Installation#
Quick installation#
The fastest way of installing this package is through PyPI or Conda:
python3 -m pip install tensorwaves[jax]
To install the package with support for amplitude analysis run:
python3 -m pip install tensorwaves[jax,pwa]
conda install -c conda-forge tensorwaves jax jaxlib
To install the package with support for amplitude analysis run:
conda install -c conda-forge ampform phasespace
This installs the latest release that
you can find on the stable
branch.
Optional dependencies can be installed as follows:
pip install tensorwaves[pwa] # installs tensorwaves with ampform
pip install tensorwaves[jax,scipy,tf]
pip install tensorwaves[all] # all runtime dependencies
GPU support
Computations with are fastest on a GPU. To get JAX and TensorFlow work with your graphics card, you need to install at least CUDA Toolkit 11.2 and cuDNN 8.1.
Below is an installation guide for installing TF and JAX with GPU support. These instructions may become outdated, so refer to this TF page an these JAX instructions if you run into trouble.
Download and install CUDA Toolkit 11.x by following these platform-dependent instructions. There may be dependency conflicts with existing NVIDIA packages. In that case, have a look here.
Tell the system where to find CUDA. In Ubuntu (or WSL-Ubuntu), this can be done by adding the following line to your
.bashrcfile:export PATH="$PATH:/usr/local/cuda/bin"
But first, check if
/usr/local/cuda/binindeed exists!Restart your machine. Then, launch a terminal to check whether CUDA is installed:
nvcc --versionDownload and install cuDNN following these instructions. Make sure that you download cuDNN for CUDA 11.x!
In Ubuntu (Debian), there are two convenient options: (1) installing through
aptor (2) using a local installer.[Recommended] Install JAX with GPU binaries in your virtual environment following these instructions.
pip install --upgrade jax[cuda] jaxlib -f https://storage.googleapis.com/jax-releases/jax_releases.html
It doesnāt matter whether you do this before or after installing TensorWaves.
If TensorFlow can correctly find your GPU, the following should return a non-empty list:
python3 -c 'import tensorflow as tf; print(tf.config.list_physical_devices("GPU"))'
If JAX can correctly find your GPU, the following should return
gpu:python3 -c 'from jax.lib import xla_bridge; print(xla_bridge.get_backend().platform)'
The latest version on the main
branch (or any other branch, tag, or commit) can be installed as follows:
python3 -m pip install git+https://github.com/ComPWA/tensorwaves@main
Or, with optional dependencies:
python3 -m pip install "tensorwaves[jax,pwa] @ git+https://github.com/ComPWA/tensorwaves@main"
Developer installation#
To contribute to the project, you need to install the package in a virtual environment. This can be done best with uv (see installation instructions here). For this, you first need to get the source code with Git:
git clone https://github.com/ComPWA/tensorwaves
cd tensorwaves
Now itās simply a matter of creating and activating the virtual environment with uv sync. The dependencies for the project are āpinnedā in each commit through the uv.lock file.
uv sync --all-extras
source .venv/bin/activate
Formatting and linting checks are automatically performed when committing changes. This is done with pre-commit. To install the hooks in your local repository, run install pre-commit with uv:
uv tool install pre-commit --with pre-commit-uv --force-reinstall --python=3.13
and pre-commit install once:
pre-commit install --install-hooks
Poe the Poet is used as a task runner. Install it globally (within your home folder) with uv:
uv tool install poethepoet --force-reinstall --python=3.13
You can see which local CI checks it defines by running
poe
For instance, all style checks can be run with
poe style
Thatās all! Have a look at General examples to try out the package. You can also have a look at Help developing for tips on how to work with this āeditableā developer setup!