semvec/pypi-packages huggingface dataset:
baseq 0.1.2
Tools For Bioinformatics
Keywords: sample setuptools development

# baseqs Tools

This is a Tools for baseq applications... [Python Packaging User
Guide][packaging guide]'s [Tutorial on Packaging and Distributing
Projects][distribution tutorial].

This project does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.

[The source for this project is available here][src].

Most of the configuration for a Python project is done in the `setup.py` file,
an example of which is included in this project. You should edit this file
accordingly to adapt this sample project to your needs.

----

This is the README file for the project.

The file should use UTF-8 encoding and can be written using
[reStructuredText][rst] or [markdown][md use] with the appropriate [key set][md
use]. It will be used to generate the project webpage on PyPI and will be
displayed as the project homepage on common code-hosting services, and should be
written for that purpose.

Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not a
good idea, although a simple “What's New” section for the most recent version
may be appropriate.

[packaging guide]: https://packaging.python.org
[distribution tutorial]: https://packaging.python.org/en/latest/distributing.html
[src]: https://github.com/pypa/sampleproject
[rst]: http://docutils.sourceforge.net/rst.html
[md]: https://tools.ietf.org/html/rfc7764#section-3.5 "CommonMark variant"
[md use]: https://packaging.python.org/specifications/core-metadata/#description-content-type-optional
hyperdash 0.15.3
Hyperdash.io CLI and SDK

Hyperdash
----------

.. image:: https://circleci.com/gh/hyperdashio/hyperdash-sdk-py/tree/master.svg?style=svg
    :target: https://circleci.com/gh/hyperdashio/hyperdash-sdk-py

`Hyperdash`_ is a machine learning monitoring library capable of running alongside Tensorflow, Scikit-Learn, and other modeling libraries. It was developed with a focus on enabling fast knowledge gain.

Use Hyperdash if you're looking for cloud-based model monitoring that:

* Is fast and easy-to-use with scripts and Jupyter.
* Tracks your hyperparameters across different model experiments.
* Graphs performance metrics (loss, reward, etc.) in real-time.
* Can be viewed remotely on the `Web`_, `iOS`_, and `Android`_ without self-hosting (e.g. Tensorboard).
* Saves your experiment's print output (standard out / error) as a local log file.
* Notifies you when a long-running experiment has finished.

Hyperdash is compatible with: **Python 2.7 - 3.6**

.. _Hyperdash: https://hyperdash.io
.. _Web: https://hyperdash.io/dashboard
.. _Android: https://play.google.com/store/apps/details?id=com.hyperdash
.. _iOS: https://itunes.apple.com/us/app/hyperdash-machine-learning-monitoring/id1257582233https://play.google.com/store/apps/details?id=com.hyperdash