Nx AI Manager Documentation
  • Nx AI Manager plugin v4.4
  • Nx AI Manager
    • Get started with the NX AI Manager plugin
    • 1. Install Network Optix
    • 2. Install Nx AI Manager Plugin
    • 3. Configure the Nx AI Manager plugin
      • 3.1 Model Settings
      • 3.2 Model pipeline selection and configuration
      • 3.3 Model pipelines on multiple devices
    • 4. Other Network Optix Plugin Settings
    • 5. Manual Plugin Installation
    • 6. Removing the Nx AI Manager
    • 7. Advanced configuration
      • 7.1 Nx AI Manager Manual Installation
      • 7.2 External Post-processing
      • 7.3 External Pre-processing
      • 7.4 Training Loop
      • 7.5 Enable ini settings
  • Nx AI Cloud
    • Introduction
    • Registration and log-in
    • Deployment and device management
    • Upload your model
      • Normalization
    • Use your model
    • API Documentation
  • SUPPORT & TROUBLESHOOTING
    • How to get support
    • Troubleshooting
      • Plugin checks
      • OS checks
      • System checks
      • Things to try
      • Controlling the server and the plugin
      • Q&A
  • Videos
    • Howto videos
  • AI Accelerators Support
    • Introduction
    • Supported AI accelerators
    • Nvidia Support
    • OpenVino Support
    • Hailo Support
  • For Data Scientists
    • Introduction
    • About ONNX
    • Custom model creation
    • ONNX requirements
    • Importing models
      • From Edge Impulse
      • From Nota AI
      • From Teachable Machine
      • From Hugging Face
      • From Ultralytics
      • From PyTorch
      • From TensorFlow / TFLite
      • From Scikit-learn
      • Common Models
  • Miscellaneous
    • Nx AI Certification Test
    • Nx AI Manager on SCAiLX
    • Privacy policy
    • Support
    • End user license agreement
    • Nx cloud cookie statement
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On this page
  • About TensorFlow
  • Model deployment from TFLite
  • Model deployment from TensorFlow
  1. For Data Scientists
  2. Importing models

From TensorFlow / TFLite

PreviousFrom PyTorchNextFrom Scikit-learn

About TensorFlow

TensorFlow is a for and . It can be used across a range of tasks but has a particular focus on and of .

TensorFlow was developed by the team for internal use in research and production. The initial version was released under the in 2015.Google released the updated version of TensorFlow, named TensorFlow 2.0, in September 2019.

TensorFlow can be used in a wide variety of programming languages, including , , , and . This flexibility lends itself to a range of applications in many different sectors.

Model deployment from TFLite

You can upload a model directly, and the cloud will take care of exporting the model to ONNX.

Model deployment from TensorFlow

Exporting from TensorFlow to ONNX is also possible using , as illustrated in these examples: and .

Your TensorFlow model can be exported to ONNX and subsequently using cleaned and checked by the the sclblonnx package for an upload to the Nx AI cloud.

After obtaining a , you can upload it to the .

free and open-source
software library
machine learning
artificial intelligence
training
inference
deep neural networks
[4]
[5]
Google Brain
Google
[6]
[7]
[8]
Apache License 2.0
[1]
[9]
[10]
Python
JavaScript
C++
Java
[11]
TFLite
the tf2onnx tools
here
here
clean ONNX graph that adheres to our requirements
Nx AI cloud for deployment