env_bench.yml

# Creates a conda environment named "sustainbench".
#
# Install and update this environment with
# conda env update -f env_bench.yml --prune
#
# Uses conda-forge channel whenever possible to avoid the multiple-channel
# mismatch problem.
#   See https://conda-forge.org/docs/user/tipsandtricks.html
#
# For TensorFlow 2.5's pinned dependencies, see
# https://github.com/tensorflow/tensorflow/blob/r2.5/tensorflow/tools/pip_package/setup.py
name: sustainbench_create
channels:
  - pytorch
  - nvidia                # only needed if using GPU with CUDA 11.1
  - conda-forge
  - defaults
dependencies:
  - python=3.9.*            # only supported by TensorFlow 2.5+
  - cpuonly                 # from `pytorch` channel, to force only CPU
  # - cudatoolkit=10.2        # from `pytorch` channel, only needed if using GPU with CUDA 10.2
  # - cudatoolkit=11.1        # from `nvidia` channel, only needed if using GPU with CUDA 11.1
  - flake8=3.9.*
  - grpcio=1.34.*             # required for TensorFlow 2.5 compatibility
  - jupyterlab=3.1.*
  - mypy=0.910
  - numpy=1.19.*              # TensorFlow's numpy requirement is pinned: https://github.com/tensorflow/tensorflow/blob/r2.5/tensorflow/tools/pip_package/setup.py
  - outdated=0.2.*
  - pandas=1.3.*
  - pip
  - pytorch=1.9.*             # from `pytorch` channel
  - scipy=1.7.*
  - six=1.15.*                # required for TensorFlow 2.5 compatibility
  - torchvision               # from `pytorch` channel
  # - tensorflow=1.15.*       # for CPU
  # - tensorflow-gpu=1.15.*   # for GPU support
  - tqdm=4.62.*
  - typing_extensions=3.7.*   # required for TensorFlow 2.5 compatibility
  - pip:
    - tensorflow==2.5.*
    - gdown