In the context of Deep Learning, the ITC Cluster organizes workshops in collaboration with NVIDIA Deep Learning Institute. A first one was organized at the end of 2019: Fundamentals of Deep Learning for Computer Vision

This year starts with a second workshop dealing going further:

GPUs have made a revolution in computational performance recently. However, using a GPU to accelerate computations has been hindered by an advanced C-like programming model. Numba brings all the benefits of accelerated computing to standard Python.

From number crunching to Deep Learning pre- and postprocessing, GPUs can complete hours-long processing in mere minutes. This course will teach everyone how to accelerate their software using the most userfriendly programming model there is.

Reach new competencies @CompetenceCentreNVIDIA Deep Learning Institute
Objectifs

This course is a one day workshop that explores how to use Numba — the just-in-time, type-specializing Python function compiler — to accelerate Python programs to run on massively parallel NVIDIA GPUs.

    You will learn how to:

  •         Use Numba to compile CUDA kernels from NumPy universal functions (ufuncs)
  •         Use Numba to create and launch custom CUDA kernels
  •         Apply key GPU memory management techniques

Upon completion, you will be able to use Numba to compile and launch CUDA kernels to accelerate your Python applications on NVIDIA GPUs.

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