38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
import kfp.dsl as dsl
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@dsl.pipeline(
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name='Prediction pipeline',
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description='Execute prediction operation for the dataset from numpy file and test accuracy and latency'
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)
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def openvino_predict(
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model_bin='gs://intelai_public_models/resnet_50_i8/1/resnet_50_i8.bin',
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model_xml='gs://intelai_public_models/resnet_50_i8/1/resnet_50_i8.xml',
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generated_model_dir='gs://your-bucket/folder',
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input_numpy_file='gs://intelai_public_models/images/imgs.npy',
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label_numpy_file='gs://intelai_public_models/images/lbs.npy',
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batch_size=1,
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scale_div=1,
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scale_sub=0
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):
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"""A one-step pipeline."""
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dsl.ContainerOp(
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name='openvino-predict',
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image='gcr.io/constant-cubist-173123/inference_server/ml_predict:5',
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command=['python3', 'predict.py'],
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arguments=[
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'--model_bin', model_bin,
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'--model_xml', model_xml,
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'--input_numpy_file', input_numpy_file,
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'--label_numpy_file', label_numpy_file,
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'--batch_size', batch_size,
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'--scale_div', scale_div,
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'--scale_sub', scale_sub,
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'--output_folder', generated_model_dir],
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file_outputs={})
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if __name__ == '__main__':
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import kfp.compiler as compiler
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compiler.Compiler().compile(openvino_predict, __file__ + '.tar.gz')
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