cloudml-hypertune

Metric Reporting Python Package for CloudML Hypertune

Helper Functions for CloudML Engine Hypertune Services.

.. _Google CloudML Engine Hyperparameter Tuning Service: https://cloud.google.com/ml-engine/docs/tensorflow/hyperparameter-tuning-overview

|pypi| |versions|

Prerequisites

• Google CloudML Engine Overview <https://cloud.google.com/ml-engine/>__.

• Google CloudML Engine Hyperparameter Tuning Overview <https://cloud.google.com/ml-engine/docs/tensorflow/hyperparameter-tuning-overview>__.

Installation

Install via pip <https://pypi.python.org/pypi/pip>__:

::

pip install cloudml-hypertune

Usage

.. code:: python

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

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