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A library to report Google CloudML Engine HyperTune metrics.

Project description

Helper Functions for CloudML Engine Hypertune Services.

pypi versions

Prerequisites

Installation

Install via pip:

pip install cloudml-hypertune

Usage

import hypertune

hpt = hypertune.HyperTune()
hpt.report_hyperparameter_tuning_metric(
    hyperparameter_metric_tag='my_metric_tag',
    metric_value=0.987,
    global_step=1000)

By default, the metric entries will be stored to /tmp/hypertune/outout.metric in json format:

{"global_step": "1000", "my_metric_tag": "0.987", "timestamp": 1525851440.123456, "trial": "0"}

Licensing

  • Apache 2.0

Project details


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Source Distribution

cloudml-hypertune-0.1.0.dev6.tar.gz (3.2 kB view hashes)

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