complete v2.0 test
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@ -244,7 +244,7 @@ predictor, data = build_predictor_for_kernel(
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)
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```
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In the experiment of nn-Meter, we set `init_sample_num` as 1000, `finegrained_sample_num` as 10, `iteration` as 5, and `error_threshold` as 0.1.
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In the experiment of nn-Meter, we set default `init_sample_num` as 1000, `finegrained_sample_num` as 10, `iteration` as 5, and `error_threshold` as 0.1.
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nn-Meter also provided a end-to-end method for users to build a series of general latency predictors, named `nn_meter.builder.build_latency_predictor`. This method will build predictors for all kernels in `<workspace-path>/configs/predictorbuild_config.yaml` according to their corresponding parameters. The parameters includes `INIT_SAMPLE_NUM`, `FINEGRAINED_SAMPLE_NUM`, `ITERATION`, and `ERROR_THRESHOLD`. Here is an example:
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2
setup.py
2
setup.py
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@ -5,7 +5,7 @@ from setuptools import setup, find_packages
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setup(
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name='nn-meter',
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version='2.0a1',
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version='2.0',
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description='nn-Meter is a novel and efficient system to accurately predict the inference latency of DNN models on diverse edge devices.',
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long_description = open('README.md', encoding='utf-8').read(),
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long_description_content_type = 'text/markdown',
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