Google's official response: we have not given up tensorflow and will develop side by side with Jax in the future

Author Chen Ping

Source: heart of machine

TensorFlow Not one of Google 「 sacrifice 」, Will continue to develop .

Some time ago ,AI There is a saying in the world , I mean ,TensorFlow It has become a piece of Google 「 sacrifice 」.

There are also foreign media Business Insider Interviewed a series of developers 、 Hardware expert 、 Cloud providers and people who work closely with Google machine learning , Got the same view ：2015 Year of the birth of TensorFlow, Once brilliant , and Meta stay 2017 Open source in PyTorch Is becoming the overlord in this field , In this war , Google started betting JAX.

People close to the project have told Insider, Google brain and DeepMind Give up to a large extent TensorFlow, Switch to JAX. A Google insider also asked Insider confirmed ,JAX Now almost in Google brain and DeepMind Is adopted worldwide .

People familiar with the project also said ,JAX Now it is expected to become the pillar of all Google products using machine learning in the next few years , It's like TensorFlow stay 2015 What I did a few years later .

For a while , About Google to give up TensorFlow, Full turn JAX Everyone knows the saying of . Revelations from Google insiders , Make people more convinced that this statement is true .

What are the facts ？TensorFlow The official blog responded , This response comes from TensorFlow Two researchers in the team Laurence Moroney and Josh Gordon. In conclusion, it is ： Google hasn't given up like the Internet TensorFlow, Will continue to invest in research and development .

Google ： We didn't give up TensorFlow, Will work with JAX Develop side by side

Stack Overflow Recently released a survey report , According to the report TensorFlow Is the most commonly used ML Tools , By 300 Million software developers adopt . In fact, this figure is just beginning , It is estimated that there will be 400 Million developers hope to adopt it in the near future .

Picture source ：https://survey.stackoverflow.co/2022/#most-loved-dreaded-and-wanted-misc-tech-want

TensorFlow Now it is downloaded more than every month 18M Time , stay GitHub On the accumulation of 166k star —— More than any other ML There are many frames . Inside Google ,TensorFlow Almost all of it AI Production workflow , Include search 、 advertisement 、YouTube、GMail、 Map 、Play etc. . It's also apple 、Netflix、Stripe、 tencent 、 Uber 、 Roche 、LinkedIn、Twitter、 Baidu 、LVMH And so on .

Google Scholar Last month there were more than 3000 A publication mentions TensorFlow, Including important applied scientific research , For example, people who know about cancer CANDLE Research .

In recent years , We learned that a single common framework cannot be applied to all scenarios —— Especially in the field where production and frontier research often conflict . therefore , We created JAX, This is a simplification for distributed numerical computation API, Provide power for the next era of scientific computing research .JAX Very good at driving new areas ： Reach a new parallel scale , In addition, it also promotes new algorithms and architectures , And help researchers develop new compilers and systems .JAX It is really popular in the field of scientific research .

In this multi frame world ,TensorFlow We give it to the application ML A developer's answer —— Engineers need to build and deploy reliable on different scales and platforms 、 Stable 、 High performance ML System . Our vision is to create a cohesive ecosystem , Researchers and engineers can use system components for research , No matter which framework they originated from . We've been JAX and TensorFlow Significant progress has been made in interoperability , especially jax2tf Development of . Development JAX The researchers of the model can pass TensorFlow Platform tools put them into production .

future , We continue to develop TensorFlow , And regard it as a first-class application ML platform , And JAX Push together ML Research and development . We will continue to ML Invest in the framework , To promote research and application , Serve our millions of users .

Reference link ：https://blog.tensorflow.org/2022/06/%20bringing-machine-learning-to-every-developers-toolbox.html?m=1

This article is only for academic sharing , If there is any infringement , Please contact to delete .

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