![]() ![]() How to build an optimized model using TVM to target your working platform. Include any platform specific optimization. The previous model was compiled to work on the TVM runtime, but did not The Python Image Library for working with the image data, numpy for preĪnd post-processing of the image data, the TVM Relay framework, and the TVM Loading and converting the model, helper utilities for downloading test data, We begin by importing a number of dependencies, including onnx for Tutorial we will work through how to load, compile, and optimize a model TVM is a deep learning compiler framework, with a number of different modulesĪvailable for working with deep learning models and operators. The goal of this section is to give you an overview of TVM’s capabilites and Run the image through the optimized model, and compare the output and model Re-compile an optimized model using the tuning data collected by TVM. Tune the model that model on a CPU using TVM. Run a real image through the compiled model, and interpret the output and model Used the Python API for TVM to accomplish the following tasks:Ĭompile a pre-trained ResNet-50 v2 model for the TVM runtime. Upon completion of this section, we will have In this tutorial we will cover the same ground we did with TVMC, but show how That gives you tremendous flexibility in working with machine learning models. Optimizing framework with APIs available for a number of different languages TVM is more that just a command-line tool though, it is an Pre-trained vision model, ResNet-50 v2 using the command line interface for In the TVMC Tutorial, we covered how to compile, run, and tune a
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