Contents

Installation

We provided two options for users to set up the execution environment: - we provide the envrionment YML file so that one can set up the execution environment with it directly; - we provide the detailed steps and commands to install each required package.

Before starting, be sure to have the git and Anaconda3 installed (alternatively, you can also use Miniconda3 instead of Anaconda3, which has been tested by us and works well for our demo).

Set up from the YML file

  1. Get and clone the github repository:

    git clone https://github.com/FAIR-UMN/FAIR-UMN-ECAL

  2. Switch to FAIR-UMN-ECAL``(or ``FAIR-UMN-CDMS) (Note: XXX here indicates the upper directory of FAIR-UMN-ECAL (or FAIR-UMN-CDMS). For example, if you clone FAIR-UMN-ECAL``(or ``FAIR-UMN-CDMS) under /home/Download, then you should replace XXX with /home/Download.):

    cd XXX/FAIR-UMN-ECAL (or cd XXX/FAIR-UMN-CDMS)

  3. Deactivate conda base environment first you are in (otherwise, go to step 4 directly) (We use Anaconda3 ):

    conda deactivate

  4. Create a new conda environment with the YML file (choose GPU or CPU version according to your computational resources).

    GPU version run:

    conda env create -f fair_gpu.yml

    CPU version run:

    conda env create -f fair_cpu.yml

  5. Activate conda environment.

    If you choose the GPU version in Step4:

    conda activate fair_gpu

    If you choose the CPU version in Step4:

    conda activate fair_cpu

  6. You are now ready to explore the codes/models! Please remember to check the src/MAIN.ipynb first.

Set up from the source

  1. Get and clone the github repository:

    git clone https://github.com/FAIR-UMN/FAIR-UMN-ECAL

  2. Switch to FAIR-UMN-ECAL``(or ``FAIR-UMN-CDMS) (Note: XXX here indicates the upper directory of FAIR-UMN-ECAL``(or ``FAIR-UMN-CDMS). For example, if you clone FAIR-UMN-ECAL``(or ``FAIR-UMN-CDMS) under /home/Download, then you should replace XXX with /home/Download.):

    cd XXX/FAIR-UMN-ECAL (or cd XXX/FAIR-UMN-CDMS)

  3. Deactivate conda base environment first you are in (otherwise, go to step 4 directly) (We use Anaconda3 ):

    conda deactivate

  4. Create a new conda environment:

    conda create -n fair_umn python=3.6

  5. Activate conda environment:

    conda activate fair_umn

  6. Install Pytorch (choose GPU or CPU version according to your computational resources).

    GPU version run:

    conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch

    CPU version run:

    conda install pytorch torchvision torchaudio cpuonly -c pytorch

  7. Install scikit-learn/pandas/matplotlib/numpy/seaborn/tqdm/Jupyter notebook:

    pip install scikit-learn
pip install pandas
pip install matplotlib
pip install numpy
pip install seaborn
pip install tqdm
pip install notebook

  8. You are now ready to explore the codes/models! Please remember to check the src/MAIN.ipynb first.

Note: To install Anaconda, please follow its official guideline. For example, to install Anaconda3 on Linux, check the Linux doc; to install Anaconda3 on Windows, check the Windows doc; and to install Anaconda3 on macOS, check the Mac doc. We test our model on Ubuntu, Windows, and macOS.

Dependencies

pytorch

scikit-learn

pandas

matplotlib

numpy

seaborn

tqdm

notebook