This modular tutorial contains content on all things related to accelerated Python:

• Notebooks containing lessons and exercises, intended for self-paced or instructor-led learning, which can be run on NVIDIA Brev or Google Colab.
• Slides containing the lecture content for the lessons.
• Syllabi that select a subset of the notebooks for a particular learning objective.
• Docker Images and Docker Compose files for creating Brev Launchables or running locally.

Brev Launchables of this tutorial should use:

• L40S, L4, or T4 instances (for non-distributed notebooks).
• 4xL4 or 2xL4 instances (for distributed notebooks).
• Crusoe or any other provider with Flexible Ports.

• CUDA Python - CuPy, cuDF, CCCL, & Kernels - 8 Hours.
• CUDA Python - cuda.core & CCCL - 2 Hours
• PyHPC - NumPy, CuPy, & mpi4py - 4 Hours

#	Exercise	Link	Solution
01	NumPy Intro: ndarray Basics
02	NumPy Linear Algebra: SVD Reconstruction
03	NumPy to CuPy: ndarray Basics
04	NumPy to CuPy: SVD Reconstruction
05	Memory Spaces: Power Iteration
06	Asynchrony: Power Iteration
07	CUDA Core: Devices, Streams and Memory

#	Exercise	Link	Solution
20	cuDF: NYC Parking Violations
21	cudf.pandas: NYC Parking Violations
22	cuML
23	CUDA CCCL: Customizing Algorithms
24	nvmath-python: Interop
25	nvmath-python: Kernel Fusion
26	nvmath-python: Stateful APIs
27	nvmath-python: Scaling
28	PyNVML

#	Exercise	Link	Solution
40	Kernel Authoring: Copy
41	Kernel Authoring: Book Histogram
42	Kernel Authoring: Gaussian Blur
43	Kernel Authoring: Black and White

#	Exercise	Link	Solution
60	mpi4py
61	Dask