Sionna requires Python and Tensorflow. In order to run the tutorial notebooks on your machine, you also need JupyterLab. You can alternatively test them on Google Colab. Although not necessary, we recommend running Sionna in a Docker container.
Sionna requires TensorFlow 2.10 or newer and Python 3.6-3.9. We recommend Ubuntu 20.04. Earlier versions of TensorFlow may still work but are not recommended because of known, unpatched CVEs.
To run the ray tracer on CPU, LLVM is required by DrJit. Please check the installation instructions for the LLVM backend.
The ray tracing preview requires a recent version of JupyterLab. You can upgrade to the latest version via
pip install --upgrade ipykernel jupyterlab (requires restart of JupyterLab).
We refer to the TensorFlow GPU support tutorial for GPU support and the required driver setup.
Installation using pip
1.) Install the package
pip install sionna
2.) Test the installation in Python
>>> import sionna >>> print(sionna.__version__) 0.15.1
For a local installation, the JupyterLab Desktop application can be used. This directly includes the Python installation and configuration.
1.) Make sure that you have Docker installed on your system. On Ubuntu 20.04, you can run for example
sudo apt install docker.io
Ensure that your user belongs to the docker group (see Docker post-installation).
sudo usermod -aG docker $USER
Log out and re-login to load updated group memberships.
For GPU support on Linux, you need to install the NVIDIA Container Toolkit.
2.) Build the Sionna Docker image. From within the Sionna directory, run:
3.) Run the Docker image with GPU support
make run-docker gpus=all
or without GPU:
This will immediately launch a Docker image with Sionna installed, running JupyterLab on port 8888.
4.) Browse through the example notebook by connecting to http://127.0.0.1:8888 in your browser.