• Stars
    star
    27,471
  • Rank 650 (Top 0.02 %)
  • Language
    Python
  • License
    Apache License 2.0
  • Created over 5 years ago
  • Updated 4 days ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
logo

JAX: Autograd and XLA

Continuous integration PyPI version

Quickstart | Transformations | Install guide | Neural net libraries | Change logs | Reference docs

What is JAX?

JAX is Autograd and XLA, brought together for high-performance machine learning research.

With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy functions. It can differentiate through loops, branches, recursion, and closures, and it can take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation) via grad as well as forward-mode differentiation, and the two can be composed arbitrarily to any order.

What’s new is that JAX uses XLA to compile and run your NumPy programs on GPUs and TPUs. Compilation happens under the hood by default, with library calls getting just-in-time compiled and executed. But JAX also lets you just-in-time compile your own Python functions into XLA-optimized kernels using a one-function API, jit. Compilation and automatic differentiation can be composed arbitrarily, so you can express sophisticated algorithms and get maximal performance without leaving Python. You can even program multiple GPUs or TPU cores at once using pmap, and differentiate through the whole thing.

Dig a little deeper, and you'll see that JAX is really an extensible system for composable function transformations. Both grad and jit are instances of such transformations. Others are vmap for automatic vectorization and pmap for single-program multiple-data (SPMD) parallel programming of multiple accelerators, with more to come.

This is a research project, not an official Google product. Expect bugs and sharp edges. Please help by trying it out, reporting bugs, and letting us know what you think!

import jax.numpy as jnp
from jax import grad, jit, vmap

def predict(params, inputs):
  for W, b in params:
    outputs = jnp.dot(inputs, W) + b
    inputs = jnp.tanh(outputs)  # inputs to the next layer
  return outputs                # no activation on last layer

def loss(params, inputs, targets):
  preds = predict(params, inputs)
  return jnp.sum((preds - targets)**2)

grad_loss = jit(grad(loss))  # compiled gradient evaluation function
perex_grads = jit(vmap(grad_loss, in_axes=(None, 0, 0)))  # fast per-example grads

Contents

Quickstart: Colab in the Cloud

Jump right in using a notebook in your browser, connected to a Google Cloud GPU. Here are some starter notebooks:

JAX now runs on Cloud TPUs. To try out the preview, see the Cloud TPU Colabs.

For a deeper dive into JAX:

You can also take a look at the mini-libraries in jax.example_libraries, like stax for building neural networks and optimizers for first-order stochastic optimization, or the examples.

Transformations

At its core, JAX is an extensible system for transforming numerical functions. Here are four transformations of primary interest: grad, jit, vmap, and pmap.

Automatic differentiation with grad

JAX has roughly the same API as Autograd. The most popular function is grad for reverse-mode gradients:

from jax import grad
import jax.numpy as jnp

def tanh(x):  # Define a function
  y = jnp.exp(-2.0 * x)
  return (1.0 - y) / (1.0 + y)

grad_tanh = grad(tanh)  # Obtain its gradient function
print(grad_tanh(1.0))   # Evaluate it at x = 1.0
# prints 0.4199743

You can differentiate to any order with grad.

print(grad(grad(grad(tanh)))(1.0))
# prints 0.62162673

For more advanced autodiff, you can use jax.vjp for reverse-mode vector-Jacobian products and jax.jvp for forward-mode Jacobian-vector products. The two can be composed arbitrarily with one another, and with other JAX transformations. Here's one way to compose those to make a function that efficiently computes full Hessian matrices:

from jax import jit, jacfwd, jacrev

def hessian(fun):
  return jit(jacfwd(jacrev(fun)))

As with Autograd, you're free to use differentiation with Python control structures:

def abs_val(x):
  if x > 0:
    return x
  else:
    return -x

abs_val_grad = grad(abs_val)
print(abs_val_grad(1.0))   # prints 1.0
print(abs_val_grad(-1.0))  # prints -1.0 (abs_val is re-evaluated)

See the reference docs on automatic differentiation and the JAX Autodiff Cookbook for more.

