Installation¶

Install from PyPI¶

pip install deepecgkit

Or with uv:

uv add deepecgkit

Clone the repository and install with development dependencies:

git clone https://github.com/stevenah/deepecg-kit.git
cd deepecg-kit
uv sync --group dev

This installs the package in editable mode along with testing and linting tools (pytest, ruff).

To build and preview the documentation site:

uv sync --group docs
make docs-serve

This starts a local server at http://127.0.0.1:8000 with live-reloading.

DeepECG-Kit automatically detects available hardware:

Device	Detection
CUDA GPU	Used automatically if `torch.cuda.is_available()`
Apple MPS	Used automatically if `torch.backends.mps.is_available()`
CPU	Fallback when no GPU is available

You can override device selection with --accelerator cpu|gpu|mps (CLI) or device="cpu" (Python API).