Amazon, Facebook, and Microsoft have recently rolled out an exciting announcement for developers. The news is…

                                         ONNX 1.0 format is now production ready!

Open Neural Network Exchange (ONNX) format allows interoperability feature between various deep learning frameworks such as Caffe2, Apache MXNet, Microsoft Cognitive Toolkit (CNTK), and PyTorch.

With the new interoperable feature, the version 1.0 allows users to get their deep learning models into production at a much faster pace. One can also train the model on one framework (PyTorch, for instance), and carry-out inference on another framework (Microsoft CNTK or Apache MXNet). Since the initial release of ONNX in the month of September, many communities are getting involved and adopting ONNX within their organizations–Amazon, Facebook, and Microsoft being the major ones.

Many hardware-based organizations such as Qualcomm, Huawei, and Intel have announced an ONNX support for their hardware platforms. This gives users the freedom to run their models on different hardware platforms. Also, making frequent use of different frameworks results into integrating optimizations separately within each framework. Here, ONNX makes it easy for optimization to reach more developers .

Tools for ONNX 1.0

• Netron

Netron is a viewer for ONNX neural network models. It is capable of running on macOS, Windows, Linux and serves models via a Python web server. For a more detailed overview on Netron, visit the GitHub link here.

• Net Drawer

The Net drawer tool is used to visualize the ONNX models. This tool takes serialized ONNX model as input and processes a directed graph representation. The output graph contains information on input/output tensors, tensor names, operator types and numbers, and so on. To know more about the working of Net drawer tool visit the GitHub link here.

At present, ONNX models are supported in frameworks such as MXNet, Microsoft Cognitive Toolkit, PyTorch, and Caffe2. However, there are connectors for other common frameworks and libraries as well. Also, the current version of ONNX is designed keeping computer vision applications in mind. Amazon, Facebook, and Microsoft communities along with the ONNX community and its partners are working in union to expand beyond vision applications in the future versions of ONNX.

To know more about ONNX 1.0 in detail, please visit GitHub , or the  ONNX Website.