Compilation with jit

You can use XLA to compile your functions end-to-end with jit, used either as an @jit decorator or as a higher-order function.

import jax.numpy as jnp
from jax import jit

def slow_f(x):
  # Element-wise ops see a large benefit from fusion
  return x * x + x * 2.0

x = jnp.ones((5000, 5000))
fast_f = jit(slow_f)
%timeit -n10 -r3 fast_f(x)  # ~ 4.5 ms / loop on Titan X
%timeit -n10 -r3 slow_f(x)  # ~ 14.5 ms / loop (also on GPU via JAX)

You can mix jit and grad and any other JAX transformation however you like.

Using jit puts constraints on the kind of Python control flow the function can use; see the Gotchas Notebook for more.

Auto-vectorization with vmap

vmap is the vectorizing map. It has the familiar semantics of mapping a function along array axes, but instead of keeping the loop on the outside, it pushes the loop down into a function’s primitive operations for better performance.

Using vmap can save you from having to carry around batch dimensions in your code. For example, consider this simple unbatched neural network prediction function:

def predict(params, input_vec):
  assert input_vec.ndim == 1
  activations = input_vec
  for W, b in params:
    outputs = jnp.dot(W, activations) + b  # `activations` on the right-hand side!
    activations = jnp.tanh(outputs)        # inputs to the next layer
  return outputs                           # no activation on last layer

We often instead write jnp.dot(activations, W) to allow for a batch dimension on the left side of activations, but we’ve written this particular prediction function to apply only to single input vectors. If we wanted to apply this function to a batch of inputs at once, semantically we could just write

from functools import partial
predictions = jnp.stack(list(map(partial(predict, params), input_batch)))

But pushing one example through the network at a time would be slow! It’s better to vectorize the computation, so that at every layer we’re doing matrix-matrix multiplication rather than matrix-vector multiplication.

The vmap function does that transformation for us. That is, if we write

from jax import vmap
predictions = vmap(partial(predict, params))(input_batch)
# or, alternatively
predictions = vmap(predict, in_axes=(None, 0))(params, input_batch)

then the vmap function will push the outer loop inside the function, and our machine will end up executing matrix-matrix multiplications exactly as if we’d done the batching by hand.

It’s easy enough to manually batch a simple neural network without vmap, but in other cases manual vectorization can be impractical or impossible. Take the problem of efficiently computing per-example gradients: that is, for a fixed set of parameters, we want to compute the gradient of our loss function evaluated separately at each example in a batch. With vmap, it’s easy:

per_example_gradients = vmap(partial(grad(loss), params))(inputs, targets)

Of course, vmap can be arbitrarily composed with jit, grad, and any other JAX transformation! We use vmap with both forward- and reverse-mode automatic differentiation for fast Jacobian and Hessian matrix calculations in jax.jacfwd, jax.jacrev, and jax.hessian.

SPMD programming with pmap

For parallel programming of multiple accelerators, like multiple GPUs, use pmap. With pmap you write single-program multiple-data (SPMD) programs, including fast parallel collective communication operations. Applying pmap will mean that the function you write is compiled by XLA (similarly to jit), then replicated and executed in parallel across devices.

Here's an example on an 8-GPU machine:

from jax import random, pmap
import jax.numpy as jnp

# Create 8 random 5000 x 6000 matrices, one per GPU
keys = random.split(random.PRNGKey(0), 8)
mats = pmap(lambda key: random.normal(key, (5000, 6000)))(keys)

# Run a local matmul on each device in parallel (no data transfer)
result = pmap(lambda x: jnp.dot(x, x.T))(mats)  # result.shape is (8, 5000, 5000)

# Compute the mean on each device in parallel and print the result
print(pmap(jnp.mean)(result))
# prints [1.1566595 1.1805978 ... 1.2321935 1.2015157]

In addition to expressing pure maps, you can use fast collective communication operations between devices:

from functools import partial
from jax import lax

@partial(pmap, axis_name='i')
def normalize(x):
  return x / lax.psum(x, 'i')

print(normalize(jnp.arange(4.)))
# prints [0.         0.16666667 0.33333334 0.5       ]

You can even nest pmap functions for more sophisticated communication patterns.

