This release is a pre-release and may not be stable for production use.
Metric Reporting Python Package for CloudML Hypertune
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
1 maintainer
Metadata
1 maintainer
Release files for cloudml-hypertune 0.1.0.dev6
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Release files / cloudml-hypertune-0.1.0.dev6.tar.gz
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