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@@ -16,7 +16,12 @@ One of the most challenging problems in the popular orchestration framework Kube
```
conda activate smartscheduler
```
5. If you want to use GPUs make sure that you have the correct version of CUDA and cuDNN installed from [here](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html). Alternatively, you can check [cudnn-compatibility](https://punndeeplearningblog.com/development/tensorflow-cuda-cudnn-compatibility) to find compatible versions and install CUDA and cuDNN with conda from [cudatoolkit](https://anaconda.org/anaconda/cudatoolkit) and [cudnn](https://anaconda.org/conda-forge/cudnn), respectively. MAKE SURE THER VERSIONS OF PYTHON, CUDA, CUDNN, AND TENSORFLOW IN YOUR CONDA ENVIRONMENT ARE COMPATIBLE.
5. If you want to use GPUs make sure that you have the correct version of CUDA and cuDNN installed from [here](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html). Alternatively, you can check [cudnn-compatibility](https://punndeeplearningblog.com/development/tensorflow-cuda-cudnn-compatibility) to find compatible versions and install CUDA and cuDNN with conda from [cudatoolkit](https://anaconda.org/anaconda/cudatoolkit) and [cudnn](https://anaconda.org/conda-forge/cudnn), respectively. Make sure ther versions of python, cuda, cudnn, and tensorflow in your conda environment are compatible.
6. Use [PyTorch](https://pytorch.org/) or [Tensorflow](https://www.tensorflow.org/install/pip#virtual-environment-install) isntallation manual to install one of them based-on your preference
