# 
# To create the conda environment:
# $ conda env create -f interpret_cpu.yaml
# To update the conda environment:
# $ conda env update -f interpret_cpu.yaml
# To register the conda environment in Jupyter:
# $ conda activate interpret_cpu
# $ python -m ipykernel install --user --name interpret_cpu --display-name "Python (interpret_cpu)"
# 
name: interpret_cpu
channels:
- defaults
- conda-forge
- pytorch
dependencies:
- python==3.7.0
- pip>=19.1.1
- ipykernel>=4.6.1
- jupyter>=1.0.0
- matplotlib>=2.2.2
- numpy>=1.13.3
- pandas>=0.24.2
- pytest>=3.6.4
- pytorch-cpu>=1.0.0
- scipy>=1.0.0
- tensorflow==1.12.0
- h5py>=2.8.0
- tensorflow-hub==0.5.0
- py-xgboost<=0.80
- dask[dataframe]==1.2.2
- pip:
  - azureml-sdk[automl,notebooks,contrib,explain]==1.0.57
  - cached-property==1.5.1
  - papermill>=1.0.1
  - nteract-scrapbook>=0.2.1
  - pytorch-pretrained-bert>=0.6
  - tqdm==4.31.1
  - scikit-learn>=0.19.0,<=0.20.3
  - nltk>=3.4
  - interpret-community>=0.1.0.2
  - pre-commit>=1.20.0
  - spacy>=2.2.3
  - transformers==2.4.1
  - pydantic==1.4