env_cogen.yml

# Install or update environment
# conda env update --file env_cogen.yml --prune
#
# Remove environment
# conda remove --name sustaingym_cogen --all
#
# Notes
# - TensorFlow 2.14:
#   - the GPU version only works with Python <=3.10 (see https://github.com/tensorflow/tensorflow/issues/61986)
#   - TensorFlow 2.15 should fix this issue
# - Ray 2.8:
#   - officially only supports up to Python 3.10 (see https://docs.ray.io/en/latest/ray-overview/installation.html)
#   - only supports gymnasium 0.28.1 (see https://github.com/ray-project/ray/blob/ray-2.8.0/python/setup.py#L305)
#   - officially seems to only supports pettingzoo 1.23.1 (see https://github.com/ray-project/ray/blob/ray-2.8.0/python/requirements/ml/rllib-test-requirements.txt),
#     but empirically seems to work with pettingzoo 1.24.*
#
# last updated: December 7, 2023
name: sustaingym_cogen
channels:
- pytorch           # for pytorch
- nvidia            # for pytorch-cuda
- conda-forge
dependencies:
- python=3.10.*
- flake8
- ipympl              # for Jupyter / VSCode notebooks
- ipykernel           # for Jupyter / VSCode notebooks
- matplotlib
- mypy
- numpy=1.26.*
- openpyxl            # for reading Excel files
- pandas
- pip
- pytorch=2.1.*
- pytz=2023.3
- seaborn
- tqdm
- xlrd                # for reading Excel files

# for GPU. comment out for CPU-only.
- pytorch-cuda=11.8   # for PyTorch 2

- pip:
  - gymnasium==0.28.1
  - pettingzoo==1.24.*
  - ray[rllib]==2.8.*
  - onnxruntime==1.16.*    # the ONNX model for CogenEnv is small and runs sufficiently fast on CPU

  # uncomment for CPU-only
  # - tensorflow==2.14.*

  # for GPU. comment out for CPU-only.
  - tensorflow[and-cuda]==2.14.*