The document outlines the CUDA DLI training sessions at GTC 2019, emphasizing hands-on training in various computing domains including accelerated computing, data science, and GPU programming. Participants will learn to enhance applications using CUDA, Numba, and RAPIDS, covering topics such as memory management, performance optimization, and data science workflows. The sessions aim to equip attendees with practical skills through industry-standard tools and frameworks for effective GPU utilization.

1. CUDA DLI TRAINING SESSIONS AT GTC 2019

2. EXPECTED TO BE THE BIGGEST YET, GTC
FEATURES SESSIONS AND DLI TRAINING ON THE
MOST IMPORTANT TOPICS IN COMPUTING TODAY

3. WHY DLI HANDS-ON TRAINING?
● LEARN HOW TO BUILD APPS ACROSS INDUSTRY SEGMENTS
● GET HANDS-ON EXPERIENCE USING INDUSTRY-STANDARD SOFTWARE, TOOLS & FRAMEWORKS
● GAIN EXPERTISE THROUGH CONTENT DESIGNED WITH INDUSTRY LEADERS

4. FUNDAMENTALS OF ACCELERATED COMPUTING WITH CUDA
PYTHON
This course 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’ll 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
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The CUDA computing platform enables acceleration of
CPU-only applications to run on the world's fastest
massively parallel GPUs. Learn how to accelerate C/C++
applications by:
● Exposing the parallelization of CPU-only
applications, and refactoring them to run in parallel
on GPUs
● Successfully managing memory
● Utilizing CUDA parallel thread hierarchy to further
increase performance
ACCELERATING APPLICATIONS WITH CUDA C/C++

6. CUDA ON DRIVE AGX
Explore how to write CUDA code and run it on
DRIVE AGX. You'll learn about:
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● Hardware architecture of DRIVE AGX
● Memory Management of iGPU and dGPU
● GPU acceleration for inferencing

7. ACCELERATING DATA SCIENCE WORKFLOWS WITH
RAPIDS
The open source RAPIDS project allows data scientists
to GPU-accelerate their data science and data
analytics applications from beginning to end, creating
possibilities for drastic performance gains and
techniques not available through traditional CPU-only
workflows. Learn how to GPU-accelerate your data
science applications by:
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● Utilizing key RAPIDS libraries like cuDF & cuML
● Learning techniques and approaches to end-to-
end data science
● Understanding key differences between CPU-
driven and GPU-driven data science

8. DEBUGGING AND OPTIMIZING CUDA APPLICATIONS
WITH NSIGHT PRODUCTS ON LINUX TRAINING
Learn how NVIDIA tools can improve development
productivity by narrowing down bugs and spotting
areas of optimization in CUDA applications on a Linux
x86_64 system.
Through a set of exercises, you'll gain hands-on
experience using NVIDIA's new Nsight Systems and
Nsight Compute tools for debugging, narrowing down
memory issues, and optimizing a CUDA application.
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9. ACCELERATED DATA SCIENCE PIPELINE WITH
RAPIDS ON AZURE
Learn how to deploy RAPIDS machine learning jobs
on NVIDIA's GPUs using Microsoft Azure and
explore:
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● Azure Portal Permits: a convenient way to
perform functional experimentation with RAPIDS.
● Azure Machine Learning (AML) SDK: enables a
batch experimentation mode and where the user
can set ranges on different parameters to be run
on a RAPIDS program, saving the results for later
analysis

10. HIGH PERFORMANCE COMPUTING USING CONTAINERS
Learn to build, deploy and run containers in an HPC
environment.
During this session, you will learn: the basics of
building container images with Docker and Singularity,
how to use HPC Container Maker (HPCCM) to make it
easier to build container images for HPC applications,
and how to use containers from the NGC with
Singularity.
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11. INTRODUCTION TO CUDA PYTHON WITH NUMBA
Explore an introduction to Numba, a just-in-time
function compiler that allows developers to utilize
the CUDA platform in Python applications. You'll
learn how to:
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● Decorate Python functions to be compiled by
Numba
● Use Numba to GPU accelerate NumPy ufuncs

12. CUDA PROGRAMMING IN PYTHON WITH NUMBA AND
CUPY
Combining Numba, an open source compiler that can
translate Python functions for execution on the GPU,
with the CuPy GPU array library, a nearly complete
implementation of the NumPy API for CUDA, creates a
high productivity GPU development environment.
Learn the basics of using Numba with CuPy, techniques
for automatically parallelizing custom Python functions
on arrays, and how to create and launch CUDA kernels
entirely from Python.
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13. REGISTER TODAY FOR GTC
AND EXPLORE THE FULL
LIST OF CUDA TRAINING,
TALKS & EXPERT SESSIONS
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