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  • How-to: Software Management with Anaconda (Software, Python, R, Etc.)
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Managing system packages, python, R and many other environments is easy with anaconda!

Note that you want to install miniconda in a network lab directory as it will quickly fill up your home directory!

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
    # download the install script
    wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
    
    # execute the installer
    bash ~/miniconda.sh -b -p /network/rit/lab/[lab_directory]/miniconda
    
    # initialize the environment to your shell 
    /network/rit/lab/[lab_directory]/miniconda/bin/conda init
    
    # If you are running bash, this is important
    cat ~/.bashrc >> ~/.bash_profile
    
    # finally, source your environment, you will only need to do this once. This will only work if your $SHELL is bash
    source ~/.bash_profile
  3. Using a centralized conda install for lab members. 
    1. There are some risks to be aware of, if a lab member updates a conda environment it could break other users code. Best practice is to have one environment per project, it will save you headaches in the long run!

      Steps to allow lab members to access conda
      # Make sure all contents of the folder are g+rwx
      chmod g+rwx -R /network/rit/lab/[lab_directory]/miniconda/
      
      # now, each lab member must execute the following commands:
      
      # initialize the environment to your shell 
      /network/rit/lab/[lab_directory]/miniconda/bin/conda init
      
      # If you are running bash, this is important
      cat ~/.bashrc >> ~/.bash_profile
      
      # finally, source your environment, you will only need to do this once. This will only work if your $SHELL is bash
      source ~/.bash_profile
  4. 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