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Platform and environment

TensorFlow.js works in the browser and Node.js, and in both platforms there are many different available configurations. Each platform has a unique set of considerations that will affect the way applications are developed.

In the browser, TensorFlow.js supports mobile devices as well as desktop devices. Each device has a specific set of constraints, like available WebGL APIs, which are automatically determined and configured for you.

In Node.js, TensorFlow.js supports binding directly to the TensorFlow API or running with the slower vanilla CPU implementations.

Environments

When a TensorFlow.js program is executed, the specific configuration is called the environment. The environment is comprised of a single global backend as well as a set of flags that control fine-grained features of TensorFlow.js.

Backends

TensorFlow.js support multiple different backends that implement tensor storage and mathematical operations. At any given time, only one backend is active. Most of the time, TensorFlow.js will automatically choose the best backend for you given the current environment. However, sometimes it's important to know which backend is being used and how to switch it.

To find which backend you are using:

console.log(tf.getBackend());

If you want to manually change the backend:

tf.setBackend('cpu');
console.log(tf.getBackend());

WebGL backend

The WebGL backend, 'webgl', is currently the most powerful backend for the browser. This backend is up to 100x faster than the vanilla CPU backend. Tensors are stored as WebGL textures and mathematical operations are implemented in WebGL shaders. Here are a few useful things to know when using this backend: \

Avoid blocking the UI thread

When an operation is called, like tf.matMul(a, b), the resulting tf.Tensor is synchronously returned, however the computation of the matrix multiplication may not actually be ready yet. This means the tf.Tensor returned is just a handle to the computation. When you call x.data() or x.array(), the values will resolve when the computation has actually completed. This makes it important to use the asynchronous x.data() and x.array() methods over their synchronous counterparts x.dataSync() and x.arraySync() to avoid blocking the UI thread while the computation completes.

Memory management

One caveat when using the WebGL backend is the need for explicit memory management. WebGLTextures, which is where Tensor data is ultimately stored, are not automatically garbage collected by the browser.

To destroy the memory of a tf.Tensor, you can use the dispose() method:

const a = tf.tensor([[1, 2], [3, 4]]);
a.dispose();

It is very common to chain multiple operations together in an application. Holding a reference to all of the intermediate variables to dispose them can reduce code readability. To solve this problem, TensorFlow.js provides a tf.tidy() method which cleans up all tf.Tensors that are not returned by a function after executing it, similar to the way local variables are cleaned up when a function is executed:

const a = tf.tensor([[1, 2], [3, 4]]);
const y = tf.tidy(() => {
  const result = a.square().log().neg();
  return result;
});
Precision

On mobile devices, WebGL might only support 16 bit floating point textures. However, most machine learning models are trained with 32 bit floating point weights and activations. This can cause precision issues when porting a model for a mobile device as 16 bit floating numbers can only represent numbers in the range [0.000000059605, 65504]. This means that you should be careful that weights and activations in your model do not exceed this range. To check whether the device supports 32 bit textures, check the value of tf.ENV.getBool('WEBGL_RENDER_FLOAT32_CAPABLE'), if this is false then the device only supports 16 bit floating point textures. You can use tf.ENV.getBool('WEBGL_RENDER_FLOAT32_ENABLED') to check if TensorFlow.js is currently using 32 bit textures.

Shader compilation & texture uploads

TensorFlow.js executes operations on the GPU by running WebGL shader programs. These shaders are assembled and compiled lazily when the user asks to execute an operation. The compilation of a shader happens on the CPU on the main thread and can be slow. TensorFlow.js will cache the compiled shaders automatically, making the second call to the same operation with input and output tensors of the same shape much faster. Typically, TensorFlow.js applications will use the same operations multiple times in the lifetime of the application, so the second pass through a machine learning model is much faster.

TensorFlow.js also stores tf.Tensor data as WebGLTextures. When a tf.Tensor is created, we do not immediately upload data to the GPU, rather we keep the data on the CPU until the tf.Tensor is used in an operation. If the tf.Tensor is used a second time, the data is already on the GPU so there is no upload cost. In a typical machine learning model, this means weights are uploaded during the first prediction through the model and the second pass through the model will be much faster.

If you care about the performance of the first prediction through your model or TensorFlow.js code, we recommend warming the model up by passing an input Tensor of the same shape before real data is used.

For example:

const model = await tf.loadLayersModel(modelUrl);

// Warmup the model before using real data.
const warmupResult = model.predict(tf.zeros(inputShape));
warmupResult.dataSync();
warmupResult.dispose();

// The second predict() will be much faster
const result = model.predict(userData);

Node.js TensorFlow backend

In the TensorFlow Node.js backend, 'node', the TensorFlow C API is used to accelerate operations. This will use the machine's available hardware acceleration, like CUDA, if available.

