A framework for building optimization modules to boost the performances of your AI systems
Documentation: docs.nebuly.com/
Nebullvm is a framework for building the optimization modules needed to optimize the performances of your AI systems. The optimization modules are stack-agnostic and work with any library. They are designed to be easily integrated into your system, providing a quick and seamless boost to its performance. Simply plug and play to start realizing the benefits of optimized performance right away.
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What can this help with?
There are multiple modules we actually provide built on top of the framework:
✅ Speedster: Automatically apply the best set of SOTA optimization techniques to achieve the maximum inference speed-up on your hardware.
✅ OpenAlphaTensor: Increase the computational performances of an AI model with custom-generated matrix multiplication algorithm fine-tuned for your specific hardware.
✅ Forward-Forward: The Forward Forward algorithm is a method for training deep neural networks that replaces the backpropagation forward and backward passes with two forward passes.
Next modules and roadmap
We are actively working on incorporating the following modules, as requested by members of our community, in upcoming releases:
- CloudSurfer: Automatically discover the optimal cloud configuration and hardware on AWS, GCP and Azure to run your AI models.
- OptiMate: Interactive tool guiding savvy users in achieving the best inference performance out of a given model / hardware setup.
Contributing
As an open source project in a rapidly evolving field, we welcome contributions of all kinds, including new features, improved infrastructure, and better documentation. If you're interested in contributing, please see the linked page for more information on how to get involved.
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Metadata
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Release files for nebullvm 0.10.0
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| nebullvm-0.10.0-py3-none-any.whl | Python 3 | none | any | Details for nebullvm-0.10.0-py3-none-any.whl |
Total release size: 356.0 kB
Release files / nebullvm-0.10.0.tar.gz
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