It all composes, so you're free to differentiate through parallel computations:

from jax import grad

@pmap
def f(x):
  y = jnp.sin(x)
  @pmap
  def g(z):
    return jnp.cos(z) * jnp.tan(y.sum()) * jnp.tanh(x).sum()
  return grad(lambda w: jnp.sum(g(w)))(x)

print(f(x))
# [[ 0.        , -0.7170853 ],
#  [-3.1085174 , -0.4824318 ],
#  [10.366636  , 13.135289  ],
#  [ 0.22163185, -0.52112055]]

print(grad(lambda x: jnp.sum(f(x)))(x))
# [[ -3.2369726,  -1.6356447],
#  [  4.7572474,  11.606951 ],
#  [-98.524414 ,  42.76499  ],
#  [ -1.6007166,  -1.2568436]]

When reverse-mode differentiating a pmap function (e.g. with grad), the backward pass of the computation is parallelized just like the forward pass.

See the SPMD Cookbook and the SPMD MNIST classifier from scratch example for more.

Current gotchas

For a more thorough survey of current gotchas, with examples and explanations, we highly recommend reading the Gotchas Notebook. Some standouts:

  1. JAX transformations only work on pure functions, which don't have side-effects and respect referential transparency (i.e. object identity testing with is isn't preserved). If you use a JAX transformation on an impure Python function, you might see an error like Exception: Can't lift Traced... or Exception: Different traces at same level.
  2. In-place mutating updates of arrays, like x[i] += y, aren't supported, but there are functional alternatives. Under a jit, those functional alternatives will reuse buffers in-place automatically.
  3. Random numbers are different, but for good reasons.
  4. If you're looking for convolution operators, they're in the jax.lax package.
  5. JAX enforces single-precision (32-bit, e.g. float32) values by default, and to enable double-precision (64-bit, e.g. float64) one needs to set the jax_enable_x64 variable at startup (or set the environment variable JAX_ENABLE_X64=True). On TPU, JAX uses 32-bit values by default for everything except internal temporary variables in 'matmul-like' operations, such as jax.numpy.dot and lax.conv. Those ops have a precision parameter which can be used to simulate true 32-bit, with a cost of possibly slower runtime.
  6. Some of NumPy's dtype promotion semantics involving a mix of Python scalars and NumPy types aren't preserved, namely np.add(1, np.array([2], np.float32)).dtype is float64 rather than float32.
  7. Some transformations, like jit, constrain how you can use Python control flow. You'll always get loud errors if something goes wrong. You might have to use jit's static_argnums parameter, structured control flow primitives like lax.scan, or just use jit on smaller subfunctions.

Installation

JAX is written in pure Python, but it depends on XLA, which needs to be installed as the jaxlib package. Use the following instructions to install a binary package with pip or conda, or to build JAX from source.

We support installing or building jaxlib on Linux (Ubuntu 16.04 or later) and macOS (10.12 or later) platforms.

Windows users can use JAX on CPU and GPU via the Windows Subsystem for Linux. In addition, there is some initial community-driven native Windows support, but since it is still somewhat immature, there are no official binary releases and it must be built from source for Windows. For an unofficial discussion of native Windows builds, see also the Issue #5795 thread.

pip installation: CPU

To install a CPU-only version of JAX, which might be useful for doing local development on a laptop, you can run

pip install --upgrade pip
pip install --upgrade "jax[cpu]"

On Linux, it is often necessary to first update pip to a version that supports manylinux2014 wheels. Also note that for Linux, we currently release wheels for x86_64 architectures only, other architectures require building from source. Trying to pip install with other Linux architectures may lead to jaxlib not being installed alongside jax, although jax may successfully install (but fail at runtime). These pip installations do not work with Windows, and may fail silently; see above.

pip installation: GPU (CUDA, installed via pip, easier)

There are two ways to install JAX with NVIDIA GPU support: using CUDA and CUDNN installed from pip wheels, and using a self-installed CUDA/CUDNN. We recommend installing CUDA and CUDNN using the pip wheels, since it is much easier!

