The example below demonstrates how that works in practice: The path to an output file in which the tuning records will be stored, and The target specification of the device you intend to run this model on In the simplest form, tuning requires you to provide three things: Ultimately the output of the tune subcommand. The results of these runs are stored in a tuning records file, which is Many different operator implementation variants to see which perform best. As part of the tuning process, TVM will try running This differs from training orįine-tuning in that it does not affect the accuracy of the model, but only Optimized to run faster on a given target. Tuning in TVM refers to the process by which a model is 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
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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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