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deepmind avatar

deepmind/sonnet

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Sonnet

Sonnet is a modular machine learning framework and TensorFlow library used for building, training, and managing deep learning models. It functions as a system for composing neural networks from reusable modules and layers that encapsulate their own parameters and internal states.

The project provides specialized tools for distributed model training, enabling the synchronization of gradients across multiple hardware devices. It also serves as a model state management system, allowing for the persistence of neural network weights and the export of portable models that separate the computation graph from the learned weights.

The framework covers a broad range of development capabilities, including parameter management for optimization processes and the construction of computation graphs for hardware acceleration.

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Features

  • Module Composition - Implements a system for building complex neural networks by composing reusable modules that encapsulate state and parameters.
  • Distributed Training - Implements data and model parallelism to train large neural networks across multiple devices.
  • Distributed Training Utilities - Provides libraries for partitioning model states and gradients across multiple hardware devices.
  • Distributed Gradient Synchronization - Provides mechanisms for synchronizing gradients across multiple hardware devices during distributed training.
  • Machine Learning Frameworks - Functions as a library for building and training machine learning models through modular composition.
  • Neural Network Building Blocks - Provides modular building blocks and layers used to construct complex neural network architectures.
  • Neural Network Modules - Provides encapsulated model components for managing parameters and forward propagation logic.
  • TensorFlow Model Development - Provides a framework for designing and building deep learning models specifically using the TensorFlow ecosystem.
  • Tensor Computation Graphs - Constructs neural networks as tensor computation graphs optimized for hardware acceleration via TensorFlow.
  • Model Exporting - Includes utilities for serializing and exporting trained models for deployment in external environments.
  • Parameter Management Utilities - Offers tools for querying and managing the state and trainability of model parameters.
  • Trainable Parameter Retrieval - Allows explicit collection of all trainable variables from a hierarchy of modules to be passed into an external optimizer.
  • Portable Model Formats - Exports computation graphs and weights into framework-agnostic formats for deployment without original source code.
  • Model State Persistence - Implements tools for saving and versioning the state of optimized AI modules to preserve progress.
  • Model State Serialization - Provides serialization of neural network weights and optimizer states for checkpointing and restoration.
  • Model State Management - Uses modular hierarchies to encapsulate and manage the internal training weights and parameters of ML models.
  • State Persistence - Provides serialization and checkpointing of internal network layer values to maintain progress between sessions.
  • Deep Learning - Neural network library built on top of computation graphs.
  • Deep Learning Ecosystems - Neural network library built on TensorFlow.
  • Deep Learning Frameworks - Neural network library built on top of TensorFlow.
  • Machine Learning Frameworks - Neural network library built on top of TensorFlow.
  • Frameworks and Libraries - Library for building complex neural networks.
9,920 stars·1,306 forks·Python·Apache-2.0·30 views

Star history

Star history chart for deepmind/sonnetStar history chart for deepmind/sonnet

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does deepmind/sonnet do?

Sonnet is a modular machine learning framework and TensorFlow library used for building, training, and managing deep learning models. It functions as a system for composing neural networks from reusable modules and layers that encapsulate their own parameters and internal states.

What are the main features of deepmind/sonnet?

The main features of deepmind/sonnet are: Module Composition, Distributed Training, Distributed Training Utilities, Distributed Gradient Synchronization, Machine Learning Frameworks, Neural Network Building Blocks, Neural Network Modules, TensorFlow Model Development.

Which projects share features with deepmind/sonnet?

Projects with overlapping indexed features include: google-deepmind/sonnet — Sonnet is a modular machine learning framework and TensorFlow neural network library designed for building composable… google/flax — Flax is a deep learning framework and JAX neural network library designed for building complex machine learning… tflearn/tflearn — tflearn is a deep learning framework and high-level API wrapper for TensorFlow. It provides a toolkit for designing… apache/mxnet — This project is a deep learning framework designed for constructing, training, and deploying neural networks across… keras-team/keras — Keras is a high-level deep learning framework designed for constructing and training neural networks through the… tensorpack/tensorpack — Tensorpack is a high-level TensorFlow neural network framework and research library designed for building and training…

Projects sharing features with Sonnet

These projects share indexed features with Sonnet. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • google-deepmind/sonnetgoogle-deepmind avatar

    google-deepmind/sonnet

    9,920View on GitHub↗

    Sonnet is a modular machine learning framework and TensorFlow neural network library designed for building composable deep learning architectures. It functions as a model orchestrator that manages parameters, state serialization, and graph exports during the training process. The framework provides a distributed training system to synchronize gradients and spread workloads across multiple GPUs or hardware devices. It enables the design of reusable research components through high-level abstractions and subclassing. The library covers neural network architecture design through sequential laye

    Pythonartificial-intelligencedeep-learningmachine-learning
    View on GitHub↗9,920
  • google/flaxgoogle avatar

    google/flax

    7,238View on GitHub↗

    Flax is a deep learning framework and JAX neural network library designed for building complex machine learning models. It functions as a distributed training library and model state manager, providing a toolkit for defining flexible neural network architectures and scaling their training across multiple hardware devices. The project is characterized by a design that separates network logic from parameter values to remain compatible with pure functions. It uses hierarchical module composition to organize networks as trees of nested modules and employs a reference-based state management system

    Jupyter Notebook
    View on GitHub↗7,238
  • tflearn/tflearntflearn avatar

    tflearn/tflearn

    9,565View on GitHub↗

    tflearn is a deep learning framework and high-level API wrapper for TensorFlow. It provides a toolkit for designing neural network architectures and a system for executing training loops and optimizing model weights across CPUs and GPUs. The project simplifies the process of building and training models through a modular interface and a high-level API for prototyping. It includes specialized utilities for deep learning visualization, allowing for the generation of graphical diagrams to analyze network structures, weights, gradients, and activations. The framework covers a broad range of capa

    Pythondata-sciencedeep-learningmachine-learning
    View on GitHub↗9,565
  • apache/mxnetapache avatar

    apache/mxnet

    20,829View on GitHub↗

    This project is a deep learning framework designed for constructing, training, and deploying neural networks across diverse hardware environments. It functions as a high-performance tensor computation library that provides both imperative and symbolic programming interfaces, allowing developers to balance flexible, step-by-step model building with the efficiency of compiled computation graphs. The framework distinguishes itself through a hybrid execution engine that integrates declarative graph compilation with imperative runtime logic. It supports scalable, distributed training across multip

    C++mxnet
    View on GitHub↗20,829
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