gpuflux 0.1.0

Stop overpaying for GPU compute. Find the cheapest provider and deploy in one command.

⚡ GPUFlux

Stop overpaying for GPU compute. GPUFlux finds the cheapest provider and deploys your AI workload in one command.

GPU prices vary 3–5x across providers at any given moment. A training job costing $10/hr on AWS might cost $3/hr on bare-metal. GPUFlux checks live pricing across multiple providers and deploys your workload to the cheapest option — automatically.

The Problem

• You're running AI/ML workloads and burning money on GPU compute
• Prices vary wildly between providers, but you don't have time to price-shop
• Managing a mix of cloud VMs and dedicated servers is painful
• When a spot instance gets reclaimed, your job dies

The Solution

gpuflux deploy --gpu A100 --job ./train.yaml

GPUFlux handles the rest: finds the cheapest available GPU across providers, provisions the server, deploys your job, and monitors it.

Features

• 🔍 Live Price Comparison — Real-time pricing from multiple GPU providers
• 🚀 One-Command Deploy — Define your job once, deploy anywhere
• 💰 Cost Optimization — Automatically selects the cheapest option matching your requirements
• 📊 Spend Tracking — See exactly what you're spending and what you're saving
• 🔌 Provider Agnostic — Works with OVH, Hetzner, Lambda Labs, RunPod, and more

Quick Start

Installation

pip install git+https://github.com/Reflex1171/GPUFlux.git

Configure Providers

gpuflux init

This walks you through adding API credentials for your GPU providers.

Check Prices

# See live GPU pricing across all configured providers
gpuflux prices --gpu A100

# Example output:
# Provider      | GPU     | $/hr  | Available | Region
# --------------|---------|-------|-----------|--------
# Lambda Labs   | A100    | 1.10  | 3         | us-west
# RunPod        | A100    | 1.64  | 12        | us-east
# OVH           | A100    | 2.10  | 2         | eu-west
# AWS (spot)    | A100    | 3.97  | 8         | us-east-1

Deploy a Job

# train.yaml
name: finetune-llama
gpu: A100
gpu_count: 1
min_vram: 80  # GB
docker_image: pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime
command: python train.py --model llama-7b --data ./data
upload:
  - ./train.py
  - ./data/

gpuflux deploy --job train.yaml

Monitor

gpuflux status           # All running jobs
gpuflux logs <job-id>    # Stream logs from a running job
gpuflux spend            # Cost summary for the current month

Supported Providers

Provider	Status	GPU Types
Lambda Labs	✅ Live	A100, H100, A10G
RunPod	✅ Live	A100, H100, RTX 4090
OVH	🚧 Coming	A100, V100
Hetzner	🚧 Coming	—
AWS (Spot)	🚧 Coming	A100, H100, various
GCP (Spot)	📋 Planned	A100, H100, T4

Architecture

┌──────────────┐     ┌──────────────────────┐     ┌──────────────┐
│   CLI / API  │────▶│   GPUFlux Core       │────▶│  Providers   │
│              │     │                      │     │              │
│  gpuflux     │     │  • Price Aggregator  │     │  Lambda Labs │
│  deploy      │     │  • Job Scheduler     │     │  RunPod      │
│  prices      │     │  • Deploy Engine     │     │  OVH         │
│  status      │     │  • Monitor           │     │  AWS         │
└──────────────┘     └──────────────────────┘     └──────────────┘

Roadmap

• Core price comparison engine
• CLI interface
• Lambda Labs provider
• RunPod provider
• OVH bare-metal provider
• Job checkpointing & migration
• Auto-failover on instance termination
• Web dashboard
• Team accounts & spend controls
• Spot instance preemption prediction

Contributing

We're early stage and moving fast. If you're interested in contributing:

1. Fork the repo
2. Create a feature branch (git checkout -b feature/add-provider)
3. Commit your changes
4. Open a PR

See CONTRIBUTING.md for more details.

License

MIT License — see LICENSE for details.

Links

• Documentation (coming soon)
• Discord (coming soon)
• Twitter/X (coming soon)