1. Installing Norse¶
We have chosen to build Norse with new features such as type hints. For that reason we require Python version 3.7 or higher. If this is a problem, it is recommended to install Norse from a Docker image.
1.1. Required dependencies¶
Because we are relying on optimised C++ for some of the hotspots in the library, you will need to download and install CMake and PyTorch before you can install Norse. For that reason, we recommend following the PyTorch “Get Started” guide as the first step.
You might also have to install Python headers if you have not already done that. In Debian-based distros (like Ubuntu),
this can be done by running
apt install python3-dev.
1.2. Installation steps¶
Note that the following commands require access to a command line interface.
pip install norse
Installing from Conda
conda install -c norse norse
Installing from source
git clone https://github.com/norse/norse cd norse python setup.py install
docker pull quay.io/norse/norse # Or, using CUDA docker pull quay.io/norse/norse:latest-cuda
1.3. Optional dependencies¶
Some of the tasks require additional dependencies like Pytorch Lightning, Torchtext and Torchvision. We also offer support for Tensorboard to make it easier to visualise the training and introspect models.
1.4. Running Norse notebooks with Docker¶
Docker creates a closed environment for you, which also means that the network and
filesystem is isolated. Without going into details, here are three steps you can
take to create a Jupyter Notebook environment with
Docker. You will have to replace
/your/directory with the full path to
your current directory.
docker run -it -p 8888:8888 -v /your/directory:/work quay.io/norse/norse bash pip3 install jupyter jupyter notebook --notebook-dir=/work --ip 0.0.0.0 --allow-root
The command line will now show you a URL you can copy-paste into your browser. And voila!
1.4.1. GPU acceleration¶
If you would like to have GPU hardware acceleration when running the
latest-cuda version of the
docker container, you will have to enable the NVIDIA runtime,
as described here: https://developer.nvidia.com/nvidia-container-runtime.
For more information on hardware acceleration, please refer to our page on Hardware acceleration.