JAX supports NVIDIA GPUs that have SM version 5.2 (Maxwell) or newer. Note that Kepler-series GPUs are no longer supported by JAX since NVIDIA has dropped support for Kepler in its software.

You must first install the NVIDIA driver. We recommend installing the newest driver available from NVIDIA, but the driver must be version >= 525.60.13 for CUDA 12 and >= 450.80.02 for CUDA 11 on Linux. If you need to use an newer CUDA toolkit with an older driver, for example on a cluster where you cannot update the NVIDIA driver easily, you may be able to use the CUDA forward compatibility packages that NVIDIA provides for this purpose.

pip install --upgrade pip

# CUDA 12 installation
# Note: wheels only available on linux.
pip install --upgrade "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

# CUDA 11 installation
# Note: wheels only available on linux.
pip install --upgrade "jax[cuda11_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

pip installation: GPU (CUDA, installed locally, harder)

If you prefer to use a preinstalled copy of CUDA, you must first install CUDA and CuDNN.

JAX provides pre-built CUDA-compatible wheels for Linux x86_64 only. Other combinations of operating system and architecture are possible, but require building from source.

You should use an NVIDIA driver version that is at least as new as your CUDA toolkit's corresponding driver version. If you need to use an newer CUDA toolkit with an older driver, for example on a cluster where you cannot update the NVIDIA driver easily, you may be able to use the CUDA forward compatibility packages that NVIDIA provides for this purpose.

JAX currently ships two CUDA wheel variants:

  • CUDA 12.0 and CuDNN 8.9.
  • CUDA 11.8 and CuDNN 8.6.

You may use a JAX wheel provided the major version of your CUDA and CuDNN installation matches, and the minor version is at least as new as the version JAX expects. For example, you would be able to use the CUDA 12.0 wheel with CUDA 12.1 and CuDNN 8.9.

Your CUDA installation must also be new enough to support your GPU. If you have an Ada Lovelace (e.g., RTX 4080) or Hopper (e.g., H100) GPU, you must use CUDA 11.8 or newer.

To install, run

pip install --upgrade pip

# Installs the wheel compatible with CUDA 12 and cuDNN 8.8 or newer.
# Note: wheels only available on linux.
pip install --upgrade "jax[cuda12_local]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

# Installs the wheel compatible with CUDA 11 and cuDNN 8.6 or newer.
# Note: wheels only available on linux.
pip install --upgrade "jax[cuda11_local]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

These pip installations do not work with Windows, and may fail silently; see above.

You can find your CUDA version with the command:

nvcc --version

Some GPU functionality expects the CUDA installation to be at /usr/local/cuda-X.X, where X.X should be replaced with the CUDA version number (e.g. cuda-11.8). If CUDA is installed elsewhere on your system, you can either create a symlink:

sudo ln -s /path/to/cuda /usr/local/cuda-X.X

Please let us know on the issue tracker if you run into any errors or problems with the prebuilt wheels.

pip installation: Google Cloud TPU

JAX also provides pre-built wheels for Google Cloud TPU. To install JAX along with appropriate versions of jaxlib and libtpu, you can run the following in your cloud TPU VM:

pip install jax[tpu] -f https://storage.googleapis.com/jax-releases/libtpu_releases.html

pip installation: Colab TPU

Colab TPU runtimes use an older TPU architecture than Cloud TPU VMs, so the installation instructions differ. The Colab TPU runtime comes with JAX pre-installed, but before importing JAX you must run the following code to initialize the TPU:

import jax.tools.colab_tpu
jax.tools.colab_tpu.setup_tpu()

