Build the docker image:
docker build -t pysr .
Start the docker image, with Jupyterlab open at port 8000:
docker run -it --rm -p 8000:8000 -v "${PWD}:/workspace" --memory=8g --cpus=4 pysr python3 -m jupyter notebook --ip="*" --port=8000 --no-browser --allow-root
We will now have a Jupyterlab instance running at http://localhost:8000.
We can now work through the tutorial notebook: pysr_demo.ipynb
.
For custom modifications to the backend, see: https://astroautomata.com/PySR/backend/.
Note that since we are sharing the workspace with -v $(pwd):/workspace
, we have access
to a local copy of SymbolicRegression.jl
. Therefore, if we set:
model = PySRRegressor(
...,
julia_project="/workspace/SymbolicRegression.jl",
)
then we will use the local copy of SymbolicRegression.jl
instead of the one installed with PySR.