Distributed Training with PyTorch on Piz Daint
The Swiss National Supercomputing Centre (CSCS) is pleased to announce the course Distributed Training with PyTorch on Piz Daint, which will be held online from November 22 to 24, 2021.
The Piz Daint supercomputer at CSCS provides an ideal platform for supporting intensive deep learning workloads as it comprises thousands of Tesla GPU compute nodes communicating through a high-speed interconnect. In this three-day course, we will look at how to run distributed deep learning workloads with PyTorch on Piz Daint.
This course is an update from last years’s workshop Efficient and Distributed Training with TensorFlow on Piz Daint. In this year's edition we will be using the PyTorch software ecosystem.
The main focus of the course will be training modules with PyTorch taking advantage of Piz Daint's setup of multiple single-gpu nodes. As a consecuence, this course is addressed to scientists who are planning or are already engaged in intensive machine learning workloads and wish to start using PyTorch on Piz Daint.
Participants are required to have basic knowledge of deep learning and some familiarity with PyTorch.
An agenda will be available closer to the courses dates.
The course will start on Monday, November 22, at 9:00 and end on Wednesday, November 24, at 16:00 Central European Time (CET).
Rafael Sarmiento (Computational Scientist, CSCS)
Henrique Mendonça (Computational Scientist, CSCS)
Registration for this program is free-of-charge. Mentors and learning materials are offered by CSCS.
All participants must register for the course. Registered attendees will receive the ZOOM details for participation at the email provided a few days prior to the workshop start. The link and password you will receive are unique to you and should not be shared with others.
Deadline for registration: November 14, 2021.
Please note that the workshop can take place only if there are sufficient registrations received by the deadline. The minimum number of participants is eight. Registration for the course will automatically close when we reach the maximum number of participants.
Inquiries may be addressed to email@example.com.