Open-source alternatives to Tfdeploy

TensorFlow ROCm port

Data manipulation and transformation for audio signal processing, powered by PyTorch

Deep learning with dynamic computation graphs in TensorFlow

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

QKeras: a quantization deep learning library for Tensorflow Keras

Ignite is a high-level training framework for PyTorch neural networks that serves as a training engine and deep learning lifecycle manager. It provides a structured system for organizing and automating training and evaluation loops, managing data iterators and triggering event handlers at specific milestones during the model training process. The project distinguishes itself through a comprehensive suite of tools for distributed training and model evaluation. It includes utilities for synchronizing gradients and coordinating collective communication across multiple GPUs or nodes, as well as a

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 g

Keras Hyperopt: A very simple wrapper for convenient hyperparameter optimization

Accelerated deep learning R&D

Distributed Deep learning with Keras & Spark

Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.

Models, data loaders and abstractions for language processing, powered by PyTorch

A lightweight library for PyTorch training tools and utilities

A simplified framework and utilities for PyTorch

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

keras-rl is a reinforcement learning library that enables the training of neural agents using Keras. It serves as a framework for implementing deep reinforcement learning agents that interact with simulated environments to discover optimal behaviors and maximize cumulative rewards. The library provides a system for configuring, training, and managing neural network agents. It handles the interaction loop between agents and environments, allowing models to learn through direct experience and gradient-based optimization. The framework includes capabilities for model weight management, allowing

This project is a comprehensive computer vision library for the PyTorch ecosystem, providing a standardized collection of neural network architectures, datasets, and high-performance transformation utilities. It serves as a foundational framework for building, training, and deploying deep learning models, offering a centralized model registry that allows developers to instantiate architectures with pre-trained weights for tasks such as image classification, object detection, and semantic segmentation. The library distinguishes itself through its modular approach to data and compute management

TensorFlow Reinforcement Learning

AI Infra / AI Orchestration / AI Control Plane

Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch

Graph Neural Networks with Keras and Tensorflow 2.

NeuPy is a Tensorflow based python library for prototyping and building neural networks

Keras is a high-level deep learning framework designed for constructing and training neural networks through the composition of modular, functional layers. It serves as a comprehensive modeling toolkit that provides standardized procedures for defining, evaluating, and deploying complex architectures. By utilizing a directed acyclic graph approach, the framework allows users to build intricate models with multiple inputs, outputs, and shared layers, ensuring consistent numerical execution through functional state management. The project distinguishes itself as a multi-backend machine learning

Hyperparameter Experiments with TensorFlow and Keras

Pyro is a deep probabilistic programming library and differentiable probabilistic modeler designed for Bayesian inference. It functions as a probabilistic programming language that allows for the construction of complex graphical models using PyTorch tensors and automatic differentiation. The framework enables the definition of universal probabilistic models as standard Python functions. It integrates deep learning with probabilistic modeling to compute posterior distributions and estimate latent variables through gradient-based optimization and algorithmic solvers. The system provides a pro