Getting Started


It is highy recommended to use the Anaconda python distribution. Most of the required libraries (outside of the bleeding edge machine learning packages) will be included with Anaconda environments.

The simplest way to get up and running with twaml is to use the environment.yml file.

$ cd /path/to/twaml
$ conda env create -f environment.yml
$ conda activate twaml

The bare requirements for data handling, plotting, and testing include (enforced by requirements.txt, see file for verions):

  • uproot
  • pandas
  • scikit-learn
  • matplotlib
  • h5py
  • pytables
  • numexpr (to ensure pandas.eval acceleration)

Since twaml adopts “live at the head”, requirement versions may be fluid.

For training and testing models (not enforced by requirements.txt) you’ll probably want:

  • tensorflow
  • pytorch
  • xgboost

For building documentation

  • sphinx
  • sphinx_rtd_theme
  • sphinx-autodoc-typehints
  • sphinxcontrib-programoutput

Base Setup in a venv

A base setup without the machine learning libraries just requires a pip installation of the twaml.

$ python3 -m venv ~/.venvs/twaml-venv
$ source ~/.venvs/twaml-venv/bin/activate
$ cd /path/to/twaml
$ pip install .

This will make the and twaml.viz APIs accessible.

Example GPU Anaconda Setup

Start with a fresh Anaconda virtual environment:

$ cd /path/to/twaml
$ conda env create -f environment.yml
$ conda activate twaml
$ conda install pytorch torchvision cuda100 -c pytorch ## requires recent nvidia linux drivers
$ conda install tensorflow-gpu ## or just tensorflow
$ pip install .