Apache Singa

An effort undergoing incubation

Apache Singa

Singa is known as incubating project to develop an open source deep learning library. The Singa project was initiated by the DB System Group in the year 2014 at Singapore’s National University. It supports different types of deep learning models such as Recurrent Neural Networks (RNN), Restricted Boltzmann Machine (RBM), and Convolutional Neural Networks (CNN) etc. Singa is based on a flexible architecture, it runs several hybrid, asynchronous, and synchronous frameworks for training. Software of Singa has three main components i.e. Model, IO, and Core. The component named Core is concerned with the tensor operations and memory management. IO carries the classes for to read and write the data to disc and the network. Model carries the algorithms and data structures for machine learning models.
Below are some of highlights of Singa-

Goals behind designing Singa

Programming model of Singa is quite easy to understand and follow. Users craft their models by combining the operators named Layers and the runtime of Singa takes complete care of the communication and the distributed execution between the nodes. By partitioning the mode and training data and distributing the training over various modes, Scalability can be achieved. Singa’s code is extensible and modular to support various types of deep learning models and different algorithms of optimization/training.
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extensibility

Scalability

Usability

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How Singa can be helpful

If you want to take absolute advantage of Apache Singa then our experts can help you. They will help you to understand its base and what are the techniques to make its best use. Apache Singa Provides an easy programming model and performs across a cluster of machines. It is mainly used in image recognition and in natural language processing. It was designed with layer abstraction intuitive programming model.

  • Singa can be extremely helpful in improving the performance of any system. It can provide the below mentioned assistance-
  • Improving the efficiency and scalability of a system by using multi-threading for one-node computation and infiniband for the network communication
  • Benchmarking with the larger models and datasets
  • Addition of more built-in models for deep learning. Users can directly train the built-in models on datasets.

What is deep learning?

Deep learning means set of representation or feature learning models that are loaded with multiple layers where distinct layers learn distinct abstraction-levels of raw input data. Deeper and larger models have displayed better performance for example, error of lower image classification in the large scale visual recognition challenge. Though, larger model needs more memory and bigger training data for reducing the over fitting. Training computation becomes intensive due to complex numeric operations. Training the large deep learning models may take weeks or sometimes even months on single node. Deep learning has earned immense attraction both industry and academia because of its humungous success in areas like speech recognition and computer vision. Training of such kinds of models is highly expensive. Singa can be a great help for the purpose. This complete distributed platform named Singa can provide the needed benefit. Singa is the best suited distributed training platform for training the deep learning models such as Deep belief Network and Deep Convolutional Neural Network etc. It is also loaded with the built-in training algorithms such as Back-Propagation. Time and technology both are changing at a rapid pace. Therefore, it is essential for you to match your steps with the contemporary methods that can provide more meaning and profit to your business operations.
Apache Singa is designed to smoothly deal with the deep learning models. If you want to know about the usage and utilities of Apache Singa, then you can approach an expert straight away. N numbers of experts are available online who can elaborate about Apache Singa exceedingly well. They can provide you the needed notes and practical examples required for learning the Apache Singa.

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