ONNX : a combined team work initiative for ai developers to hover around framework easily

Human being's support are really mandatory for any field streams from planting , mining and on space as well. With the Machine Learning's day to day prudence in variety of arenas that humans consume time a lot , this deep learners does this task hand-fully in minutes span of time that are extremely brilliant . When companies like Microsoft, Facebook , Amazon join hands to frame work one need platform for ai researchers and enthusiasts ,it is truly a gift for the developers to build to put to practice. Months back , i was stating about Gluon , now i am going to state something more alike to that of is ONNX ( Open Neural Network Exchange ) a platform where re used trained network could be trained to use for multiple platforms . One of the foremost problem we face in this network while we develop is to choose the right framework. 

Though Data Researchers and Data scientists has large no. of options available , it is not so easy to port the trained model into proper framework. Even though it takes lots of tasks to train neural network signals to practice to retrain will be a stress making process for all developers to re-frame and upload it to proper channel. ONNX does this task more easily by allowing users to retrain and re-frame it through proper export without any problems that leads the users to develop and train them more quickly. Developers can import PYTORCH model in Microsoft Cognitive Tool kit or process any images in Tensor Flow flawlessly with this uni-platform. This inter operation enables the users to reuse of any models into any proper frameworks without pressure from scratch till end of the development.

onnx details in website

As you all know deep learning needs parametric training and it is not only lengthy , complex but also expensive one. In terms of time and complexity, its a round off process like if you pass each data set through ai, neural network is trained and evolved based on parameters such as input layer , hidden layer and output layer via. modern training process. If you know of neural network training method, each weight is associated with layers to be trained and processed for evaluation under correcting neurons through proper optimization say genetic algorithm or back propagation algorithm. For every evaluation , accuracy value is compared and manual adjustments are tuned manually or automatically.

ONNX inter operability training from pytorch to apache market

onnx converters and frameworks , runtimes

  ( Image Source : ONNX )

Each frame work development is meant for specific purpose and developers can perform any tools without compromising quality and performance and also based on ONNX community .Google didn't join yet but it is another landmark in developing the framework projection. It has supported frameworks and converters to do it easily , also you can import and export easily between frameworks without any hassles. To how to do the import and export task , please visit their official link https://onnx.ai/getting-started and to check out the end-end tutorials please check out the github repository https://github.com/onnx/tutorials. For more info. about ONNX news and other supporting stuffs, please check out the official website https://onnx.ai and for self understanding please check out  how Nvidia linked out with ONNX and they made it successful in the video below