From TensorFlow / TFLite

About Tensorflow

TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.[4][5]

TensorFlow was developed by the Google Brain team for internal Google use in research and production.[6][7][8] The initial version was released under the Apache License 2.0 in 2015.[1][9]Google released the updated version of TensorFlow, named TensorFlow 2.0, in September 2019.[10]

TensorFlow can be used in a wide variety of programming languages, including Python, JavaScript, C++, and Java.[11] This flexibility lends itself to a range of applications in many different sectors.

Model deployment from Tensorflow

Model deployment from Tensorflow (or the subset of tensorflow optimized for edge devices called tflite) is simple to achieve by exporting your Tensorflow model to ONNX and subsequently using (if neccesary) the sclblonnx package to clean and check the resulting graph for an upload to the Nx AI cloud.

• Exporting from TF to ONNX is easy using the tf2onnx tools, as illustrated in these examples: here and here.

• Otherwise, you can upload the TFLite model directly, and the cloud will take care of exporting the model to ONNX.

After obtaining a clean ONNX graph that adheres to our requirements, you can upload it to the Nx AI cloud for deployment.