Create conda environment
$ conda create -n mioflow python=3.10
$ conda activate mioflow
install pytorch according to instructions https://pytorch.org/get-started/
we used
$ conda install pytorch pytorch-cuda=11.6 -c pytorch -c nvidia
install requirements and MIOFlow
$ pip install -e .
Remove unused packages and caches.
$ conda clean --all
Install MIOFlow for developers and internal use:
$ cd path/to/this/repository
$ pip install -e MIOFlow
For greater detail see the official docs for
nb_conda_kernels
.
In short, install nb_conda_kernels
in the environment from which you
launch JupyterLab / Jupyter Notebooks from (e.g. base
) via:
$ conda install -n <notebook_env> nb_conda_kernels
to add a new or exist conda environment to Jupyter simply install
ipykernel
into that conda environment e.g.
$ conda install -n <python_env> ipykernel
add to your Jupyter Notebook kernels via
$ python -m ipykernel install --user --name sklab-mioflow
It can be removed via:
$ jupyter kernelspec uninstall sklab-mioflow
kernels recognized by conda
$ python -m nb_conda_kernels list
check which kernels are discovered by Jupyter:
$ jupyter kernelspec list
This repository consists of our python library MIOFlow
as well as a
directory of scripts for running and using it.
To recreate our results with MMD loss and density regulariazation you can run the following command:
python scripts/run.py -d petals -c mmd -n petal-mmd
This will generate the directory results/petals-mmd
and save
everything there.
For a full list of parameters try running:
python scripts/run.py --help
One could simply import everything and use it piecemeal:
from MIOFlow.ode import *
from MIOFlow.losses import *
from MIOFlow.utils import *
from MIOFlow.models import *
from MIOFlow.plots import *
from MIOFlow.train import *
from MIOFlow.constants import *
from MIOFlow.datasets import *
from MIOFlow.exp import *
from MIOFlow.geo import *
from MIOFlow.eval import *
One can also consult or modify the tutorial notebooks for their uses: - EB Bodies tutorial - Dyngen tutorial - Petals tutorial