TensorFlow.js 2.0
In TensorFlow.js 2.x, the only breaking change will be moving the CPU and WebGL backends from tfjs-core into their own NPM packages (tfjs-backend-cpu and tfjs-backend-webgl respectively). While today these are included by default, we want to make tfjs-core as modular and lean as possible.What does this mean for me as a user?
If you are using the union package (i.e. @tensorflow/tfjs), you should see no impact to your code. If you are using @tensorflow/tfjs-core directly, you will need to import a package for each backend you want to use.What benefit do I get?
If you are using @tensorflow/tfjs-core directly, you will now have the option of omitting any backend you do not want to use in your application. For example, if you only want the WebGL backend, you will be able to get modest savings by dropping the CPU backend. You will also be able to lazily load the CPU backend as a fallback if your build tooling/app supports that.TensorFlow.js 3.0
In this release, we will have fully modularized all our ops and kernels (backend specific implementations of the math behind an operation). This will allow tree shakers in bundlers like WebPack, Rollup, and Closure Compiler to do better dead-code elimination and produce smaller bundles.We will move to a dynamic gradient and kernel registration scheme as well as provide tooling to aid in creating custom bundles that only contain kernels for a given model or TensorFlow.js program.
We will also start shipping ES2017 bundles by default. Users who need to deploy to browsers that only support earlier versions can transpile down to their desired target.
What does this mean for me as a user?
If you are using the union package (i.e. @tensorflow/tfjs), we anticipate the changes will be minimal. In order to support ease of use in getting started with tfjs, we want the default use of the union package to remain close to what it is today.For users who want smaller production oriented bundles, you will need to change your code to take advantage of ES2015 modules to import only the ops (and other functionality) you would like to end up in your bundle.
In addition, we will provide command-line tooling to enable builds that only load and register the kernels used by the models/programs you are deploying.
What benefit do I get?
Production oriented users will be able to opt into writing code that results in smaller more optimized builds. Other users will still be able to use the union package pretty much as is, but will not get the advantage of the smallest builds possible.Dynamic gradient and kernel registration will make it easier to implement custom kernels and gradients for researchers and other advanced users.
FAQ
When will this be ready?
We plan to release TensorFlow.js 2.0 this month. We do not have a release date for Tensorflow 3.0 yet because of the magnitude of the change. Since we need to touch almost every file in tfjs-core, we are also taking the opportunity to clean up technical debt where we can.Should I upgrade to TensorFlow.js 2.x or just wait for 3.x?
We recommend that you upgrade to TensorFlow 2.x if you are actively developing a TensorFlow.js project. It should be a relatively painless upgrade, and any future bug fixes will be on this release train. We do not yet have a release date for TensorFlow.js 3.x.How do I migrate my app to 2.x or 3.x? Will there be a tutorial to follow?
As we release these versions, we will publish full release notes with instructions on how to upgrade. Separately, with the launch of 3.x, we will publish a guide on making production builds.How much will I have to change my code to get smaller builds?
We’ll have more details as we get closer to the release of 3.x, but at a high level, we want to take advantage of the ES2015 module system to let you control what code gets into your bundle.In general, you will need to do things like
import {max, div, mul, depthToSpace} from @tensorflow/tjfs (rather than import * as tf from @tensorflow/tfjs) in order for our tooling to determine which kernels to register from the backends you have selected for deployment. We are even working on making the chaining API on the Tensor class opt-in when targeting production builds.Will this make TensorFlow.js harder to use?
We do not want to make the barrier to entry higher for using TensorFlow.js so we are designing this in a way that only production oriented users need to do extra work to get optimized builds. For end-users developing applications using the union package (@tensorflow/tfjs) from either a hosted script or from NPM in concert with our collection of pre-trained models we expect there will be no changes as a result of these updates Vollständiger Original-Bericht
Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org.
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