HPFRACC development environment (summary)๏ƒ

This file mirrors the canonical setup in config/environment.yml and CONTRIBUTING.md. Prefer those sources if anything conflicts.

Overview๏ƒ

  • Conda env name: hpfracc (legacy names hpfracc-env / fracnn may still exist on older machines)

  • Python: 3.11 (recommended; CI smoke / full pytest / RTD use 3.11; main test workflow also runs 3.10 and 3.12)

  • Install: editable checkout with dev extras

Quick start๏ƒ

conda env create -f config/environment.yml   # or: conda env update -f config/environment.yml
conda activate hpfracc
pip install -e ".[dev]"

Optional shell helpers (from repo root):

source tools/activate_env.sh

Core stack (from config/environment.yml)๏ƒ

Versions drift over time; the YAML is authoritative. In general the env includes NumPy, SciPy, Matplotlib, Pandas, SymPy, JAX/jaxlib, Numba, PyTorch (via pip), Optax, pytest stack, Sphinx, Black, Flake8, Mypy, and pre-commit.

Testing๏ƒ

conda activate hpfracc
python -c "import hpfracc; print(hpfracc.__version__)"
python -m pytest tests/ -c pytest.ini --tb=short

Managing the environment๏ƒ

conda env list
# Recreate from repo spec (do not overwrite environment.yml with conda export unless you intend to pin everything):
conda env remove -n hpfracc   # only if you need a clean rebuild
conda env create -f config/environment.yml

Troubleshooting๏ƒ

  • CUDA / GPU: match driver and optional jax[cuda12] / PyTorch CUDA builds to your hardware (see README).

  • Import errors: confirm conda activate hpfracc and that pip install -e ".[dev]" was run from the repository root.

Performance๏ƒ

GPU memory and throughput depend on your hardware; use nvidia-smi (NVIDIA) or smaller batch sizes if you hit OOM during ML workloads.