A CLI tool that runs GitHub projects on free cloud GPUs
Project description
RunOnGPU
RunOnGPU is a CLI tool that helps you run GitHub projects on a GPU with minimal setup.
It is useful if you want to test CUDA, PyTorch, or other GPU code but do not have a local NVIDIA GPU.
Requirements
- Windows
- Python 3.10+
- Git
- Google Chrome
- Google account for Colab
Install
pip install runongpu
python -m playwright install
Check setup:
```bash
runongpu doctor
Quick Start
Go to the project you want to run and initialize RunOnGPU:
runongpu init
Enter your GitHub repo URL when asked.
This creates a runongpu.txt file. Edit this file to tell RunOnGPU how to set up, build, test, and run your project.
Then run:
runongpu run
RunOnGPU will open Colab, copy the starter notebook, clone your repo, set the runtime to a T4 GPU, and run your commands.
runongpu.txt
runongpu.txt controls what happens inside Colab.
It has four sections:
[setup]
# install dependencies here
[build]
# compile or build the project here
[test]
# run tests here
[run]
# run the final program here
Add one command per line.
Example: CUDA
If your repo has this structure:
my-cuda-project/
├── main.cu
└── runongpu.txt
Use:
[setup]
[build]
nvcc main.cu -o vector_add
[test]
[run]
./vector_add
Example: CUDA in a Subfolder
If your repo has this structure:
runongpu-examples/
├── cuda/
│ └── vector-add/
│ └── main.cu
└── runongpu.txt
Use:
[setup]
[build]
cd cuda/vector-add && nvcc main.cu -o vector_add
[test]
[run]
cd cuda/vector-add && ./vector_add
Example: Python
[setup]
pip install -r requirements.txt
[build]
[test]
pytest
[run]
python main.py
Example: CMake
[setup]
[build]
cmake -S . -B build
cmake --build build
[test]
ctest --test-dir build --output-on-failure
[run]
./build/my_program
Notes
On the first run, you may need to sign into Google Colab. RunOnGPU saves the copied notebook URL and reuses it on future runs.
Do not interact with the Colab window while RunOnGPU is setting it up.
Run Tests
python -m pytest
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