Open-source alternatives to Trfl

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

Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy.

TensorLight - A high-level framework for TensorFlow

Ludwig is a multimodal machine learning platform and low-code framework designed for building, training, and deploying neural networks. It enables the construction of models that process text, images, audio, and tabular data through a unified interface using declarative configuration files rather than custom code. The system features a specialized low-code framework for large language models, supporting supervised fine-tuning, preference alignment, and a constrained decoding tool to force structured data output via logit extraction. It also includes an automated model architecture search to i

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

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

PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array operations across both CPU and accelerator hardware. It provides a foundational infrastructure for mathematical computation and dynamic neural network construction, utilizing a tape-based automatic differentiation system that allows for flexible, non-static graph execution. The framework is designed for deep integration with Python, enabling natural usage alongside standard scientific computing ecosystems. It distinguishes itself through a comprehensive distributed training sui

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

Tensorforce: a TensorFlow library for applied reinforcement learning

Keras community contributions

Distributed Deep learning with Keras & Spark

Hyperparameter Experiments with TensorFlow and Keras

Wrapper library for text generation / language models at character and word level with RNNs in TensorFlow

Accelerated deep learning R&D

GPyTorch is a GPU-accelerated probabilistic framework and PyTorch library for implementing scalable Gaussian process models. It provides a system for Gaussian process modeling and uncertainty estimation, designed to perform efficient matrix operations on graphics hardware. The framework features a modular kernel system for constructing custom covariance functions and modeling complex data dependencies. It specifically integrates Gaussian processes with deep neural networks to create hybrid models for regression and classification. The system employs numerical linear algebra techniques, inclu

Graph Neural Networks with Keras and Tensorflow 2.

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

A simplified framework and utilities for PyTorch

Keras Hyperopt: A very simple wrapper for convenient hyperparameter optimization

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

QKeras: a quantization deep learning library for Tensorflow Keras

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

A lightweight library for PyTorch training tools and utilities

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

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