Note that Colab TPU runtimes are not compatible with JAX version 0.4.0 or newer. If you need to re-install JAX on a Colab TPU runtime, you can use the following command:

!pip install jax<=0.3.25 jaxlib<=0.3.25

Conda installation

There is a community-supported Conda build of jax. To install using conda, simply run

conda install jax -c conda-forge

To install on a machine with an NVIDIA GPU, run

conda install jaxlib=*=*cuda* jax cuda-nvcc -c conda-forge -c nvidia

Note the cudatoolkit distributed by conda-forge is missing ptxas, which JAX requires. You must therefore either install the cuda-nvcc package from the nvidia channel, or install CUDA on your machine separately so that ptxas is in your path. The channel order above is important (conda-forge before nvidia).

If you would like to override which release of CUDA is used by JAX, or to install the CUDA build on a machine without GPUs, follow the instructions in the Tips & tricks section of the conda-forge website.

See the conda-forge jaxlib and jax repositories for more details.

Building JAX from source

See Building JAX from source.

Neural network libraries

Multiple Google research groups develop and share libraries for training neural networks in JAX. If you want a fully featured library for neural network training with examples and how-to guides, try Flax.

In addition, DeepMind has open-sourced an ecosystem of libraries around JAX including Haiku for neural network modules, Optax for gradient processing and optimization, RLax for RL algorithms, and chex for reliable code and testing. (Watch the NeurIPS 2020 JAX Ecosystem at DeepMind talk here)

Citing JAX

To cite this repository:

@software{jax2018github,
  author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and Chris Leary and Dougal Maclaurin and George Necula and Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao Zhang},
  title = {{JAX}: composable transformations of {P}ython+{N}um{P}y programs},
  url = {http://github.com/google/jax},
  version = {0.3.13},
  year = {2018},
}

In the above bibtex entry, names are in alphabetical order, the version number is intended to be that from jax/version.py, and the year corresponds to the project's open-source release.

A nascent version of JAX, supporting only automatic differentiation and compilation to XLA, was described in a paper that appeared at SysML 2018. We're currently working on covering JAX's ideas and capabilities in a more comprehensive and up-to-date paper.

Reference documentation

For details about the JAX API, see the reference documentation.

For getting started as a JAX developer, see the developer documentation.

More Repositories

1

material-design-icons

Material Design icons by Google
49,605
star
2

guava

Google core libraries for Java
Java
48,313
star
3

zx

A tool for writing better scripts
JavaScript
37,928
star
4

styleguide

Style guides for Google-originated open-source projects
HTML
36,377
star
5

leveldb

LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.
C++
33,564
star
6

material-design-lite

Material Design Components in HTML/CSS/JS
HTML
32,276
star
7

googletest

GoogleTest - Google Testing and Mocking Framework
C++
32,215
star
8

python-fire

Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
Python
26,112
star
9

mediapipe

Cross-platform, customizable ML solutions for live and streaming media.
C++
25,107
star
10

comprehensive-rust

This is the Rust course used by the Android team at Google. It provides you the material to quickly teach Rust.
Rust
24,867
star
11

gson

A Java serialization/deserialization library to convert Java Objects into JSON and back
Java
22,856
star
12

flatbuffers

FlatBuffers: Memory Efficient Serialization Library
C++
21,883
star
13

iosched

The Google I/O Android App
Kotlin
21,801
star
14

ExoPlayer

An extensible media player for Android
Java
21,309
star
15

eng-practices

Google's Engineering Practices documentation
19,715
star
16

web-starter-kit

Web Starter Kit - a workflow for multi-device websites
HTML
18,426
star
17

flexbox-layout

Flexbox for Android
Kotlin
18,141
star
18

fonts

Font files available from Google Fonts, and a public issue tracker for all things Google Fonts
HTML
17,389
star
19

filament

Filament is a real-time physically based rendering engine for Android, iOS, Windows, Linux, macOS, and WebGL2
C++
16,946
star
20

