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  • Machine Learning frameworks (Tensorflow, PyTorch, Keras, OpenCV)→

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

Machine Learning frameworks (Tensorflow, PyTorch, Keras, OpenCV)

Machine Learning frameworks (Tensorflow, PyTorch, Keras, OpenCV)

  • 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
6ms

The HBSGrid offers artificial intelligence(AI) and machine learning (ML) capabilities through its Conda AI environment. This environment includes:

  • Python v3.8
  • TensorFlow(-gpu) v2.0
  • PyTorch v1.3
  • Keras v2.3
  • numpy v1.19
  • scipy 1.5
  • pandas v1.1
  • scikitlearn v0.23
  • matplotlib v3.3
  • the Spyder IDE v4.1

The AI environment can be loaded using the following command in your terminal window:

module load AI/python.3.7.7

To learn more about software modules on the HBSGrid, please see our general info on software modules.

The ML/AI frameworks can be run on the standard compute nodes or on our compute node equipped with NVidia GPUs. Please see GPU Computing for more information on how to submit jobs to utilize the GPUs.

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Research Computing Services (RCS) 
Harvard Business School
Baker Library, B90, 25 Harvard Way
Boston, MA 02163
Phone: 617.495.6100
Email: research@hbs.edu
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