Installation & Setup๏ƒ

Basic Installation๏ƒ

Install the core library via pip:

pip install hpfracc

GPU & Machine Learning Support๏ƒ

For high-performance research involving GPU acceleration and ML, we recommend the following setup:

# 1. Install PyTorch with CUDA 12.8
pip install torch==2.9.0 --index-url https://download.pytorch.org/whl/cu128

# 2. Install JAX with CUDA support
pip install --upgrade "jax[cuda12]"

# 3. Install HPFRACC with ML extras
pip install hpfracc[ml,gpu]

Requirements๏ƒ

  • Python: 3.10+ (matches requires-python in pyproject.toml)

  • Backends: PyTorch (>=1.12), JAX (>=0.4.0), Numba (>=0.56.0)

  • GPU: CUDA-compatible hardware (optional)

Intelligent Backend Selection๏ƒ

HPFRACC automatically detects your hardware and selects the best backend for each operation.

  • PyTorch is generally selected for large-scale training.

  • JAX is used for complex spectral operations.

  • Numba provides sub-microsecond latency for small-scale local calculations.

Verification๏ƒ

Verify your installation with this simple check:

import hpfracc
from hpfracc.ml.backends import BackendManager

print(f"HPFRACC version: {hpfracc.__version__}")
print(f"Active Backends: {BackendManager.get_available_backends()}")