Description:
hpoflow 0.1.4
HPOflow - Sphinx DOC
Tools for Optuna, MLflow and the integration of both.
Detailed documentation with examples can be found here: Sphinx DOC
Table of Contents
Maintainers
Installation
Support and Feedback
Reporting Security Vulnerabilities
Contribution
Code of Conduct
Licensing
Maintainers
This project is maintained by the One Conversation
team of Deutsche Telekom AG.
The main components are:
hpoflow.OptunaMLflow:
A wrapper to use Optuna and log to MLflow at the same time.
hpoflow.OptunaMLflowCallback:
Class inheriting from transformers.TrainerCallback that integrates with OptunaMLflow
to send the logs to MLflow and Optuna during model training.
hpoflow.SignificanceRepeatedTrainingPruner:
An Optuna pruner
to use statistical significance (a t-test which serves as a heuristic) to stop
unpromising trials early, avoiding unnecessary repeated training during cross validation.
Installation
HPOflow is available at the Python Package Index (PyPI).
It can be installed with pip:
$ pip install hpoflow
Some additional dependencies might be necessary.
To use hpoflow.optuna_mlflow.OptunaMLflow:
$ pip install mlflow GitPython
To use hpoflow.optuna_transformers.OptunaMLflowCallback:
$ pip install mlflow GitPython transformers
To install all optional dependencies use:
$ pip install hpoflow[optional]
To install all dependencies use:
$ pip install hpoflow[all]
Release News
Here you can find the latest versions of the software:
pypi package
GitHub releases
Important news and features in the releases:
add Python 3.10 support and remove Python 3.6 support #95 -
version 0.1.4 at 2022-08-14
Support and Feedback
The following channels are available for discussions, feedback, and support requests:
open an issue in our GitHub repository
send an e-mail to our open source team
Reporting Security Vulnerabilities
This project is built with security and data privacy in mind to ensure your data is safe.
We are grateful for security researchers and users reporting a vulnerability to us, first.
To ensure that your request is handled in a timely manner and non-disclosure of vulnerabilities
can be assured, please follow the below guideline.
Please do not report security vulnerabilities directly on GitHub.
GitHub Issues can be publicly seen and therefore would result in a direct disclosure.
Please address questions about data privacy, security concepts,
and other media requests to the opensource@telekom.de mailbox.
Contribution
Our commitment to open source means that we are enabling - in fact encouraging - all interested
parties to contribute and become part of our developer community.
Contribution and feedback is encouraged and always welcome. For more information about how to
contribute, as well as additional contribution information, see our
Contribution Guidelines.
Code of Conduct
This project has adopted the Contributor Covenant
as our code of conduct. Please see the details in our
Contributor Covenant Code of Conduct.
All contributors must abide by the code of conduct.
Licensing
Copyright (c) 2021 Philip May, Deutsche Telekom AG
Copyright (c) 2021 Philip May
Copyright (c) 2021 Timothy Wolff-Piggott
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License by reviewing the file
LICENSE in the repository.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
Customer Reviews
There are no reviews.
