Create PT env with geopandas, pytorch and libraries for spatio_temporal `ml`

ml_env.sh

# python=3.8
conda create -n ml --yes 
conda activate ml
conda install pytorch torchvision cudatoolkit -c pytorch --yes
# conda install -c conda-forge seaborn=0.11 --yes3 
# conda install -c conda-forge jupyterlab=3.0.16 --yes
conda install -c conda-forge jupyterlab seaborn --yes
conda install -c conda-forge netcdf4 numba tqdm tensorboard ipython pip ruamel.yaml xarray descartes statsmodels scikit-learn black mypy eofs xskillscore cdsapi cftime seaborn gcsfs zarr loguru google-cloud-bigquery --yes
conda install -c conda-forge cartopy pyflwdir --yes
conda install -c conda-forge pytest pytest-cov --yes
conda install -c anaconda flake8 --yes
conda install -c conda-forge pyarrow sacred --yes
pip install geopandas

ipython --pdb -c "import torch; assert torch.cuda.is_available(); print(torch.backends.cudnn.version()); print(torch.cuda.device_count());"

# autoclosing jupyterlab quotes/brackets - https://github.com/jupyterlab/jupyterlab/issues/9897

# setup nvidia drivers
# https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=18.04&target_type=deb_local
# https://cloud.google.com/compute/docs/gpus/install-drivers-gpu

# get nvidia toolkit
# https://linuxconfig.org/how-to-install-nvidia-driver-on-debian-10-buster-linux
# sudo apt install nvidia-cuda-toolkit

# Connect to gcloud instance using ssh keys
# https://stackoverflow.com/questions/27535945/how-to-access-ssh-keys-for-a-google-cloud-platform-compute-engine-vm-instance