gpu-proc 0.1.0

Explicit local GPU memory and compute stress process for scheduling and monitoring tests.

gpu-proc

gpu-proc is an explicit local GPU stress process for testing schedulers, monitoring alerts, dashboards, and GPU capacity behavior on machines you are authorized to use.

It can reserve a configurable fraction of one CUDA device's memory and keep the device busy with repeated matrix multiplication. The default target is 0.95 of GPU memory with a finite 60 second run.

Actual utilization reported by tools such as nvidia-smi depends on the GPU, driver, backend, matrix size, and sampling window.

This package is intentionally not a daemon, not stealthy, and not persistent. Run it only on hardware you own or have permission to test.

Install

Install the package itself:

pip install gpu-proc

Install one backend:

pip install "gpu-proc[torch]"

or, for CUDA 12 CuPy environments:

pip install "gpu-proc[cupy-cuda12]"

You can also install the package from a local checkout:

pip install -e ".[torch,dev]"

Quick start

Create a config file:

gpu-proc init-config

Run with explicit confirmation:

gpu-proc run --yes

Run from a specific config:

gpu-proc run --config ./gpu-proc.toml --yes

Check backend availability:

gpu-proc doctor

Config

Default path:

~/.config/gpu-proc/config.toml

Example:

backend = "auto"
device = 0
memory_fraction = 0.95
duration_seconds = 60
matrix_size = 4096
dtype = "float32"
chunk_mb = 256
reserve_mb = 256
sync_every = 16
progress_interval_seconds = 5
allow_indefinite = false
require_confirmation = true

Set duration_seconds = 0 only with allow_indefinite = true; stop the process with Ctrl-C.

Build and publish

Prepare build tooling:

python -m pip install --upgrade build twine

Build distributions:

python -m build

Check artifacts:

python -m twine check dist/*

Upload to TestPyPI first:

python -m twine upload --repository testpypi dist/*

Then upload to PyPI:

python -m twine upload dist/*

Use a PyPI API token instead of an account password.