Skip to content
master
Switch branches/tags
Code

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
May 31, 2018
Jun 12, 2018
May 31, 2018

Metric Reporting Python Package for CloudML Hypertune

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

About

No description, website, or topics provided.

Resources

License

Packages

No packages published

Languages