Accelerated Python Tutorial
November 16, 2025 ยท View on GitHub
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.
Syllabi
- CUDA Python - CuPy, cuDF, CCCL, & Kernels - 8 Hours.
- CUDA Python - cuda.core & CCCL - 2 Hours
- PyHPC - NumPy, CuPy, & mpi4py - 4 Hours
Notebooks
Fundamentals
| # | 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 | | |
Libraries
| # | 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 | |
Kernels
| # | Exercise | Link | Solution |
|---|---|---|---|
| 40 | Kernel Authoring: Copy | | |
| 41 | Kernel Authoring: Book Histogram | | |
| 42 | Kernel Authoring: Gaussian Blur | | |
| 43 | Kernel Authoring: Black and White | | |
Distributed
| # | Exercise | Link | Solution |
|---|---|---|---|
| 60 | mpi4py | | |
| 61 | Dask | |