What is Caffe – Everything you need to know about the deep learning framework from Coding compiler. Caffe is a deep learning framework characterized by its speed, scalability, and modularity. Caffe works with CPUs and GPUs and is scalable across multiple processors. The Deep Learning Framework is suitable for industrial applications in the fields of machine vision, multimedia and speech.
What is Caffe – The Deep Learning Framework
The Deep Learning Framework Caffe was originally developed by Yangqing Jia at the Vision and Learning Center of the University of California at Berkeley. Members of the community continued to drive Caffe’s evolution.
Optimized for speed, modularity and scalability, the framework provides solutions for both academic research projects and industrial applications in artificial intelligence. The framework is specialized on language, machine vision and multimedia. The framework is available as free open source software under BSD license. Caffe written in C ++.
Yahoo has integrated Caffe into Spark and enables Deep Learning on distributed architectures. With Caffe’s high learning and processing speed and the use of CPUs and GPUs, deep learning models can be trained in just a few hours. Latest Nvidia Pascal GPUs support Caffe and offer up to 65 percent faster speeds. Up to 60 million images per day can be processed (as of 2018). The intended programming interfaces of the framework are Python and MATLAB.
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The main features of Caffe
Key features of Caffe include support for Central Processing Units and Graphics Processing Units, as well as Nvidia’s Compute Unified Device Architecture (CUDA ) and the cuDNN Library (CUDA Deep Neural Network), also from this vendor . Thus, the framework is designed primarily for speed.
As a platform for Caffe come Linux distributions such as Ubuntu but also MacOS and Docker container in question. For Windows installations, solutions are also available on GitHub. For the Amazon AWS Cloud, Caffe is available as a preconfigured Amazon Machine Image (AMI).
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Caffe can work with many different types of deep learning architectures. The framework is suitable for various architectures such as CNN ( Convolutional Neural Network ), Long-Term Recurrent Convolutional Network (LRCN), Long Short-Term Memory (LSTM ) or fully connected neural networks . A large number of preconfigured training models are available to the user, allowing a quick introduction to machine learning and the use of neural networks.
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Caffe and the meaning of blobs
Caffe stores and processes data in so-called blobs. A blob is a standard array and unified memory interface. The properties of a blob describe how information is stored in the various layers of the neural network and how it is communicated across the network.
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Caffe and Spark applications
Yahoo has developed CaffeOnSpark specifically for the integration of deep learning applications in Spark. Spark’s MLlib machine learning library does support some deep learning algorithms, but CaffeOnSpark makes it easy to implement powerful deep learning applications. CaffeOnSpark combines deep learning with the cluster computing framework.
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Even large amounts of data from Hadoop can be processed thanks to the integration for applications of deep learning. Deep learning can be applied directly to distributed, cluster-based big data Architectures are executed. The necessary data movements are reduced and higher processing speeds compared to conventional solutions are the result. CaffeOnSpark is available via GitHub.
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More deep learning frameworks besides Caffe
Caffe is in competition with other deep learning frameworks. Typical frameworks in this area are:
- The Microsoft Cognitive Toolkit – also known as the Computational Network Toolkit (CNTK)
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The Microsoft Cognitive Toolkit features the ability to use distributed architecture and allows training of models across multiple servers and CPUs / GPUs. The Microsoft Cognitive Toolkit is used, for example, for Microsoft services such as Cortana, Skype, Bing or for the game console Xbox.
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TensorFlow was developed by the Google Brain team and is used by, among others, companies of the Alphabet Group and in applications such as Gmail, Google Search, Google Translate or Yahoo. Like Caffe, TensorFlow is freely available on GitHub. As a special feature TensorFlow offers the possibility to visualize the used graphs in the graphical user interface TensorBoard.
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The Theano framework was developed at the Montreal Institute for Learning Algorithms (MILA). It features GPU support and advanced fault diagnosis and detection capabilities.
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Veles comes from Russian developers and is designed for deep learning on distributed platforms. Models can be trained on PCs and on high-performance clusters. Extracted applications can be made available as a cloud service.