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  • ♪ (intro music) ♪

  • At Google, we've been on a journey to make the training, deployment,

  • managing, and scaling of machine-learned models

  • as easy as possible.

  • To that end, we're now delighted to say we've released TensorFlow 2.0.

  • TensorFlow 2.0 has been driven by feedback from lots of folks in the community,

  • from individual developers, to enterprises, to researchers,

  • telling us that they want an easy-to-use framework

  • that is both flexible and powerful

  • and supports deployment to any platform.

  • TensorFlow 2.0 provides a comprehensive ecosystem of tools

  • for developers, enterprises, and researchers

  • who want to push the state of the art of machine learning

  • and build scalable ML-powered applications.

  • With TensorFlow 2.0, we strive to make development

  • of machine-learned applications much easier.

  • With tight integration of Keras into TensorFlow,

  • eager execution by default,

  • and an emphasis on Pythonic function execution,

  • instead of sessions,

  • the goal is to make the experience of developing applications

  • with TensorFlow 2.0

  • as familiar as possible for Python developers.

  • Our drive to make simpler APIs does not come at the expense

  • of giving you the flexibility

  • to develop advanced customizations for your needs.

  • We have invested heavily to create a more complete low-level API.

  • So, for example, you can now export all ops that were used internally

  • and provide inheritable interfaces for crucial concepts

  • such as variables and checkpoints.

  • This allows you to build on top of the internals to TensorFlow

  • without having to rebuild TensorFlow.

  • For example, creating your own optimizer, like you can see here.

  • We have standardized on the Saved model file format

  • to run models on a variety of runtimes,

  • including the Cloud, Web, Browser,

  • Node.js, Mobile and embedded systems.

  • This allows you to not just run your model to a TensorFlow

  • but deploy them to Web and Cloud with TensorFlow Extended.

  • You can use them on mobile and embedded systems with TensorFlow Lite,

  • and you can train and run them in the browser on Node.js

  • with TensorFlow.js.

  • With the distribution strategy API, you'll be able to distribute training

  • with minimal code changes, yielding great out-of-the-box performance.

  • It supports distributed training with Keras's

  • as well as with custom training loops.

  • Multi-GPU support is available,

  • and, of course, TensorFlow 2.0 also supports TensorRT

  • for fast inference on GPUs.

  • Check out the guide for more details.

  • Another feedback item we heard was that making access to data easy

  • would be a massive benefit.

  • Much of the code that developers write is in managing and preparing their data.

  • So we have expanded TensorFlow datasets,

  • giving a standard interface to a variety of diverse datasets,

  • including those containing images, text, video, and much more.

  • While the traditional session-based programming model is still maintained,

  • we recommend using regular Python with eager execution.

  • The tf.function decorator can be used to convert your code into graphs

  • which can then be executed remotely, serialized, and optimized for performance.

  • This is complemented by autograph, which is built into tf.function,

  • and it can convert regular Python control flow

  • directly into TensorFlow control flow.

  • And, of course, if you've used TensorFlow 1.x

  • and you're looking for a migration guide to 2.0,

  • we've published a guide which includes an automatic conversion script

  • to help you get started.

  • If you want to learn how to build applications

  • using TensorFlow 2.0,

  • take a look at the effective 2.0 guide

  • and then try out our online courses that we've created together

  • with the folks from and Udacity.

  • For more about TensorFlow 2.0, including how to download

  • and get started with coding machine-learned applications,

  • check out the official TensorFlow site at

  • ♪ (ending music) ♪

♪ (intro music) ♪


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TensorFlow 2.0を発表 (Announcing TensorFlow 2.0)

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    林宜悉 に公開 2021 年 01 月 14 日