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 |
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| 01 | NumPy Intro: ndarray Basics |
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| 02 | NumPy Linear Algebra: SVD Reconstruction | |
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| 03 | NumPy to CuPy: ndarray Basics |
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| 04 | NumPy to CuPy: SVD Reconstruction | |
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| 05 | Memory Spaces: Power Iteration | |
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| 06 | Asynchrony: Power Iteration | |
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| 07 | CUDA Core: Devices, Streams and Memory | |
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| # | Exercise | Link | Solution |
|---|---|---|---|
| 20 | cuDF: NYC Parking Violations | |
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| 21 | cudf.pandas: NYC Parking Violations | |
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| 22 | cuML | |
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| 23 | CUDA CCCL: Customizing Algorithms | |
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| 24 | nvmath-python: Interop | |
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| 25 | nvmath-python: Kernel Fusion | |
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| 26 | nvmath-python: Stateful APIs | |
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| 27 | nvmath-python: Scaling | |
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| 28 | PyNVML | |
| # | Exercise | Link | Solution |
|---|---|---|---|
| 40 | Kernel Authoring: Copy | |
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| 41 | Kernel Authoring: Book Histogram | |
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| 42 | Kernel Authoring: Gaussian Blur | |
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| 43 | Kernel Authoring: Black and White | |
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| # | Exercise | Link | Solution |
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| 60 | mpi4py | |
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| 61 | Dask | |