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env_bench.yml
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env_bench.yml
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# 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- outdated=0.2.*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