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React recently introduced an experimental profiler API. This page gives instructions on how to use this API in a production release of your app.

Table of Contents

• Profiling in production
  • Create React App
  • Webpack 4
• Troubleshooting
  • Tracing broke after an NPM update

Profiling in production

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import React, {useState} from 'react';
import {View, Text, TouchableOpacity, StyleSheet} from 'react-native';

const App = () => {
  const [count, setCount] = useState(0);
  return (
    <View style={styles.container}>
      <Text style={styles.title}>Hello from {'\n'}React Native Web!</Text>
      <TouchableOpacity
        onPress={() => setCount(count + 1)}

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{
  "scripts": {
    "build": "npm run build:es2015 && npm run build:esm && npm run build:cjs && npm run build:umd && npm run build:umd:min",
    "build:es2015": "tsc --module es2015 --target es2015 --outDir dist/es2015",
    "build:esm": "tsc --module es2015 --target es5 --outDir dist/esm",
    "build:cjs": "tsc --module commonjs --target es5 --outDir dist/cjs",
    "build:umd": "rollup dist/esm/index.js --format umd --name YourLibrary --sourceMap --output dist/umd/yourlibrary.js",
    "build:umd:min": "cd dist/umd && uglifyjs --compress --mangle --source-map --screw-ie8 --comments --o yourlibrary.min.js -- yourlibrary.js && gzip yourlibrary.min.js -c > yourlibrary.min.js.gz",
  }
}

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enum keyCodes {
  BACKSPACE: 8,
  TAB: 9,
  ENTER: 13,
  SHIFT: 16,
  CTRL: 17,
  ALT: 18,
  PAUSE: 19,
  CAPS_LOCK: 20,
  ESCAPE: 27,

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/**
 * number_format(number, decimals, decPoint, thousandsSep) in JavaScript, known from PHP.
 * It formats a number to a string with grouped thousands, with custom seperator and custom decimal point
 * @param {number} number - number to format
 * @param {number} [decimals=0] - (optional) count of decimals to show
 * @param {string} [decPoint=.] - (optional) decimal point
 * @param {string} [thousandsSep=,] - (optional) thousands seperator
 * @author Felix Leupold <felix@xiel.de>
 */
function number_format(number, decimals, decPoint, thousandsSep) {

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# This small script shows how to use AllenNLP Semantic Role Labeling (http://allennlp.org/) with SpaCy 2.0 (http://spacy.io) components and extensions
# Script installs allennlp default model
# Important: Install allennlp form source and replace the spacy requirement with spacy-nightly in the requirements.txt
# Developed for SpaCy 2.0.0a18

from allennlp.commands import DEFAULT_MODELS
from allennlp.common.file_utils import cached_path
from allennlp.service.predictors import SemanticRoleLabelerPredictor
from allennlp.models.archival import load_archive

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import threading
import time


class ThreadingExample(object):
    """ Threading example class

    The run() method will be started and it will run in the background
    until the application exits.
    """

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def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':

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class AttentionLSTM(LSTM):
    """LSTM with attention mechanism

    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

    The layer expects two inputs instead of the usual one:

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class AttentionWithContext(Layer):
    """
        Attention operation, with a context/query vector, for temporal data.
        Supports Masking.
        Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf]
        "Hierarchical Attention Networks for Document Classification"
        by using a context vector to assist the attention
        # Input shape
            3D tensor with shape: `(samples, steps, features)`.
        # Output shape