cadvisor

Analyzes resource usage and performance characteristics of running containers.
Go
16,184
star
21

libphonenumber

Google's common Java, C++ and JavaScript library for parsing, formatting, and validating international phone numbers.
C++
15,728
star
22

gvisor

Application Kernel for Containers
Go
14,646
star
23

WebFundamentals

Former git repo for WebFundamentals on developers.google.com
JavaScript
13,848
star
24

yapf

A formatter for Python files
Python
13,560
star
25

tink

Tink is a multi-language, cross-platform, open source library that provides cryptographic APIs that are secure, easy to use correctly, and hard(er) to misuse.
Java
13,318
star
26

deepdream

13,212
star
27

brotli

Brotli compression format
TypeScript
12,921
star
28

guetzli

Perceptual JPEG encoder
C++
12,863
star
29

guice

Guice (pronounced 'juice') is a lightweight dependency injection framework for Java 8 and above, brought to you by Google.
Java
12,324
star
30

wire

Compile-time Dependency Injection for Go
Go
12,131
star
31

blockly

The web-based visual programming editor.
TypeScript
12,033
star
32

grumpy

Grumpy is a Python to Go source code transcompiler and runtime.
Go
10,464
star
33

sanitizers

AddressSanitizer, ThreadSanitizer, MemorySanitizer
C
10,437
star
34

dopamine

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
Jupyter Notebook
10,319
star
35

or-tools

Google's Operations Research tools:
C++
10,299
star
36

auto

A collection of source code generators for Java.
Java
10,234
star
37

go-github

Go library for accessing the GitHub v3 API
Go
9,941
star
38

oss-fuzz

OSS-Fuzz - continuous fuzzing for open source software.
Shell
9,324
star
39

go-cloud

The Go Cloud Development Kit (Go CDK): A library and tools for open cloud development in Go.
Go
9,314
star
40

sentencepiece

Unsupervised text tokenizer for Neural Network-based text generation.
C++
8,657
star
41

re2

RE2 is a fast, safe, thread-friendly alternative to backtracking regular expression engines like those used in PCRE, Perl, and Python. It is a C++ library.
C++
8,190
star
42

traceur-compiler

Traceur is a JavaScript.next-to-JavaScript-of-today compiler
JavaScript
8,178
star
43

tsunami-security-scanner

Tsunami is a general purpose network security scanner with an extensible plugin system for detecting high severity vulnerabilities with high confidence.
Java
8,036
star
44

trax

Trax — Deep Learning with Clear Code and Speed
Python
7,917
star
45

skia

Skia is a complete 2D graphic library for drawing Text, Geometries, and Images.
C++
7,874
star
46

benchmark

A microbenchmark support library
C++
7,812
star
47

android-classyshark

Android and Java bytecode viewer
Java
7,440
star
48

pprof

pprof is a tool for visualization and analysis of profiling data
Go
7,235
star
49

agera

Reactive Programming for Android
Java
7,227
star
50

closure-compiler

A JavaScript checker and optimizer.
Java
7,195
star
51

magika

Detect file content types with deep learning
Python
7,171
star
52

accompanist

A collection of extension libraries for Jetpack Compose
Kotlin
7,157
star
53

flutter-desktop-embedding

Experimental plugins for Flutter for Desktop
C++
7,108
star
54

diff-match-patch

Diff Match Patch is a high-performance library in multiple languages that manipulates plain text.
Python
6,918
star
55

lovefield

Lovefield is a relational database for web apps. Written in JavaScript, works cross-browser. Provides SQL-like APIs that are fast, safe, and easy to use.
JavaScript
6,847
star
56

glog

C++ implementation of the Google logging module
C++
6,748
star
57

jsonnet

Jsonnet - The data templating language
Jsonnet
6,711
star
58

latexify_py

A library to generate LaTeX expression from Python code.
Python
6,708
star
59

error-prone

Catch common Java mistakes as compile-time errors
Java
6,690
star
60

model-viewer

Easily display interactive 3D models on the web and in AR!
TypeScript
6,390
star
61

