Google expands TensorFlow open-source tooling for accelerated machine learning development

The large synthetic intelligence (AI) information at Google I/O right this moment is the launch of the corporate’s PaLM 2 massive language mannequin, however that’s not the one AI information on the occasion.

The firm can also be rolling out a collection of open-source machine learning (ML) expertise updates and enhancements for the rising TensorFlow ecosystem. TensorFlow is an open-source expertise effort, led by Google, that gives ML instruments to assist builders construct and practice fashions.

Google is launching its new DTensor expertise at Google I/O. This expertise brings new parallelism strategies to ML coaching, serving to to enhance mannequin coaching and scaling effectivity.

There can also be a preview launch of the TF Quantization API, which is meant to assist make fashions extra resource-efficient total and thus scale back the price of development.

A key a part of the TensorFlow ecosystem is the Keras API suite, which gives a set of Python language-based deep learning capabilities on high of the core TensorFlow expertise. Google is asserting a pair of latest Keras instruments: KerasCV for laptop imaginative and prescient (CV) purposes, and KerasNLP for pure language processing (NLP).

“A big part of what we’re looking at in terms of the tooling and the open-source space is really driving new capabilities and new efficiency and new performance,” Alex Spinelli, Google’s vice chairman of product administration for machine learning, informed VentureBeat. “Absolutely Google will build awesome, amazing AI and ML into its products, but we also want to kind of create a rising tide that lifts all ships, so we’re really committed to our open source strategies, and enabling developers at large.”

TensorFlow stays the ‘workhouse’ of machine learning at Google

In an period the place massive language fashions (LLMs) are all the fad, Spinelli emphasised that it’s now much more important than ever to have the precise ML coaching instruments.

“TensorFlow is still today the workhorse of machine learning,” he stated. “It is still … the fundamental underlying infrastructure [in Google] that powers a lot of our own machine learning developments.”

To that finish, the DTensor updates will present extra “horsepower” as the necessities of ML coaching proceed to develop. DTensor introduces extra parallelization capabilities to assist optimize coaching workflows.

Spinelli stated that ML total is simply getting extra hungry for knowledge and compute sources. As such, discovering methods to enhance efficiency in an effort to course of extra knowledge to serve the wants of more and more bigger fashions is extraordinarily essential. The new Keras updates will present much more energy, with modular elements that truly let builders construct their very own laptop imaginative and prescient and pure language processing capabilities. 

Still extra energy will come to TensorFlow because of the brand new JAX2TF expertise. JAX is a analysis framework for AI, extensively used at Google as a computational library, to construct applied sciences such because the Bard AI chatbot. With JAX2TF, fashions written in JAX will now be extra simply usable with the TensorFlow ecosystem.

“One of the things that we’re really excited about is how these things are going to make their way into products — and watch that developer community flourish,” he stated.

PyTorch vs TensorFlow

While TensorFlow is the workhorse of Google’s ML efforts, it’s not the one open-source ML coaching library.

In current years the open-source PyTorch framework, initially created by Facebook (now Meta), has turn out to be more and more standard. In 2022, Meta contributed PyTorch to the Linux Foundation, creating the brand new PyTorch Foundation, a multi-stakeholder effort with an open governance mannequin.

Spinelli stated that what Google is attempting to do is help developer alternative with regards to ML tooling. He additionally famous that TensorFlow isn’t simply an ML framework, it’s a complete ecosystem of instruments for ML that may assist help coaching and development for a broad vary of use instances and deployment eventualities.

“This is the same set of technologies, essentially, that Google uses to build machine learning,” Spinelli stated. “I think we have a really competitive offering if you really want to build large-scale high-performance systems and you want to know that these are going to work on all the infrastructures of the future.”

One factor Google apparently won’t be doing is following Meta’s lead and creating an unbiased TensorFlor Foundation group.

“We feel pretty comfortable with the way it’s developed today and the way it’s managed,” Spinelli stated. “We feel pretty comfortable about some of these great updates that we’re releasing now.”

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