Operationalize TensorFlow Models With ML.NET

• tensorflow-onnx

  7 2,214 7.1 Jupyter Notebook

  Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX

  

The easiest way to transform the downloaded TensorFlow model to an ONNX model is to use the tool tf2onnx from https://github.com/onnx/tensorflow-onnx

• onnx

  38 16,803 9.5 Python

  Open standard for machine learning interoperability

  

ONNX is a format for representing machine learning models in a portable way. Additionally, ONNX models can be easily optimized and thus become smaller and faster.

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• optimizer

  2 589 6.1 C++

  Actively maintained ONNX Optimizer (by onnx)

  

If you happen to start with an ONNX model that you still want to optimize, then you can use the official ONNX optimizer tool https://github.com/onnx/optimizer.

• netron

  32 26,110 9.9 JavaScript

  Visualizer for neural network, deep learning and machine learning models

We need to find out the exact input and output tensor names. A tool like Netron makes this super easy. Open the original .tflite and/or the ONNX model in Netron and click the Model Properties button in the lower left corner.

• Operationalize.ML.NET

  1 1 5.8 C#

You can find the whole source code at https://github.com/mariusmuntean/Operationalize.ML.NET

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

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