The auto-tuner, to find a better configuration for our model and get a boost In some cases, we might not get the expected performance when running How to build an optimized model using TVMC to target your working platform. Include any platform specific optimization. The previous model was compiled to work on the TVM runtime, but did not savez ( 'imagenet_cat', data = img_data ) expand_dims ( norm_img_data, axis = 0 ) # Save to. shape ): norm_img_data = ( img_data / 255 - imagenet_mean ) / imagenet_stddev # Add batch dimension img_data = np. astype ( 'float32' ) for i in range ( img_data. transpose ( img_data, ( 2, 0, 1 )) # Normalize according to ImageNet imagenet_mean = np. astype ( 'float32' ) # ONNX expects NCHW input, so convert the array img_data = np. preprocess.py from import download_testdata from PIL import Image import numpy as np img_url = '' img_path = download_testdata ( img_url, 'imagenet_cat.png', module = 'data' ) # Resize it to 224x224 resized_image = Image.

Making your Hardware Accelerator TVM-ready with UMA.Quick Start Tutorial for Compiling Deep Learning Models.Optimizing Operators with Auto-scheduling.Optimizing Operators with Schedule Templates and AutoTVM.Working with Operators Using Tensor Expression.Compiling and Optimizing a Model with the Python Interface (AutoTVM).Getting Starting using TVMC Python: a high-level API for TVM.Compiling an Optimized Model with Tuning Data.Running the Model from The Compiled Module with TVMC.Compiling an ONNX Model to the TVM Runtime.Compiling and Optimizing a Model with TVMC.An Overview of TVM and Model Optimization.

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