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Software Tools

Software Tools

  • Git and GitHub
  • Julia
  • Jupyter Notebooks
  • Machine Learning frameworks (Tensorflow, PyTorch, Keras, OpenCV)
  • R and Python
  • Rclone
  • SAS/CONNECT
  • Software Environments via Conda
  • Stata and Maintaining Version Stability

Julia

Julia

  • Compute Cluster
    • Technical Benefits and Features
    • Quick Start
    • Requesting an Account
    • Logging In
    • Copying & Extracting Files
    • Running Jobs
    • Software Tools
      • Git and GitHub
      • Julia
      • Jupyter Notebooks
      • Machine Learning frameworks (Tensorflow, PyTorch, Keras, OpenCV)
      • R and Python
      • Rclone
      • SAS/CONNECT
      • Software Environments via Conda
      • Stata and Maintaining Version Stability
  • Data Storage
  • Database Server
  • Other Research Computing Environments
4ms

The Julia programming language is a flexible dynamic language, appropriate for scientific and numerical computing, combining features of imperative, functional, and object-oriented programming. Julia provides ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also supports general programming. To achieve this, Julia builds upon the lineage of mathematical programming languages, but also borrows much from popular dynamic languages, including Lisp, Perl, Python, Lua, and Ruby. With little effort, the performance of Julia programs can be comparable to traditional statically-typed languages like C or Java.

Julia is installed and accessible via software modules in terminal / shell sessions -- as SSH login sessions or terminal windows in the NoMachine/Gnome desktop -- and require users to submit jobs via custom submission scripts (standard LSF bsub commands). Julia is not yet available via default submission scripts (wrapper scripts).

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