In this backend, just like the WebGL backend, operations return tf.Tensors synchronously. However, unlike the WebGL backend, the operation is completed before you get the tensor back. This means that a call to tf.matMul(a, b) will block the UI thread.

For this reason, if you intend to use this in a production application, you should run TensorFlow.js in worker threads to not block the main thread.

For more information on Node.js, see this guide.

WASM backend

TensorFlow.js provides a WebAssembly backend (wasm), which offers CPU acceleration and can be used as an alternative to the vanilla JavaScript CPU (cpu) and WebGL accelerated (webgl) backends. To use it:

// Set the backend to WASM and wait for the module to be ready.
tf.setBackend('wasm');
tf.ready().then(() => {...});

If your server is serving the .wasm file on a different path or a different name, use setWasmPath before you initialize the backend. See the "Using Bundlers" section in the README for more info:

import {setWasmPath} from '@tensorflow/tfjs-backend-wasm';
setWasmPath(yourCustomPath);
tf.setBackend('wasm');
tf.ready().then(() => {...});
Why WASM?

WASM was introduced in 2015 as a new web-based binary format, providing programs written in JavaScript, C, C++, etc. a compilation target for running on the web. WASM has been supported by Chrome, Safari, Firefox, and Edge since 2017, and is supported by 90% of devices worldwide.

Performance

WASM backend leverages the XNNPACK library for optimized implementation of neural network operators.

Versus JavaScript: WASM binaries are generally much faster than JavaScript bundles for browsers to load, parse, and execute. JavaScript is dynamically typed and garbage collected, which can cause slowdowns at runtime.

Versus WebGL: WebGL is faster than WASM for most models, but for tiny models WASM can outperform WebGL due to the fixed overhead costs of executing WebGL shaders. The “When should I use WASM” section below discusses heuristics for making this decision.

Portability and Stability

WASM has portable 32-bit float arithmetic, offering precision parity across all devices. WebGL, on the other hand, is hardware-specific and different devices can have varying precision (e.g. fallback to 16-bit floats on iOS devices).

Like WebGL, WASM is officially supported by all major browsers. Unlike WebGL, WASM can run in Node.js, and be used server-side without any need to compile native libraries.

When should I use WASM?

Model size and computational demand

In general, WASM is a good choice when models are smaller or you care about lower-end devices that lack WebGL support (OES_texture_float extension) or have less powerful GPUs. The chart below shows inference times (as of TensorFlow.js 1.5.2) in Chrome on a 2018 MacBook Pro for 5 of our officially supported models across the WebGL, WASM, and CPU backends:

Smaller models

Model WebGL WASM CPU Memory
BlazeFace 22.5 ms 15.6 ms 315.2 ms .4 MB
FaceMesh 19.3 ms 19.2 ms 335 ms 2.8 MB

Larger models

Model WebGL WASM CPU Memory
PoseNet 42.5 ms 173.9 ms 1514.7 ms 4.5 MB
BodyPix 77 ms 188.4 ms 2683 ms 4.6 MB
MobileNet v2 37 ms 94 ms 923.6 ms 13 MB

The table above shows that WASM is 10-30x faster than the plain JS CPU backend across models, and competitive with WebGL for smaller models like BlazeFace, which is lightweight (400KB), yet has a decent number of ops (~140). Given that WebGL programs have a fixed overhead cost per op execution, this explains why models like BlazeFace are faster on WASM.

These results will vary depending on your device. The best way to determine whether WASM is right for your application is to test it on our different backends.

Inference vs Training

To address the primary use-case for deployment of pre-trained models, the WASM backend development will prioritize inference over training support. See an up-to-date list of supported ops in WASM and let us know if your model has an unsupported op. For training models, we recommend using the Node (TensorFlow C++) backend or the WebGL backend.

CPU backend

The CPU backend, 'cpu', is the least performant backend, however it is the simplest. Operations are all implemented in vanilla JavaScript, which makes them less parallelizable. They also block the UI thread.

This backend can be very useful for testing, or on devices where WebGL is unavailable.

Flags

TensorFlow.js has a set of environment flags that are automatically evaluated and determine the best configuration in the current platform. These flags are mostly internal, but a few global flags can be controlled with public API.

  • tf.enableProdMode(): enables production mode, which will remove model validation, NaN checks, and other correctness checks in favor of performance.
  • tf.enableDebugMode(): enables debug mode, which will log to the console every operation that is executed, as well as runtime performance information like memory footprint and total kernel execution time. Note that this will greatly slow down your application, do not use this in production.