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 PyTorch
  • Model deployment from PyTorch
  1. For Data Scientists
  2. Importing models

From PyTorch

PreviousFrom UltralyticsNextFrom TensorFlow / TFLite

About PyTorch

is a based on the library, used for applications such as and , originally developed by and now part of the umbrella. It is released under the . Although the interface is more polished and the primary focus of development, PyTorch also has a interface.

Model deployment from PyTorch

Model deployment from PyTorch is simple to achieve by exporting your PyTorch model to ONNX and subsequently using (if neccesary) the sclblonnx package to for an upload to the Nx AI cloud.

  • You can find details on PyTorch to ONNX exports . You can find an insightful tutorial .

  • You can find an example using PyTorch and sclblonnx .

After obtaining a , you can for deployment.

PyTorch
machine learning
framework
Torch
computer vision
natural language processing
Meta AI
Linux Foundation
free and open-source software
modified BSD license
Python
C++
here
here
here
clean ONNX graph that adheres to our requirements
upload it to the Nx AI cloud
clean and check the resulting graph