cloudml-hypertune
A library to report Google CloudML Engine HyperTune metrics.
Decision gist · record as of 2026-08-14
No, unless you are maintaining a legacy Google CloudML Engine hyperparameter tuning project that already depends on this library. The package is abandoned (last release December 2019, no commits since June 2023), targets obsolete Python versions (2.7, 3.5), and modern Google Cloud ML alternatives have superseded it. For new projects, use Google's current ML training and tuning services instead.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Google CloudML Engine hyperparameter tuning service infrastructure; metrics are written to /tmp/hypertune/output.metric in JSON format.
- High install friction due to a development-stage tarball release (0.1.0.dev6).
- Package is abandoned as of 2023-06-02, with no maintenance for over 2 years.
License · maintenance · safety
Apache Software License (permissive) — Apache 2.0 permissive license allows commercial and private use with minimal restrictions, making licensing itself not a barrier to adoption.
last release 2019-12-18 (2431 days) · last repo commit 2023-06-02 · 39 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 86,211 downloads/mo, #13,881 on PyPI
Alternatives
Verify before relying
pip install cloudml-hypertune
import hypertune
hpt = hypertune.HyperTune()
hpt.report_hyperparameter_tuning_metric(
hyperparameter_metric_tag='my_metric_tag',
metric_value=0.987,
global_step=1000)- Whether this package works with modern Python versions (classifiers list Python 2.7 and 3.5 only, both EOL)
- Current compatibility with Google CloudML Engine API if the service has evolved since 2019
- Whether /tmp/hypertune/output.metric path is still the correct metric reporting location for current CloudML Engine
What it is and what it does
cloudml-hypertune is a helper library for reporting metrics to Google CloudML Engine's hyperparameter tuning service. It provides a simple HyperTune class that writes metric data—including hyperparameter tags, metric values, and global step counts—to a local JSON file that the CloudML Engine service reads and processes.
The package has no runtime dependencies and is designed as a lightweight bridge between your training code and Google's hyperparameter tuning infrastructure. It is abandoned, with its last release in December 2019 and no commits since June 2023, making it suitable only for legacy projects already committed to this integration pattern.
Use it for
- Report custom metrics during hyperparameter tuning runs on Google CloudML Engine to guide the tuning algorithm.
- Log training progress (loss, accuracy, etc.) at specific global steps for CloudML Engine to evaluate hyperparameter configurations.
- Integrate metric reporting into existing TensorFlow or other ML training scripts running on CloudML Engine infrastructure.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No, unless you are maintaining a legacy Google CloudML Engine hyperparameter tuning project that already depends on this library.
The package is abandoned (last release December 2019, no commits since June 2023), targets obsolete Python versions (2.7, 3.5), and modern Google Cloud ML alternatives have superseded it. For new projects, use Google's current ML training and tuning services instead.
Install
cloudml-hypertune on PyPI
Before you install
High install friction due to a development-stage tarball release (0.1.0.dev6). Package is abandoned as of 2023-06-02, with no maintenance for over 2 years. Suitable only for legacy Google CloudML Engine projects already locked to this library.
Requires Google CloudML Engine hyperparameter tuning service infrastructure; metrics are written to /tmp/hypertune/output.metric in JSON format.
License in practice
Apache 2.0 permissive license allows commercial and private use with minimal restrictions, making licensing itself not a barrier to adoption.
Quickstart
pip install cloudml-hypertune
import hypertune
hpt = hypertune.HyperTune()
hpt.report_hyperparameter_tuning_metric(
hyperparameter_metric_tag='my_metric_tag',
metric_value=0.987,
global_step=1000)Verify before relying
- Whether this package works with modern Python versions (classifiers list Python 2.7 and 3.5 only, both EOL)
- Current compatibility with Google CloudML Engine API if the service has evolved since 2019
- Whether /tmp/hypertune/output.metric path is still the correct metric reporting location for current CloudML Engine
Package facts
| License | Apache Software License permissive |
| Python support | Not specified |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Abandoned 2,431 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 86,211 / month, #13,881 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseOperating System :: MacOSOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 2.7Programming Language :: Python :: 3.5Topic :: InternetTopic :: Scientific/EngineeringTopic :: Software Development :: Libraries :: Python ModulesTopic :: System :: Distributed Computing |
Evidence: cloudml-hypertune-0.1.0.dev6.tar.gz
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