google-deepmind/sonnet
Sonnet
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 layer composition and custom module definitions. It includes utilities for parameter collection tracking, model state management, and the export of model graphs for use in non-training environments.
Features
- TensorFlow Model Development - Provides a framework for designing and building deep learning architectures specifically within the TensorFlow ecosystem.
- Composable Network Modules - Provides modular abstractions for building and composing deep learning research architectures.
- Reusable Component Definitions - Allows the creation of reusable machine learning components by subclassing base modules.
- Deep Learning Research Workflows - Streamlines the design and iteration of experimental neural network structures for research engineering.
- Distributed Training Frameworks - Provides a library for synchronizing gradients and scaling model training across multiple GPUs and compute nodes.
- Distributed Training - Enables scaling the training of large neural networks across multiple GPUs through data and model parallelism.
- Machine Learning Frameworks - Offers a modular framework for building and training machine learning models using high-level abstractions.
- Sequential Layer Containers - Supports grouping and connecting multiple neural network layers into a single sequential unit.
- Neural Network Composition - Implements methods for stacking and organizing neural network layers into sequential hierarchical structures.
- Neural Network Design Frameworks - Supports the building of complex machine learning models using composable and reusable modules.
- Neural Network Frameworks - Provides a modular codebase for defining and scaling complex, composable neural network architectures.
- Parameter Management Utilities - Includes utilities to query and manage the state and trainability of model parameters for optimizers.
- Trainable Parameter Retrieval - Maintains internal registries of trainable variables to simplify their retrieval for optimizers.
- Neural Network Module Abstractions - Provides a class-based system for encapsulating network logic and automating the tracking of internal weights and biases.
- Model Orchestrators - Functions as an orchestrator that manages parameters, state serialization, and graph exports.
- Distributed Training - Implements a system to synchronize gradients and distribute training workloads across multiple GPUs or hardware devices.
- Distributed Gradient Synchronization - Coordinates gradient updates across multiple compute nodes during distributed training to scale workloads.
- Model Parameter Management - Provides tools for tracking, saving, and loading neural network weights to preserve model state.
- Model Exporting - Includes utilities for serializing and exporting trained models for use in non-training environments.
- Model State Serialization - Ships mechanisms to serialize and reload network weights and optimizer states for checkpointing.
- Machine Learning - Library for building complex neural networks.
- Machine-Learning-Frameworks - TensorFlow-based library for building complex neural network architectures.
- Computation and Optimization - Library for composable abstractions in TensorFlow research.
