https://github.com/gjbex/python-on-gpus
Repository for the training on using GPUs from Python.
https://github.com/gjbex/python-on-gpus
cuda cupy gpu numba pycuda python training
Last synced: 9 months ago
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Repository for the training on using GPUs from Python.
- Host: GitHub
- URL: https://github.com/gjbex/python-on-gpus
- Owner: gjbex
- License: cc-by-4.0
- Created: 2022-06-14T07:17:11.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2025-07-10T08:59:19.000Z (12 months ago)
- Last Synced: 2025-07-10T17:17:02.112Z (12 months ago)
- Topics: cuda, cupy, gpu, numba, pycuda, python, training
- Language: Jupyter Notebook
- Homepage: https://gjbex.github.io/Python-on-GPUs/
- Size: 1.84 MB
- Stars: 12
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# Python on GPUs
GitHub repository for participants of the "Python on GPUs" training.
For information on the training, see the website
[https://gjbex.github.io/Python-on-GPUs/](https://gjbex.github.io/Python-on-GPUs/)
**Note: this is work in progress**
## What is it?
1. [`source-code`](source-code): sample code written to develop the slides and
illustrate concepts.
1. [`python_on_gpus_science_linux64_conda_specs.txt`](python_on_gpus_linux64_conda_specs.txt):
conda environment specification file specific for 64-bit Linux to precisely
reproduce the environment on which the code was developed.
1. [License](LICENSE): license information for the material in this repository.
1. [Contributing](CONTRIBUTING.md): information on how to contribute to this
repository.
1. docs: directory containing the website for this repository.
## Environment setup
Each of the subdirectories of the `source-code` directory contains an `environment.yml`
file that can be used to create a conda environment with the necessary packages.