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I'm also experiencing this issue. If I only load the .yml file to create the environment I get conflicting versions of tensorflow. I know very little about coding so I'm unsure how to troubleshoot this.
I built step-by-step starting with the tensorflow package and going from there. My final build (and all dependency versions) are included in the attached file (sedinet.txt) (it's saved as a .txt for upload here but you can just change the extension to .yml if you choose to download it). Essentially I followed the tensorflow guidelines at https://www.tensorflow.org/install/pip for windows native with a nvidia quadra; I had to initialize the sedinet conda environment with python 3.9 to be able to install tensorflow < 2.11. After that was installed I was able to add the other dependent packages without any environment issues.
Commands:
conda create --name sedinet python=3.9
conda activate sedinet
(the next couple are from the tensorflow website)
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
pip install "tensorflow<2.11"
pip install tensorflow-addons
pip install image
conda install -c conda-forge pandas h5py requests scikit-learn joblib tqdm scikit-image matplotlib scipy
conda install -c conda-forge ipython
The requirements.txt contains local file paths like:
threadpoolctl @ file:///tmp/tmp79xdzxkt/threadpoolctl-2.1.0-py3-none-any.whl
tifffile @ file:///home/conda/feedstock_root/build_artifacts/tifffile_1595072593978/workand
the .yml archive likely has the same issues.
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