Contributing & Development¶

Thanks for helping improve KeepGPU! This page collects the key commands and expectations so you can get productive quickly and avoid surprises in CI.

Setup¶

Clone and install dev extras: bash git clone https://github.com/Wangmerlyn/KeepGPU.git cd KeepGPU pip install -e ".[dev]" # add .[rocm] if you need ROCm SMI
Ensure you have the right torch build for your platform (CUDA/ROCm/CPU).
Optional: install nvidia-ml-py (CUDA) or rocm-smi (ROCm) for telemetry.

Fast CUDA suite: bash pytest tests/cuda_controller tests/global_controller \ tests/utilities/test_platform_manager.py tests/test_cli_thresholds.py
ROCm-only tests are marked rocm and skipped by default; run with: bash pytest --run-rocm tests/rocm_controller
MCP + utilities: bash pytest tests/mcp tests/utilities/test_gpu_info.py
All tests honor markers rocm and large_memory; avoid enabling large_memory in CI.

Start: keep-gpu-mcp-server (stdin/stdout JSON-RPC)
HTTP option: keep-gpu-mcp-server --mode http --host 0.0.0.0 --port 8765
Methods: start_keep, stop_keep, status, list_gpus
Example request: json {"id":1,"method":"start_keep","params":{"gpu_ids":[0],"vram":"512MB","interval":60,"busy_threshold":20}}
Remote tip: for shared clusters, prefer HTTP behind your own auth/reverse-proxy or tunnel with SSH (ssh -L 8765:localhost:8765 gpu-box), then point your MCP client at http://127.0.0.1:8765/.

Keep changesets focused; small commits are welcome.
Add/adjust tests for new behavior; skip GPU-specific tests in CI by way of markers.
Update docs/README when behavior or interfaces change.
Stick to the existing style (Typer CLI, Rich logging) and keep code paths simple—avoid over-engineering.