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edge-devices

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TensorRT optimises any Deep Learning model by not only making it lightweight but also by accelerating its inference speed with an idea to extract every ounce of performance from the model, making it perfect to be deployed at the edge. This repository helps you convert any Deep Learning model from TensorFlow to TensorRT!

  • Updated Jul 26, 2019
  • Jupyter Notebook

Helmut Hoffer von Ankershoffen experimenting with arm64 based NVIDIA Jetson (Nano and AGX Xavier) edge devices running Kubernetes (K8s) for machine learning (ML) including Jupyter Notebooks, TensorFlow Training and TensorFlow Serving using CUDA for smart IoT.

  • Updated Dec 6, 2019
  • Python

Oct 12th @ in5 - Hands-on Internet Of Things workshop with Etisalat Digital & PTC. At this session, we’ll take you step by step over the process of creating a modular IoT solution using the Etisalat Thingworx Platform to monitor weather conditions at various locations. We’ll show you how to sync data from edge devices and sensors onto the cloud …

  • Updated Dec 23, 2019
  • Lua

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