{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/21"},{"label":"Internet","url":"https://skillfed.io/packages/category/internet/5"},{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing/3"}],"enrichment":{"capability":"Reports hyperparameter tuning metrics to Google CloudML Engine's hyperparameter tuning service, writing metric data to a local file for the service to consume.","skillfed_tags":["google-cloud","hyperparameter-tuning","abandoned"],"use_cases":["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."],"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\u2014including hyperparameter tags, metric values, and global step counts\u2014to a local JSON file that the CloudML Engine service reads and processes.\n\nThe 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.","worth_installing":"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no commits since June 2023), targets obsolete Python versions (2.7, 3.5), and modern Google Cloud ML alternatives have superseded it.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."},"id":"cloudml-hypertune","links":{"html":"https://skillfed.io/packages/cloudml-hypertune","md":"https://skillfed.io/packages/cloudml-hypertune.md","pypi":"https://pypi.org/project/cloudml-hypertune/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2019-12-18","license_spdx":null,"license_treatment":"permissive","name":"cloudml-hypertune","python_support":"unspecified","summary":"A library to report Google CloudML Engine HyperTune metrics."},"popularity":{"monthly_downloads":86211,"position":13881,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.0.dev6"}