# cloudml-hypertune

**Repository Path**: mirrors_GoogleCloudPlatform/cloudml-hypertune

## Basic Information

- **Project Name**: cloudml-hypertune
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-08
- **Last Updated**: 2026-02-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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/output.metrics`` in json format:

::

    {"global_step": "1000", "my_metric_tag": "0.987", "timestamp": 1525851440.123456, "trial": "0"}

Licensing
---------

- Apache 2.0

.. |pypi| image:: https://img.shields.io/pypi/v/cloudml-hypertune.svg
   :target: https://pypi.org/project/cloudml-hypertune/
.. |versions| image:: https://img.shields.io/pypi/pyversions/cloudml-hypertune.svg
   :target: https://pypi.org/project/cloudml-hypertune/