SageMaker

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—Table of Contents—

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SageMaker Processing

Local Mode

If arguments is provided, entrypoint is required, although aws mentioned it is not.

processor = Processor(
        ...
    entrypoint=["python3", "evaluation.py"]
)

processor.run(
    arguments=[
        "--onnx_filename", "xxxx.onnx",
    ],
        ...
)

ProcessingOutput

For source in processing output, you cannot specify a file, but only directory

SageMaker Experiment

download experiment result

import pandas as pd
from sagemaker.analytics import ExperimentAnalytics

def save_experiment_df(experiment_name: str) -> pd.DataFrame:
    trial_component_analytics = ExperimentAnalytics(
        experiment_name=experiment_name,
        input_artifact_names=[]
    )
    df = trial_component_analytics.dataframe()
    df.to_csv(f"experiment-{experiment_name}.csv")
    return

if __name__ == "__main__":
    save_experiment_df("tune-features-20220324")

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SageMaker Neo

“Amazon SageMaker Neo enables developers to optimize machine learning (ML) models for inference on SageMaker in the cloud and supported devices at the edge” … “Amazon SageMaker Neo automatically optimizes machine learning models to perform up to 25x faster with no loss in accuracy. SageMaker Neo uses the tool chain best suited for your model and target hardware platform while providing a simple standard API for model compilation”

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