Top 10 Deep Learning Frameworks for 2022

One of the better-known deep learning platforms on the market, TensorFlow is an end-to-end open source solution for powering any machine learning strategy. It’s made up of a comprehensive ecosystem of libraries, tools, and community resources to help developers easily build and deploy powerful applications in their field. 

Created by Google, TensorFlow is a JavaScript-based framework, with easy solutions for deploying models on browsers. You can build and train your models using high-level APIs, and even produce ML offerings for the cloud, on-prem, and on-device ecosystem. A simple architecture helps anyone bring ideas to life faster.

Chainer is an intuitive, flexible, and powerful framework for connecting and transforming neural networks. The solution conveniently bridges the gap between implementations of deep learning and algorithms, with CUDA computation included. It only takes a few lines of code to leverage a GPU, and the intuitive environment makes it easy to control flow statements in Python.

As a flexible environment for deep learning, Chainer supports various network architectures, including recurrent nets, convnets, and feed-forward nets, as well as per-batch architectures. There’s plenty of helpful documentation to get you started too.

A phenomenal example of the power of open-source machine learning frameworks, PyTorch helps to accelerate the potential of any project, from research through to production deployment. With PyTorch, companies gain access to a production-ready environment where they can seamlessly transition between graph and eager models with Torchscript. 

There’s a scalable and distributed training system, as well as tools for performance optimization and research. What’s more, a robust ecosystem of tools and libraries makes it easy to extend your PyTorch experience and unlock new creations in computer vision, natural language processing, and other areas.

One of the better-known deep learning environments for Python coders, Keras benefits from being simple, flexible, and versatile. This powerful tool can run on top of existing environments like Tensorflow and Microsoft Cognitive Toolkit. The offering consistently follows best practices for minimizing cognitive load, offers simple and consistent APIs, and minimizes the number of user actions required for common use cases. 

One of the biggest selling points of Keras is its phenomenal speed. The system comes with built-in solutions for data parallelism, and can process massive volumes of data in no time. You can accelerate your training time for models with fast-paced solutions. There are also extensive developer and documentation guides available. 

Created by the experts at DeepMind, Sonnet is a powerful library and framework created to build complex neural network structures via TensorFlow. This deep learning framework is actually built on top of the TensorFlow environment, taking full advantage of the functionality offered by Google. With Sonnet, business leaders can develop and create the primary Python objects connected to specific parts of a neural network.

From there, you can connect your objects to your computational Tensorflow graph, to create a variety of powerful tools. The overall solution is streamlined and intuitive, perfect for companies hoping to create innovative machine learning projects quickly.

Created as part of a collaboration between Microsoft and AWS (Amazon Web Services), Gluon is an easy-to-use interface for deep learning projects. The solution was designed to improve the flexibility, accessibility, and speed of using machine learning technologies for all developers. The Gluon interface is an open-source library within the Apache MXNet space, which adapts to support the needs of developers of all skill levels. 

Gluon delivers a straightforward and effective API for defining deep learning and machine learning models with an arrangement of pre-built neural network components. There are a complete range of plug-and-play building blocks to work with too, so you can take your solution to market faster.

ONNX, or the Open Neural Network Exchange is the result of a collaboration between Facebook (now Meta) and Microsoft. The solution provides developers with an open ecosystem for the development and presentation of deep learning and ML models. The benefits of the system include interoperability with a range of interface engines, and easy hardware access. 

With the ONNX environment, users have access to an computation graph model, along with various definitions of built-in standard data types and operators. ONNX also helps to simplify the process of moving models between different modes of working with AI.

A suite of tools created to aid developers in running deep learning models in the JVM, Eclipse Deeplearning4J is the only framework which allows users to train models from Java while interoperating with the python environment. The use cases for this solution include retraining and importing models, and deploying JVM within various micro service environments. 

Deeplearning4J comes with a range of submodules to help get you started, as well as a huge selection of knowledgebase articles and documentation options. The open-source environment offers excellent levels of flexibility.

Another phenomenal opensource solution for machine learning, deep learning, and predictive analytics, H2O makes ML innovations more accessible to the masses. The technology is supported by a leading company in the machine learning space, and provides data science teams with the technology they need to build, deploy, and manage scalable predictive models. 

The framework comes with machine learning algorithms, as well as pre-built models for beginners. There are also visualisation tools which assist developers in creating models for data. H2O uses a unique kind of architecture which also leverages distributed computing to manage and leverage data at an incredible scale.

Otherwise known as Apache MxNet, MxNet is a leading deep learning framework used for flexible research production and prototyping. The hybrid front-end environment transitions between Eager and Gluon imperative mode, and symbolic mode for speed and flexibility. You’ll also have access to 8 different language bindings to choose from, including Java and Python.

MxNet is packed full of useful capabilities, including distributed training for scalable operations and performance optimization. There’s also a thriving ecosystem of tools and libraries to help your discover new use cases and opportunities for growth.