BMED365-2025/environment-cuda.yml at main · MMIV-ML/BMED365-2025 · GitHub

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name: bmed365-cuda
channels:
  - conda-forge
dependencies:
  - python=3.11
  - ipython
  - ipykernel
  - ipywidgets
  - pandas
  - matplotlib
  - seaborn
  - jupyter
  - jupyterlab
  - git
  - pip
  - pip:
    - gdown
    - pooch
    - pycaret
    - shap
    - catboost
    - xgboost
    - pyreadstat
    - statsmodels
    - pingouin
    - voila
    - scikit-learn
    - scikit-image
    - SimpleITK
    - itkwidgets
    - imageio
    - pydicom
    - dcm2niix
    - readimc
    - tiffile
    - nilearn
    - networkx
    - dipy
    - templateflow
    - openpyxl
    - pygraphviz     # Lab1
    - pydot          # Lab1
    - python-louvain # Lab1
    - pytorch_lightning  # Lab2
    - openai
    - llm
    - torch-geometric
    - hyperopt
    # TabPFN            # https://github.com/PriorLabs/tabpfn
    - tabpfn
    - "git+https://github.com/PriorLabs/TabPFN.git#egg=tabpfn[dev]"
    - "git+https://github.com/priorlabs/tabpfn-extensions.git#egg=tabpfn-extensions"
    - ucimlrepo  # UC Irivine ML repository
    - streamlit

## Configure the Conda-environment bmed365-cuda for data analysis, computational imaging and modeling (only once):
# conda env create -f environment-cuda.yml

## To make a BMED365-CUDA kernel (optional):
# python -m ipykernel install --user --name bmed365-cuda --display-name "BMED365-CUDA"

## To activate the BMED365-CUDA conda environment:
# conda activate bmed365-cuda

## To update the bmed365-cuda conda environment:
# conda env update -f environment-cuda.yml

## To remove and reinstall the bmed365-cuda environment (if installation problems):
# conda deactivate
# conda env remove -n bmed65-cuda
# conda env create -f environment-cuda.yml