# Environment Setup for Deep Learning

# Framework

# PyTorch

We will use PyTorch on Python 3.6 as our main deep learning framework for the lab sessions for now. Later, we may expand these lab sessions with other popular deep learning frameworks such as TensorFlow and Theano. Your helps for adopting other frameworks or for enriching the lab materials are always welcome!

# Python Backend

We recommend you to use Anaconda for your Python backend (the tutorial notebooks are based on Python 3.6).

# GPU acceleration

If you have a proper NVIDIA GPU(s) with a driver installed, you just need to install the associated version of PyTorch binary, which contains CUDA Toolkit and cuDNN library already.

※ For MacOS: MacOS Binaries dont support CUDA, install from source if CUDA is needed after installing CUDA Toolkit (8.0, 9.0 or 10.0) and cuDNN first.

# Install PyTorch 1.0

You will use PyTorch 1.0 throughout the lab series.

# Linux and Windows

# CUDA 8.0, 9.0, or 10.0

conda install pytorch torchvision cudatoolkit=<CUDA version> -c pytorch

For example, if you are using CUDA 9.0 toolkit,

conda install pytorch torchvision cudatoolkit=9.0 -c pytorch

# CPU only

conda install pytorch-cpu torchvision-cpu -c pytorch

# macOS

# CPU Only

conda install pytorch torchvision -c soumith

Mac users who want to use your GPU, you will need to build PyTorch from the source. Here is a good blog post about it (link).

# Install using pip

Please refer to PyTorch get started page.

# Notebooks

If you want to download all notebook files to your local:

git clone https://github.com/ast0414/CSE6250BDH-LAB-DL.git

For more details, please refer to the official homepage of PyTorch.