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MLX provides an array computing framework designed for Apple silicon devices.

The MegEngine framework supports deep learning and automatic differentiation, according to its repository description.

QKeras provides quantization tools for deep learning with TensorFlow Keras.

The linfa project provides a machine learning framework written in Rust.

FATE supports federated learning through an industrial grade framework.

Sinabs supports fast training of spiking neural networks with PyTorch and inference on neuromorphic hardware.

PyTorch Ignite provides a high level library for flexibly training and evaluating neural networks.

Mosec serves machine learning models with dynamic batching and CPU or GPU pipelines for inference workloads.

The fuku-ml project provides a machine learning library described as simple to use.

Train and run models with OneFlow, a scalable deep learning framework for distributed machine learning workloads.

ClearML combines experiment and data management, pipelines, scheduling, orchestration, and model serving for AI workloads.

ClearML manages AI experiments, data, pipelines, orchestration, scheduling, and model serving in one MLOps platform.

BentoML helps build and serve AI apps and models through inference APIs, job queues, and multi-model pipelines.

Cascade supports MLOps workflows for smaller projects without a full platform.

ModelFox supports training, deployment, and monitoring for machine learning models.

List of awesome compiler projects and papers for tensor computation and deep learning.

DJL is a Java framework for running deep learning models across different engines.

Maze provides an applied reinforcement learning framework for developing projects in Python.

Catniff provides JavaScript developers with a Torch-like deep learning framework.

PyTorch TNT supplies lightweight tools and utilities for training with PyTorch.

The cONNXr project provides a pure C runtime for ONNX models intended for embedded devices.

This repository contains machine learning code examples that use Comet.ml.

Burn provides a flexible, efficient, and portable tensor library for building deep learning applications.

Adaptive Reliability Layer decides when to retrain a model based on drift signals and delayed labels.