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Star history, last 90 days
Date	Stars
2026-07-24	9945
2026-07-25	9945
2026-07-28	9945
2026-07-30	9945
2026-07-31	9944
2026-08-01	9945
2026-08-02	9945
2026-08-06	9946

Star history, last 90 days

Date

Stars

2026-07-24

9945

2026-07-25

9945

2026-07-28

9945

2026-07-30

9945

2026-07-31

9944

2026-08-01

9945

2026-08-02

9945

2026-08-06

9946

![Sonnet](https://sonnet.dev/images/sonnet_logo.png) # Sonnet [**Documentation**](https://sonnet.readthedocs.io/) | [**Examples**](#examples) Sonnet is a library built on top of [TensorFlow 2](https://www.tensorflow.org/) designed to provide simple, composable abstractions for machine learning research. # Introduction Sonnet has been designed and built by researchers at DeepMind. It can be used to construct neural networks for many different purposes (un/supervised learning, reinforcement learning, ...). We find it is a successful abstraction for our organization, you might too! More specifically, Sonnet provides a simple but powerful programming model centered around a single concept: `snt.Module`. Modules can hold references to parameters, other modules and methods that apply some function on the user input. Sonnet ships with many predefined modules (e.g. `snt.Linear`, `snt.Conv2D`, `snt.BatchNorm`) and some predefined networks of modules (e.g. `snt.nets.MLP`) but users are also encouraged to build their own modules. Unlike many frameworks Sonnet is extremely unopinionated about **how** you will use your modules. Modules are designed to be self contained and entirely decoupled from one another. Sonnet does not ship with a training framework and users are encouraged to build their own or adopt those built by others. Sonnet is also designed to be simple to understand, our code is (hopefully!) clear and focussed. Where we have picked defaults (e.g. defaults for initial parameter values) we try to point out why. # Getting Started ## Examples The easiest way to try Sonnet is to use Google Colab which offers a free Python notebook attached to a GPU or TPU. - [Predicting MNIST with an MLP](https://colab.research.google.com/github/deepmind/sonnet/blob/v2/examples/mlp_on_mnist.ipynb) - [Training a Little GAN on MNIST](https://colab.research.google.com/github/deepmind/sonnet/blob/v2/examples/little_gan_on_mnist.ipynb) - [Distributed training with `snt.distribute`](https://colab.research.google.com/github/deepmind/sonnet/blob/v2/examples/distributed_cifar10.ipynb) ## Installation To get started install TensorFlow 2.0 and Sonnet 2: ```shell $ pip install tensorflow tensorflow-probability $ pip install dm-sonnet ``` You can run the following to verify things installed correctly: ```python import tensorflow as tf import sonnet as snt print("TensorFlow version {}".format(tf.__version__)) print("Sonnet version {}".format(snt.__version__)) ``` ### Using existing modules Sonnet ships with a number of built in modules that you can trivially use. For example to define an MLP we can use the `snt.Sequential` module to call a sequence of modules, passing the output of a given module as the input for the next module. We can use `snt.Linear` and `tf.nn.relu` to actually define our computation: ```python mlp = snt.Sequential([ snt.Linear(1024), tf.nn.relu, snt.Linear(10), ]) ``` To use our module we need to "call" it. The `Sequential` module (and most modules) define a `__call__` method that means you can call them by name: ```python logits = mlp(tf.random.normal([batch_size, input_size])) ``` It is also very common to request all the parameters for your module. Most modules in Sonnet create their parameters the first time they are called with some input (since in most cases the shape of the parameters is a function of the input). Sonnet modules provide two properties for accessing parameters. The `variables` property returns **all** `tf.Variable`s that are referenced by the given module: ```python all_variables = mlp.variables ``` It is worth noting that `tf.Variable`s are not just used for parameters of your model. For example they are used to hold state in metrics used in `snt.BatchNorm`. In most cases users retrieve the module variables to pass them to an optimizer to be updated. In this case non-trainable variables should typically not be in that list as they are updated via a different mechanism. TensorFlo