A library to report Google CloudML Engine HyperTune metrics.
Project description
cloudml-hypertune provides functionalities to report metrics for Google CloudML Engine Hyperparameter Tuning Service.
Installation
Install via pip:
pip install cloudml-hypertune
Prerequisites
Google CloudML Engine Hyperparameter Tuning Overview.
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 /var/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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