Software Management with Anaconda (System, Python, R, Etc.)

Managing system packages, python, R and many other environments is easy with anaconda!

Step-by-step guide

1. Connect to head.arcc.albany.edu via SSH (see: How-to: Connect via SSH (PuTTY, macOS terminal, X2Go)

2. Run the following commands, line by line. Make sure you change [lab_directory] to be your lab directory name. 

   
   

   Installing miniconda

   cat ~/.bashrc >> ~/.bash_profile
   wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
   bash ~/miniconda.sh -b -p /network/rit/lab/[lab_directory]/miniconda
   /network/rit/lab/[lab_directory]/miniconda/bin/conda init
   cat ~/.bashrc >> ~/.bash_profile
   source ~/.bash_profile

3. Some helpful hints
   1. Do not install packages in your (base) environment (e.g. conda activate base). It is best to create separate environments for different packages, or conda is at a risk of breaking (e.g.

      conda create -n tensorflow python=3)

   2. Sharing conda across lab group members is possible, make sure the folder is g+rwx (chmod -R g+rwx /network/rit/lab/[lab_directory]/miniconda)
   3. Always check if conda has a software package available. There are many different channels, but researchers and software developers commonly publish to conda-forge (e.g. 

      conda install -c conda-forge ffmpeg)

   4. Read the docs: https://conda.io/projects/conda/en/latest/user-guide/index.html

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