https://github.com/neuralmagic/examples
Notebooks using the Neural Magic libraries 📓
https://github.com/neuralmagic/examples
Last synced: about 1 year ago
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Notebooks using the Neural Magic libraries 📓
- Host: GitHub
- URL: https://github.com/neuralmagic/examples
- Owner: neuralmagic
- Created: 2023-07-12T16:42:43.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-07-24T17:37:45.000Z (almost 2 years ago)
- Last Synced: 2025-04-15T07:05:38.349Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 45.3 MB
- Stars: 42
- Watchers: 12
- Forks: 8
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Neural Magic 🪄 Model Optimization Examples
Deep learning models have grown bigger over the years making their deployment complex and expensive. [Neural Magic](https://github.com/neuralmagic)
is building model optimization tools to change this narrative. We provide tools for optimizing these models to make them more
performant and easier to deploy. For example, you can use [SparseML](https://github.com/neuralmagic/sparseml) to
make your model sparse in a couple of lines of code. Neural Magic also provides numerous optimized natural language and computer vision models on the
[SparseZoo](https://sparsezoo.neuralmagic.com/). When you are ready to move to production, you can take advantage of the [DeepSparse](https://github.com/neuralmagic/deepsparse)
inference runtime to achieve GPU-class performance on commodity CPUs.
In this repository, you will find examples that show how to use Neural Magic's tools to apply optimizations on your models.
You will also find examples on how to use Neural Magic's libraries with other machine learning packages.
## Highlighted Notebooks
Here are three notebooks you can look at immediately:
### Stable Diffusion on CPUs With DeepSparse
[](https://colab.research.google.com/github/neuralmagic/notebooks/blob/main/notebooks/deepsparse-stable-diffusion/notebook.ipynb)
In this notebook, you will learn how to perform text-to-image generation on DeepSparse using stable diffusion models.
DeepSparse accelerates inference, enabling the images to be generated on commodity CPUs.
### Optimize YOLOv5 Model With SparseML
[](https://colab.research.google.com/github/neuralmagic/examples/blob/main/notebooks/yolov5-custom-data/yolov5_custom_data.ipynb)
This notebook will take you through the process of optimizing a YOLOv5 model for real-time object detection. You will also
learn how to deploy the resulting model on CPU using DeepSparse.
### Run Hugging Face Models on DeepSparse
[](https://colab.research.google.com/github/neuralmagic/notebooks/blob/main/notebooks/optimum-deepsparse/optimum-deepsparse.ipynb)
DeepSparse supports numerous transformer models. This notebook illustrates how to pull models from Hugging Face using
Optimum and run them on CPU using DeepSparse.
## Bugs and Contribution
Let us know in case something isn't working by [creating an issue](https://github.com/neuralmagic/examples/issues). You
can also propose a fix by [submitting a pull request](https://github.com/neuralmagic/examples/pulls). Join our
[Slack community](https://neuralmagic.com/community/) to
engage with other practitioners or inform us of any examples you'd like to see.