Apache MXNet was an open-source deep learning framework that could train models efficiently across multiple GPUs and machines, with interfaces for Python, C++, R, Scala and other languages. Amazon Web Services backed it as its preferred framework for some years. The Apache Software Foundation retired the project in September 2023 and moved it to the Apache Attic by February 2024; it is no longer developed. Existing models are usually migrated to other frameworks.
BMXNet is an open-source library for binary neural networks, released in 2017 by the Hasso Plattner Institute in Potsdam and built on the Apache MXNet framework. Binary networks store weights and activations with a single bit instead of 32, making models far smaller and faster – attractive for smartphones and embedded devices, though usually at some cost to accuracy. Since MXNet was retired in 2023, BMXNet is now mainly of research interest (as of October 2026).
PyTorch is an open-source framework for machine learning and deep learning in the Python programming language. It was originally developed at Meta (then Facebook) and has been stewarded since 2022 by the PyTorch Foundation under the umbrella of the Linux Foundation. PyTorch is widely used in research and industry because it lets developers build models flexibly, train them on GPUs and test them step by step; many well-known language and image models are built with it.
TensorFlow is an open-source machine learning framework developed by Google and released in 2015. It is used to build, train and deploy neural networks in applications, on servers or on mobile devices. Keras provides an accessible high-level interface; typical uses include image classification, forecasting and recommendation systems.
Open Neural Network Exchange (ONNX) is an open file format for machine learning models, introduced in 2017 by Microsoft and Facebook (now Meta) and now hosted by the LF AI & Data Foundation. A model trained in PyTorch, for example, can be exported to ONNX and run with other tools or runtimes such as ONNX Runtime. This makes it easier to switch between frameworks and deploy on different hardware.
