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INSTALL.md

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SDANNCE Installation Guide

To start with, clone the SDANNCE repository to your local machine, if not already:

git clone https://github.com/tqxli/sdannce.git
cd sdannce

Environment Setup

Prepare the runtime environment using Conda:

conda create --name sdannce python=3.8

conda activate sdannce

# REPLACEABLE with other workable PyTorch installation
conda install pytorch=1.9.1 torchvision=0.10.1 cudatoolkit=11.1 cudnn ffmpeg -c pytorch -c nvidia

pip install setuptools==59.5.0

pip install -e .

The installation was tested on Linux (Ubuntu 16.04, 20.04) with a NVIDIA GPU (Titan V, RTX 3090, A5000, A6000) and Conda version=4.10.3.

The code was tested for Python 3.7-3.8, PyTorch 1.8-1.9.1 (and torchvision that matches the PyTorch installation according to the official instructions https://pytorch.org/get-started/previous-versions/).

If you encounter any issues with installing PyTorch (at the step conda install pytorch=1.9.1 ...), specifically, using modern versions of Conda, please install with pip instead:

pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html

Users should be mindful of choosing PyTorch versions compatible with their local CUDA installation.