.X - CUDA toolkit included
+ Docker - Docker pre-installed for containerized workflows
Shade OS - Optimized lightweight version for maximum GPU performance

Pre-configured ML Environments (Ubuntu 24.04)

Environment	Includes	Best For
ML Everything	PyTorch, TensorFlow, JAX	Multi-framework experimentation
ML PyTorch	PyTorch optimized	LLM training, computer vision
ML TensorFlow	TensorFlow optimized	Production ML, enterprise

Selection Guide

Use Case	Recommended Environment	Why
Beginners	Ubuntu 24.04 ML Everything	All frameworks pre-installed
LLM Training	Ubuntu 24.04 ML PyTorch	PyTorch with CUDA pre-installed
TensorFlow	Ubuntu 24.04 ML TensorFlow	TensorFlow with CUDA pre-installed
Production	Ubuntu 22.04 + CUDA 12.8 + Docker	Stable, containerized
Research	Ubuntu 24.04 + CUDA 13.0 Open	Latest features
Legacy Apps	Ubuntu 20.04 LTS	Older dependency support
Max Performance	Ubuntu 22.04 (Shade OS)	Optimized, minimal overhead

Docker vs Non-Docker

Without Docker:

Direct GPU access
Simpler setup
Single-purpose instances
Good for: Learning, simple projects

With Docker:

Containerized workflows
Dependency isolation
Multi-project instances
Good for: Production, complex setups

Deploying

Go to app.spheron.ai → Deploy
Select GPU
Choose Ubuntu environment from OS dropdown
Deploy (ready in 30-60 seconds)

Verify Installation

After deployment, connect and verify:

# Connect
ssh root@your-instance-ip
 
# Check OS version
cat /etc/os-release
 
# Check CUDA (if applicable)
nvcc --version
 
# Check GPU
nvidia-smi
 
# Check Docker (if applicable)
docker --version

Frequently Asked Questions

What does LTS mean?
Long Term Support - 5 years of security updates and bug fixes.

Can I change environments after deployment?
No. Deploy new instance with desired environment.

Do I need Docker?
Not for simple projects. Use Docker for complex dependencies or multi-project instances.

Which CUDA version should I use? See CUDA & NVIDIA Drivers for a full guide including framework compatibility tables and version recommendations.

Can I install multiple CUDA versions? Not recommended. Select correct version during deployment.

Ubuntu 22.04 or 24.04?
22.04 for production stability. 24.04 for latest features.

What is Shade OS?
Optimized Ubuntu variant with minimal overhead for maximum GPU performance.

Additional Resources

CUDA & NVIDIA Drivers - CUDA versions, driver details, and how to choose
Getting Started - Deploy your first instance
Quick Start - Fast deployment
SSH Connection - SSH setup guide
TensorFlow - TensorFlow environment
Jupyter - Jupyter Notebook setup