gops

A tool to list and diagnose Go processes currently running on your system
Go
6,375
star
62

automl

Google Brain AutoML
Jupyter Notebook
6,113
star
63

gopacket

Provides packet processing capabilities for Go
Go
6,067
star
64

physical-web

The Physical Web: walk up and use anything
Java
6,017
star
65

j2objc

A Java to iOS Objective-C translation tool and runtime.
Java
5,975
star
66

grafika

Grafika test app
Java
5,964
star
67

draco

Draco is a library for compressing and decompressing 3D geometric meshes and point clouds. It is intended to improve the storage and transmission of 3D graphics.
C++
5,947
star
68

snappy

A fast compressor/decompressor
C++
5,940
star
69

ios-webkit-debug-proxy

A DevTools proxy (Chrome Remote Debugging Protocol) for iOS devices (Safari Remote Web Inspector).
C
5,848
star
70

osv-scanner

Vulnerability scanner written in Go which uses the data provided by https://osv.dev
Go
5,763
star
71

seesaw

Seesaw v2 is a Linux Virtual Server (LVS) based load balancing platform.
Go
5,586
star
72

seq2seq

A general-purpose encoder-decoder framework for Tensorflow
Python
5,577
star
73

EarlGrey

🍵 iOS UI Automation Test Framework
Objective-C
5,570
star
74

google-java-format

Reformats Java source code to comply with Google Java Style.
Java
5,366
star
75

flax

Flax is a neural network library for JAX that is designed for flexibility.
Python
5,358
star
76

wireit

Wireit upgrades your npm/pnpm/yarn scripts to make them smarter and more efficient.
TypeScript
5,280
star
77

battery-historian

Battery Historian is a tool to analyze battery consumers using Android "bugreport" files.
Go
5,249
star
78

clusterfuzz

Scalable fuzzing infrastructure.
Python
5,170
star
79

bbr

5,156
star
80

gumbo-parser

An HTML5 parsing library in pure C99
HTML
5,141
star
81

git-appraise

Distributed code review system for Git repos
Go
5,090
star
82

google-authenticator

Open source version of Google Authenticator (except the Android app)
Java
5,077
star
83

gemma.cpp

lightweight, standalone C++ inference engine for Google's Gemma models.
C++
5,076
star
84

syzkaller

syzkaller is an unsupervised coverage-guided kernel fuzzer
Go
5,063
star
85

uuid

Go package for UUIDs based on RFC 4122 and DCE 1.1: Authentication and Security Services.
Go
4,942
star
86

gts

☂️ TypeScript style guide, formatter, and linter.
TypeScript
4,890
star
87

closure-library

Google's common JavaScript library
JavaScript
4,832
star
88

cameraview

[DEPRECATED] Easily integrate Camera features into your Android app
Java
4,734
star
89

grr

GRR Rapid Response: remote live forensics for incident response
Python
4,627
star
90

liquidfun

2D physics engine for games
C++
4,559
star
91

pytype

A static type analyzer for Python code
Python
4,454
star
92

gxui

An experimental Go cross platform UI library.
Go
4,450
star
93

bloaty

Bloaty: a size profiler for binaries
C++
4,386
star
94

clasp

🔗 Command Line Apps Script Projects
TypeScript
4,336
star
95

ko

Build and deploy Go applications on Kubernetes
Go
4,329
star
96

santa

A binary authorization and monitoring system for macOS
Objective-C
4,288
star
97

google-ctf

Google CTF
Go
4,237
star
98

tamperchrome

Tamper Dev is an extension that allows you to intercept and edit HTTP/HTTPS requests and responses as they happen without the need of a proxy. Works across all operating systems (including Chrome OS).
TypeScript
4,137
star
99

end-to-end

End-To-End is a crypto library to encrypt, decrypt, digital sign, and verify signed messages (implementing OpenPGP)
JavaScript
4,126
star
100

orbit

C/C++ Performance Profiler
C++
3,981
star