Install

To use TFPWA, we need some dependent packages. There are two main ways, conda and virtualenv you can choose one of them. Or you can choose other method in 5. Other install method.

1. vitrual environment

To avoid conflict of dependence, we recommed to use vitrual environment. there are two main vitrual environment for python packages, conda and virtualenv. You can choose one of them. Since conda include cudatoolkit for gpu, we recommed it for user.

1.1 conda

  • 1.1.1 Get miniconda for python3 from miniconda3 and install it.

  • 1.1.2 Create a virtual environment by

conda create -n tfpwa

, the -n <name> option will create a environment named by <name>. You can also use -p <path> option to create environment in the <path> directory.

  • 1.1.3 You can activate the environment by

conda activate tfpwa

and then you can install packages in the conda environment

  • 1.1.4 You can exit the environment by

conda deactivate

1.2 virtualenv

  • 1.2.1 You should have a python3 first.

  • 1.2.2 Install virtualenv

python3 -m pip install --user virtualenv

  • 1.2.3 Create a virtual environment

python3 -m virtualenv ./tfpwa

, it will store in the path tfpwa

  • 1.2.4 You can activate the environment by

source ./tfpwa/bin/activete

  • 1.2.5 You can exit the environment by

deactivate

2. tensorflow2

The most important package is tensorflow2. We recommed to install tensorflow first. You can following the install instructions in tensorflow website (or tensorflow.org).

2.1 conda

Here we provide the simple way to install tensorflow2 gpu version in conda environment

conda install tensorflow-gpu=2.4

it will also install cudatoolkit.

2.2 virtualenv

When using virtualenv, there is also simple way to install tensorflow2

python -m pip install tensorflow

, but you should check you CUDA installation for GPU.

Note

You can use -i https://pypi.tuna.tsinghua.edu.cn/simple option to use pypi mirror site.

3. Other dependences

Other dependences of TFPWA is simple.

3.1 Get TFPWA package

Get the packages using

git clone https://github.com/jiangyi15/tf-pwa

3.2 conda

3.2.1 other dependences

In conda environment, go into the directory of tf-pwa, you can install the rest dependences by

conda install --file requirements-min.txt

Note

we recommed Ampere card users to install with tensorflow_2_6_requirements.txt (see this technical FAQ).

conda install --file tensorflow_2_6_requirements.txt -c conda-forge

3.2.2 TFPWA

install TFPWA

python -m pip install -e ./ --no-deps

Use --no-deps to make sure that no PyPI package will be installed. Using -e, so it can be updated by git pull directly.

3.3 virtualenv

In virtualenv, You can install dependences and TFPWA together.

python3 -m pip install -e ./

Using -e, so it can be updated by git pull directly.

4. (option) Other dependences.

There are some option packages, such as uproot for reading root file.

4.1 conda

It can be installed as

conda install uproot -c conda-forge

4.2 virtualenv

It can be installed as

python -m pip install uproot

5. Other install method.

We also provided other install method.

5.1 conda channel (experimental)

A pre-built conda package (Linux only) is also provided, just run following command to install it.

conda config --add channels jiangyi15
conda install tf-pwa

5.2 pip

When using pip, you will need to install CUDA to use GPU. Just run the following command :

python3 -m pip install -e .

6. For developer

To contribute to the project, please also install additional developer tools with:

python3 -m pip install -e .[dev]