Introduction to ONNX
The ecological community of deep learning scheme, platforms and gadgets are increasing at an astonishingly nimble speed. A few years ago, there were few deep learning and machine learning frameworks available to the world, not a month goes by these days without hearing about a new excellent deep learning framework that specializes in a particular area of interoperability between the different deep learning stacks in the market which kills any hope of reusing models and networks across different runtimes. In this article we discuss about ONNX.
The basic working of Deep learning is consummate through calculation over data flow graphs. Under the shed of Deep knowledge, The graphs are divided into types – Dynamic and Static graphs. Different frameworks of deep learning use different kinds of graphs. Frameworks such as CNTK, Caffe2, Theano, and Tensorflow prefer the use of static graphs; on the other hand, frameworks like PyTorch and Chainer use dynamic graphs. These graphs deliver an Intermediate Representation that catches that specific intent of any source code. It can run on the number of devices (FPGA, GPU, CPU, etc.). Rising of different frameworks demands their interoperability too. The demand for these frameworks maneuverability and portability is becoming more critical than ever.
The first step which is approaching to touch the mentioned demand is the rising of open environs known as Open Neural Network Exchange . It is an open source format subjected to contribute an open source configuration for Artificial Intelligence models. It characterizes an expandable computation graph model and the translation of built-in operators and authoritative data types.
Supporting Capabilities of ONNX
The installation of ONNX is purely subjected to the framework which will be used to define the model. The following table presents the learning about the supporting capabilities of ONNX –
|TYPE OF TOOLS||SUPPORTED TOOLS|
|Frameworks||Caffe2, Chainer, Cognitive Toolkit,MxNet,PyTorch,Paddle Paddle|
|Converters||CoreML , MathWorks, Tensorflow|
|Runtimes||Nvidia, Qualcomm, SOPHON, Tencent, Vespa, Windows|
|Compilers||Intel AI, skymizer,tvm|
|Visualizers||NETRON , Visual DL|
Installation of ONNX
The primary target of this topic is to show how the logic of Deep learning can be implemented using different frameworks and ONNX. And how the applied models can be imported and exported in the Open Neural Network Exchange ecosystem.
The following command will install ONNX concerning MxNet –
pip install onnx-mxnet
Why Open Neural Network Exchange?
Two main things about an Artificial Intelligence Model are the production of the model and of course its deployment. The incrementation in the frameworks related to deep learning making it a cumbersome task. The technical ability provided by ONNX allows a data scientist to get great ideas into production faster. It gives an extra edge to choose a specific framework for a particular task from different frameworks available which results spending less time for making a model ready for production and its deployment.
An ecosystem of tools for visualization and acceleration of models is also provided under functionalities of ONNX models. For supporting the concept of transfer learning pre-trained ONNX models are also available for common scenarios.
The community of partners which supported and developed ONNX emphasis on two critical factors as the advantage of Open Neural Network Exchange –
Framework Interoperability – This can be treated as the primary necessity after the invention of ONNX. It provides a free hand to the Data scientists to train a model in a framework and to generate inference in another framework.
Hardware Optimizations – With the use of Open Neural Network Exchange, it is easy to give the ability of optimization to the Data Scientists. It provides the benefits of ONNX compatible runtimes and libraries to every tool which uses the exported ONNX models, and it results in the maximization of the performance on some of the best hardware in the technical field.
A Comprehensive Approach
The rise of Open Neural Network Exchange can be understood as the rise of the concept of having a single universal language in the field of Deep learning.The adequacy of standards is limited, and the key element is the acceleration in the adoption of the mainstream deep learning platforms which will be gained from the integration of deep learning models and ONNX.To know more about Neural Networks and Frameworks we recommend taking the following steps –
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