Metadata-Version: 2.1
Name: aiqc
Version: 7.1.3
Summary: End-to-end machine learning on your desktop or server.
Home-page: https://aiqc.readthedocs.io/
Author: Layne Sadler
Author-email: layne.sadler@gmail.com
License: BSD 3-Clause
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        <a href="https://badge.fury.io/py/aiqc"><img src="https://badge.fury.io/py/aiqc.svg" alt="PyPI version" height="18"></a>
        <a href="https://docs.aiqc.io"> <img src="https://readthedocs.org/projects/aiqc/badge/?version=latest" alt="docs status" height="18"></a>
        [![License](https://img.shields.io/badge/License-BSD_3--Clause-brightgreen.svg)](https://opensource.org/licenses/BSD-3-Clause)
        
        
        </br>
        <h1 align='center'>📚&nbsp;&nbsp;<a href="https://docs.aiqc.io">Documentation</a></h1>
        </br></br>
        
        <a href="https://docs.aiqc.io">
        	<img src="https://raw.githubusercontent.com/aiqc/aiqc/main/docs/_static/images/web/framework_simple.png" width="95%" alt="framework"/>
        </a>
        </br></br></br>
        
        
        <h2>Technical Overview</h2>
        </br>
        <h3>What is it?</h3>
        <p>
        	AIQC is an open source Python package that provides a <i>declarative API for end-to-end MLOps</i> (dataset registration, preprocessing, experiment tracking, model evaluation, inference, post-processing, etc) in order to make deep learning more accessible to researchers.
        </p>
        </br>
        <h3>How does it work?</h3>
        <p>
        	The backend is a <i>SQLite object-relational model (ORM)</i> for machine learning objects (Dataset, Feature, Label, Splits, Algorithm, Job, etc). The high-level API stacks these building blocks into <i>standardized workflows</i> for various: analyses (classify, regress, generate), data types (tabular, sequence, image), and libraries (TensorFlow, PyTorch). The benefits of this approach are:
        </p>
        </br>
        <ol>
        	<li>
        		⏱️&nbsp;&nbsp;<i>90% reduction in data wrangling</i> via automation of highly conditional and repetitive tasks that vary for each type of dataset and analysis (e.g. model evaluation, metrics, and charts for every split of every model).
        	</li>
        	</br>
        	<li>
        		💾&nbsp;&nbsp;<i>Reproducibility</i>, not only because the workflow is persisted (e.g. encoder metadata) but also because it provides standardized classes as opposed to open-ended scripting (e.g. 'X_train, y_test').
        	</li>
        	</br>
        	<li>
        		🎛️&nbsp;&nbsp;<i>No need to install and maintain</i> application and database servers for experiment tracking. SQLite is just a highly-performant and portable file that is automatically configured by `aiqc.setup()`. AIQC is just a pip-installable Python package that works great in Jupyter (or any IDE/shell), and provides a Dash-Plotly user interface (UI) for a <i>real-time experiment tracking</i>.
        	</li>
        </ol>
        </br>
        
        <h3>What's on the roadmap?</h3>
        <ol>
        	<li>
        		🖥️ &nbsp;&nbsp;Expand the UI (e.g. dataset registration and model design) to make it even more approachable for less technical users.
        	</li>
        	<li>
        	 	☁️&nbsp;&nbsp;Schedule parallel training of models in the cloud.
        	</li>
        </ol>
        
        </br></br>
        <h1 align='center'>📚&nbsp;&nbsp;<a href="https://docs.aiqc.io">Documentation</a></h1>
        </br></br>
        
        
        <h2>Experiment Tracker</h2>
        <a href="https://docs.aiqc.io">
        	<img src="https://raw.githubusercontent.com/aiqc/AIQC/main/docs/_static/images/dashboard/experiment_tracker.gif" width="95%" alt="experiment_tracker"/>
        </a>
        </br></br></br>
        
        <h2>Compare Models</h2>
        <a href="https://docs.aiqc.io">
        	<img src="https://raw.githubusercontent.com/aiqc/AIQC/main/docs/_static/images/dashboard/compare_models.gif" width="95%" alt="compare_models"/>
        </a>
        </br></br></br>
        
        <h2>What if?</h2>
        <a href="https://docs.aiqc.io">
        	<img src="https://raw.githubusercontent.com/aiqc/AIQC/main/docs/_static/images/dashboard/what_if.gif" width="95%" alt="compare_models"/>
        </a>
        
        </br></br></br></br>
        
        
        <h2>Usage</h2>
        
        ```python
        # Built on Python 3.7.12 to mirror Google Colab
        $ pip install --upgrade pip
        $ pip install --upgrade wheel
        $ pip install --upgrade aiqc
        
        # Monitor and evaluate models (from CLI)
        $ python -m aiqc.ui.app
        ```
        
        ```python
        # High-level API
        from aiqc import mlops
        
        # Declare preprocessing steps
        mlops.Pipeline()
        
        # Define, train, & evaluate models
        mlops.Experiment().run_jobs()
        
        # Infer using original Pipeline
        mlops.Inference()
        ```
        
        > Official Installation Documentation:
        >
        > https://aiqc.readthedocs.io/en/latest/notebooks/installation.html
        
        
        </br></br>
        <h1 align='center'>📚&nbsp;&nbsp;<a href="https://docs.aiqc.io">Documentation</a></h1>
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 1 - Planning
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.5, <=3.8.12
Description-Content-Type: text/markdown
