graphcore-research/jax-scalify
JAX Scalify: end-to-end scaled arithmetics
This tool helps machine learning engineers and researchers accelerate the training and inference of deep neural networks. It takes your existing JAX-based neural network models and training data, then outputs a modified model that can run efficiently using lower precision numbers like BF16, FP16, or even FP8. This allows you to develop and deploy models faster and with less computational resources.
No commits in the last 6 months. Available on PyPI.
Use this if you are a deep learning practitioner working with JAX and want to speed up model training and inference by leveraging low-precision arithmetic without complex manual scaling implementations.
Not ideal if you are not using JAX, or if your primary goal is not related to optimizing neural network performance through precision scaling.
Stars
18
Forks
—
Language
Python
License
Apache-2.0
Category
Last pushed
Oct 30, 2024
Commits (30d)
0
Dependencies
5
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