NeuralCompression is an innovative Python repository aimed at advancing neural network techniques for data compression, including image and video formats. In this article, we’ll guide you step-by-step on how to install and use NeuralCompression effectively.
What’s New?
Here’s a brief overview of the latest releases:
- August 2023 – Released PyTorch implementation of MS-ILLM: Link
- April 2023 – Released PyTorch implementation of VCT: Link
- November 2022 – Released Bits-Back coding with diffusion models: Link
About NeuralCompression
This project is a work in progress and offers tools like JAX-based entropy coders, image compression models, video compression models, and evaluation metrics. Keep in mind that as an alpha software, the API may undergo significant changes.
Installation
To start using NeuralCompression, you can install it via two primary methods:
PyPI Installation
First, ensure that you have installed PyTorch by following the instructions on the PyTorch website. After that, you can run the following command to install NeuralCompression:
bash
pip install neuralcompression
Development Installation
If you wish to contribute and work in development mode, clone the repository and navigate to the NeuralCompression root directory. Then, run:
bash
pip install --editable .[tests]
If you’re not interested in matching the test environment, simply apply:
bash
pip install -e .
Repository Structure
The NeuralCompression repository has a 2-tier structure. The core package offers high-quality tools for neural compression research, while the projects folder allows for quicker experimentation and paper reproduction. This allows for rapid iterations and effective learning from earlier work.
Understanding NeuralCompression Code
To explain the repository’s structure, think of it like a library. The neuralcompression section is like the reference room, containing reliable, rigorously-tested resources that you can build upon to conduct serious research. The projects section, on the other hand, is more like a community space where new ideas can be tried out without the constraints of formal reviews. This dual structure allows you to utilize a solid foundation while also exploring innovative methodologies.
Tutorial Notebooks
You can explore interactive notebooks available in the tutorials directory to get a deeper understanding of different parts of the package:
Troubleshooting
If you encounter any issues during installation or while using NeuralCompression, here are a few tips to help you out:
- Make sure you have the latest version of PyTorch installed, as it’s essential for NeuralCompression to function properly.
- Review the library documentation for any specific requirements or dependencies related to your operating system.
- Check for updates regularly as this is an alpha software and there may be frequent changes.
- If you cannot resolve an issue, consider posting your question on relevant community forums or the GitHub repository.
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Contributions
We highly encourage contributions! Please refer to our CONTRIBUTING guide and our CODE_OF_CONDUCT for more information on how to contribute effectively. All pull requests are welcomed, but please ensure they are thoroughly tested.
License
NeuralCompression is released under the MIT License, detailed in the LICENSE file. Model weights are CC-BY-NC 4.0 licensed, found in the WEIGHTS_LICENSE.
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
Conclusion
Getting started with NeuralCompression is an exciting venture into the frontier of AI-driven data compression. By following the steps above, you’ll be well on your way to utilizing this powerful tool for your projects.