Tvmc 2018 download10/29/2023 astype ( "float32" ) for i in range ( img_data. shape ): norm_img_data = ( img_data / 255 - imagenet_mean ) / imagenet_stddev # Add batch dimension img_data = np. 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. Include any platform specific optimization. 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 How to build an optimized model using TVMC to target your working platform. 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. The results of these runs are stored in a tuning records file, which is Many different operator implementation variants to see which perform best. Output resnet50-v2-7-autotuner_records.json \ 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.
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