11-06, 09:00–12:20 (US/Eastern), Central Park West
Accelerating Python using the GPU is much easier than you might think. We will explore the powerful CUDA-enabled Python ecosystem in this tutorial through hands-on examples using some of the most popular accelerated scientific computing libraries.
Topics include:
- Introduction to General Purpose GPU Computing
- GPU vs CPU - Which processor is best for which tasks
- Introduction to CUDA
- How to use CUDA with Python
- Using Numba to write kernel functions
- CuPy
- cuDF
No prior experience with GPU's is necessary, but attendees should be familiar with Python.
To get the most from your hands-on learning experience, please complete these steps prior to getting started:
- Review the agenda, prerequisites, and suggested material for full-day workshops (as detailed in the course datasheet below). This is an important step to properly prepare for the workshop.
- Create or log into your NVIDIA Developer Program account - https://courses.nvidia.com/join. You will receive an email letting you know when your account is ready. This account will provide you with access to all of the DLI training materials during and after the workshop. You will have three months of access to all course materials.
- Visit websocketstest.courses.nvidia.com and make sure all three test steps are checked “Yes.” This will test the ability for your system to access and deliver the training contents. If you encounter issues, try updating your browser. Note: Only Chrome and Firefox are supported.
- Check your bandwidth. 1 Mbps downstream is required and 5 Mbps is recommended. This will ensure consistent streaming of audio/video during the workshop to avoid glitches and delays.
Now you’re ready to get started with the tutorial!
Simply enter the code NVIDIA_XLAB_NV24 at courses.nvidia.com/dli-event
No previous knowledge expected
Jacob Tomlinson is a senior Python software engineer at NVIDIA with a focus on deployment tooling for distributed systems. His work involves maintaining open source projects including RAPIDS and Dask. RAPIDS is a suite of GPU accelerated open source Python tools which mimic APIs from the PyData stack including those of Numpy, Pandas and SciKit-Learn. Dask provides advanced parallelism for analytics with out-of-core computation, lazy evaluation and distributed execution of the PyData stack. He also tinkers with the open source Kubernetes Python framework kr8s in his spare time. Jacob volunteers with the local tech community group Tech Exeter and lives in Exeter, UK.
Dr. Katrina Riehl is a Principal Technical Product Manager at NVIDIA supporting CUDA and Python. For over two decades, Katrina has worked extensively in the fields of scientific computing, machine learning, data science, and visualization. Most notably, she has helped lead initiatives at the University of Texas Austin Applied Research Laboratory, Anaconda, Apple, Expedia Group, Cloudflare, and Snowflake. She is an active volunteer in the Python open-source scientific software community and continues to serve on the Advisory Council for NumFOCUS.
Mike is a Senior Software Engineering Manager at NVIDIA, leading teams working on RAPIDS Cloud and HPC deployments, build infrastructure, and PyData projects. Mike is a former member of the advisory counsel at NumFOCUS and Prefect. He holds two BS degrees in Computer Science and Physics, and has over 20 years of experience in astronomy, computational sciences, data science, machine learning, and enterprise products.