A library to report Google CloudML Engine HyperTune metrics.
Project description
Helper Functions for CloudML Engine Hypertune Services.
Prerequisites
Google CloudML Engine Overview.
Google CloudML Engine Hyperparameter Tuning Overview.
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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