Sionna Installation

Sionna is only required for development purposes. It is not required to run the OpenAirInterface stack.

Note

Sionna 2.0 requires PyTorch. See the platform-specific sections below.

Virtual Environment

We recommend using a virtual environment to isolate Python dependencies:

python3 -m venv ~/.venv/sionna-rk
source ~/.venv/sionna-rk/bin/activate

To activate the environment automatically, add to your ~/.profile:

echo 'source ~/.venv/sionna-rk/bin/activate' >> ~/.profile

DGX Spark

On DGX Spark, install all requirements (including PyTorch and Sionna):

pip install -r requirements.txt

Jetson Thor

On Jetson Thor, install all requirements:

pip install -r requirements_thor.txt

Jetson AGX Orin & Orin Nano

On Jetson Orin platforms, install all requirements:

pip install -r requirements_orin.txt

Note

For Jetson platforms, NVIDIA provides pre-built PyTorch wheels via the Jetson PyTorch containers. Refer to the NVIDIA documentation for the latest compatible PyTorch version for your JetPack release.

TensorRT Python Bindings

To access system TensorRT bindings in the virtual environment:

On AGX Orin:

echo 'export PYTHONPATH=$PYTHONPATH:/usr/lib/python3.10/dist-packages' >> ~/.profile
source ~/.profile

On AGX Thor:

echo 'export PYTHONPATH=$PYTHONPATH:/usr/lib/python3.12/dist-packages' >> ~/.profile
echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.profile
source ~/.profile

Verification

Verify that PyTorch detects the GPU:

import torch
print(torch.__version__)
print(torch.cuda.is_available())
print(torch.cuda.get_device_name(0))

Expected output (version string, CUDA availability, device name):

2.x.x
True
NVIDIA <device name>