Open source deep learning frameworks are becoming increasingly mature, and many of them now offer advanced machine learning and AI capabilities tailored for personalized solutions. Choosing the right framework can be a challenging task, as each has its own strengths and weaknesses. This article aims to provide a helpful comparison of the major open-source deep learning frameworks, offering insights that can guide readers in selecting the most suitable option for their specific needs. Which deep learning framework do you find most promising?
Today’s machine learning frameworks are widely used across various domains such as image recognition, speech processing, natural language understanding, and more. However, no single framework is perfect for every business problem. The goal of this article is to present an intuitive overview through diagrams and explanations, helping you make informed decisions when addressing your unique challenges.
The following chart provides a summary of the most popular open-source deep learning frameworks based on their GitHub repository popularity, compiled by Mitch De Felice in early May 2017.
TensorFlow was initially developed by Google under the name DistBelief V2, a deep learning library used internally as part of the Google Brain Project. Some consider it a complete rewrite of Theano. After being open-sourced, TensorFlow quickly gained traction among developers. It supports a wide range of applications, including image recognition, speech processing, and natural language tasks. TensorFlow was released under the Apache 2.0 license on November 9, 2015.
Version 1.0 of TensorFlow was launched on February 15, 2017, marking a significant improvement over previous versions. It addressed several limitations and enhanced core functionalities. Key features of TensorFlow include:
- TensorBoard: A powerful visualization tool that helps users understand and debug neural network models.
- TensorFlow Serving: Enables easy deployment of models in production environments with consistent APIs and architecture.
- Multi-language support: Python and C++ are the primary languages, with alpha versions of Java, Go, R, and Haskell also available.
- Cloud compatibility: Runs smoothly on Google Cloud and Amazon Web Services.
- Windows support: From version 0.12 onwards, it supports Windows 7, 10, and Server 2016, making it more accessible for a wider audience.
- Flexibility: Allows users to build custom layers without low-level coding and supports subgraph execution for easier debugging.
- Distributed training: Version 0.8 introduced distributed TensorFlow, enabling parallel training across multiple devices.
Since March 2016, several top universities like Stanford, UC Berkeley, and the University of Toronto have included TensorFlow in their online courses, further boosting its adoption.
However, TensorFlow does have some drawbacks. It requires static graph construction, which limits flexibility in handling dynamic computations. It lacks 3D convolution support, which is essential for video analysis. Despite significant performance improvements—now 58 times faster than version 0.5—it still lags behind some competitors in speed and ease of use.
Caffe, created by Jason Wang and now maintained by Facebook's AI team, is one of the first industrial-grade deep learning frameworks to gain widespread attention since late 2013. Known for its efficient convolutional operations, Caffe became a favorite in computer vision tasks and even won the 2014 ImageNet Challenge. It follows the BSD 2-Clause license, making it open and flexible for both research and production.
One of Caffe’s main strengths is its speed, allowing it to process up to 60 million images per day on a single NVIDIA K40 GPU. It offers a prediction time of about 1 millisecond per image and a training time of around 4 milliseconds. Its latest versions continue to improve upon these benchmarks.
Being built on C++, Caffe can be compiled on various platforms, including Windows. It supports programming interfaces in C++, MATLAB, and Python, and has a large community contributing to a shared model library called Model Zoo. Popular networks like AlexNet and GoogleNet were developed by the community, showcasing the framework’s versatility and strength in computer vision applications.
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