Installation Instructions¶
This toolkit requires and is tested on Python versions 3.6 and above. It may work with older Python versions with some fiddling, but is currently not tested nor supported. The easiest way to get started is to use the published pip package:
pip install neurox
This will install the toolkit and all its dependencies. You can then use the toolkit by importing the relevant modules like:
1 2 3 4 5 | import neurox.data.extraction.transformers_extractor as transformers_extractor
transformers_extractor.extract_representations(...)
from neurox.analysis.visualization import TransformersVisualizer
visualizer = TransformersVisualizer('bert-base-uncased')
|
Manual Installation¶
If you wish to install this package manually (e.g. to modify or contribute to the code base), you can clone the repository into a directory of your choice:
git clone https://github.com/fdalvi/NeuroX.git
Add the directory to your python path. This can be done dynamically at runtime using the sys.path list:
import sys
sys.path.append("path/to/cloned/NeuroX/")
A Conda environment is provided with all the necessary dependencies for the toolkit. The toolkit primarily relies on PyTorch and NumPy for most of its operations. To create a new environment with all the dependencies, run:
conda env create -f conda-environment.yml -n neurox-toolkit
conda activate neurox-toolkit
If you wish to manage your enviroment in other ways, a standard requirements.txt is also provided for use by pip directly.