{"id":23344,"date":"2023-07-27T07:15:09","date_gmt":"2023-07-27T05:15:09","guid":{"rendered":"https:\/\/info.gwdg.de\/news\/?p=23344"},"modified":"2023-08-01T08:33:12","modified_gmt":"2023-08-01T06:33:12","slug":"a-beginners-guide-to-deep-learning-with-gpus-on-grete","status":"publish","type":"post","link":"https:\/\/info.gwdg.de\/news\/a-beginners-guide-to-deep-learning-with-gpus-on-grete\/","title":{"rendered":"A Beginner’s Guide to Deep Learning with GPUs on Grete"},"content":{"rendered":"

Deep learning powers a broad range of applications, from autonomous cars and image generation to voice assistants. The successful training of deep learning models necessitates the use of graphics processing units (GPUs). To facilitate a quick and easy start with our GPUs, we’ve compiled a selection of materials designed to expedite your learning process. Simply follow the instructions below to explore the Nvidia A100 generation GPU on our cluster Grete!<\/p>\n

<\/a>Steps<\/h3>\n
    \n
  1. \n

    Get access<\/strong>: Create an NHR account at https:\/\/zulassung.hlrn.de<\/a>. This is free for all researchers based in Germany. Is this your first time on any cluster? Check out our bonus material on cluster concepts<\/a>.<\/p>\n<\/li>\n

  2. \n

    Acquire material<\/strong>: Get the freely-available course content at https:\/\/gitlab-ce.gwdg.de\/dmuelle3\/deep-learning-with-gpu-cores<\/a> . The theoretical part is contained in the slides<\/code> while the practical part is under code<\/code>.<\/p>\n<\/li>\n

  3. \n

    Practical session<\/strong>: Follow along to bring the workflow of the Gitlab repository to the cluster by watching the video series at https:\/\/www.youtube.com\/playlist?list=PLvcoSsXFNRblM4AG5PZwY1AfYEW3EbD9O<\/a> .<\/p>\n<\/li>\n<\/ol>\n

    What’s included in the course?<\/h3>\n