modal-cuda 0.1.1

CLI that compiles and runs CUDA C programs on Modal GPUs

mcc

mcc is a tiny CLI that compiles and runs a local CUDA C (.cu) program on a cloud GPU using Modal. Install the package published as modal-cuda, keep using the mcc command, and the tool will ship your source to a disposable container, invoke nvcc inside the GPU runtime you pick, stream stdout/stderr back to your terminal, and clean up the artifacts once the run finishes.

Highlights

• Runs CUDA samples on-demand without managing local GPU drivers.
• Lets you choose GPU tier (T4, A100, H100, etc.) and container image per run.
• Accepts extra nvcc arguments for fine-grained builds.
• Wraps the entire workflow (upload -> compile -> execute -> teardown) in a single command.

Requirements

• Python 3.12+
• A Modal account with CLI access (modal token new).
• Network access that allows Modal to pull the chosen CUDA container image.

Installation

# install from PyPI
uv add modal-cuda          # exposes the `mcc` command

# from source (editable)
uv sync && uv pip install -e .

# run command using uvx
uvx --from modal-cuda mcc ...

Usage

mcc path/to/program.cu [--gpu GPU] [--image IMAGE] [--timeout SECONDS] [--nvcc-arg FLAG ...]

Flag	Description
input	Path to a .cu file. The file must exist and be non-empty.
--app	Name of the Modal app; defaults to the source filename. Useful to group runs in the Modal dashboard.
--gpu	GPU type to request (T4, L4, A10, A100, A100-40GB, A100-80GB, L40S, H100, H200, B200). Defaults to T4.
--image	Container image reference passed to modal.Image.from_registry. Defaults to nvidia/cuda:12.4.1-devel-ubuntu22.04.
--timeout	Execution timeout (seconds) enforced by Modal. Defaults to 600.
--nvcc-arg	Additional flag forwarded to nvcc. Repeat for multiple flags (e.g., --nvcc-arg -arch=sm_90).

Example session:

# Run the provided sample kernel on an A100 GPU with verbose PTX output
mcc sample.cu --gpu A100 --nvcc-arg=-Xptxas --nvcc-arg=-v

Modal streams stdout/stderr as the compiler and executable run. If either command exits non‑zero, mcc surfaces the failure message and returns a non-zero status to your shell.

Development

1. Install dependencies with uv sync.
2. Run python -m mcc.main path/to/file.cu to skip the console entry point while iterating.
3. Use uv run python -m pip install -e . to get an editable install for local testing.

Troubleshooting

• modal.ClientException: Unauthorized -> Run modal token new and try again.
• nvcc failed to compile... -> Fix compiler errors shown in the streamed stderr or adjust --nvcc-arg flags.
• Long image pull times -> Prefer the default CUDA image or build a custom Modal image that layers your dependencies once and reuse it via --image.

License

MIT © ExpressGradient