Skip to main content
Help the Python Software Foundation raise $60,000 USD by December 31st!  Building the PSF Q4 Fundraiser

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

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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for cloudml-hypertune, version 0.1.0.dev2
Filename, size File type Python version Upload date Hashes
Filename, size cloudml_hypertune-0.1.0.dev2-py2.py3-none-any.whl (5.3 kB) File type Wheel Python version py2.py3 Upload date Hashes View
Filename, size cloudml-hypertune-0.1.0.dev2.tar.gz (3.0 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page