Getting Started¶
Requirements¶
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 twaml.data
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 .