Installation

Installation

1. Install the package

   pip

   pip install epftoolbox2

   uv

   uv add epftoolbox2

   From source

   git clone https://github.com/dawidlinek/epftoolbox2.git
   cd epftoolbox2
   pip install -e .

2. Verify installation

   import epftoolbox2
   
   epftoolbox2.verify()

3. Configure for parallel processing (optional but recommended)

   See the section below for GIL-free Python setup.

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Requirements

• Python 3.10+ (Python 3.14t recommended for parallel processing)
• pandas, numpy, scikit-learn
• requests (for API sources)
• openpyxl (for Excel export)
• rich (for terminal output)

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GIL-Free Python & Multithreading Python 3.13t+

For parallel model training on multi-core systems, use Python 3.14t (free-threading build) which removes the Global Interpreter Lock (GIL).

Note

Multithreading in epftoolbox2 requires GIL-free Python. Standard Python builds will run training sequentially.

Installing Python 3.14t

uv

uv python install 3.14t
uv venv --python 3.14t

pyenv

pyenv install 3.14.0t
pyenv local 3.14.0t

Complete Setup

Caution

For GIL disabling to work, environment variables must be set before running Python.

1. Set environment variables before running Python

   PowerShell

   $env:PYTHON_GIL=0; python your_script.py

   CMD

   set PYTHON_GIL=0 && python your_script.py

   Linux/Mac

   PYTHON_GIL=0 python your_script.py

   X flag

   python -Xgil=0 your_script.py

2. Configure threading in your script (optional)

   import os
   
   # Set number of parallel threads (adjust to your CPU cores)
   os.environ["MAX_THREADS"] = "16"
   
   # Prevent nested threading from NumPy/BLAS libraries
   os.environ["OMP_NUM_THREADS"] = "1"
   os.environ["MKL_NUM_THREADS"] = "1"
   os.environ["OPENBLAS_NUM_THREADS"] = "1"

3. Import the library

   from epftoolbox2.pipelines import DataPipeline, ModelPipeline
   from epftoolbox2.models import OLSModel, LassoCVModel

Tip

With GIL-free Python, model training is much faster on multi-core systems.