marek094 / calabi_yau_metrics

Project of the Log ML summer school to predict Calabi-Yau metrics

MLGeometry

Higher dimensional computational geometry using machine learning software

• Kahler geometry and Kahler-Einstein metrics

More to come.

Set up the environment

1. Install conda via Anaconda or Miniconda

2. Install the jupyter notebook in the base environment:

   conda install -c conda-forge notebook
   conda install -c conda-forge nb_conda_kernels
   conda install -c conda-forge cudatoolkit=11.8.0
   

3. Create the environment with necessary packages:

   conda create -n MLGeometry pip tensorflow-probability sympy matplotlib ipykernel
   

4. Activate the environment and install Tensorflow:

   conda activate MLGeometry
   python3 -m pip install nvidia-cudnn-cu11==8.6.0.163 tensorflow==2.12.*
   mkdir -p $CONDA_PREFIX/etc/conda/activate.d
   echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
   echo 'export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/:$CUDNN_PATH/lib:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
   source $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
   

5. Verify install:

   python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
   

6. Open Jupyter with jupyter-notebook in the command line, and change the kernel in Kernel -> Change kernel -> Python [conda env:MLGeometry]

7. Clone the repository

   git clone https://github.com/yidiq7/MLGeometry/
   

   Or download the released version here

Sample jupyter notebook

Leaderboard