title: What are the main differences between TensorFlowLite, TendorFlow-TRT and TensorRT?
body: <p>I am using the Coral devboard and the Nvidia Jetson TX2. And that is how I got to know about TensorFlow-Lite, TensorFlow-TRT and TensorRT.
  I have some questions about them:</p>
  <ol>
  <li><p>Between TensorFlow-TRT and TensorRT:
  When using a fully optimised/compatible graph with TensorRT, which one is faster and why?</p>
  </li>
  <li><p>The pipeline to use TFlite in a Google Coral (When using TensorFlow 1.x...) is:</p>
  </li>
  </ol>
  <p>a. Use a model available in TensorFlow's zoo</p>
  <p>b. Convert the model to frozen (.pb)</p>
  <p>c. Use protobuff to serialize the graph</p>
  <p>d. Convert to Tflite</p>
  <p>e. Apply quantization (INT8)</p>
  <p>f. Compile</p>
  <p>what would be the pipeline when using TensorFlow-TRT and TensorRT?
  Is there somewhere where I can find a good documentation about it?</p>
  <p>So far I think TensorRT is closer to TensorFlow Lite because:</p>
  <ul>
  <li><p>TFlite: after compilation you end up with a .quant.edtpu.tflite file which can be used to make inference in the devboard</p>
  </li>
  <li><p>TensorRT: you will end up with a .plan file to make inference in the devboard.</p>
  </li>
  </ul>
  <p>Thank you for the answers, and if you can point me to documentation which compares them, that will be appreciated.</p>