Metadata-Version: 2.1
Name: ml_base
Version: 0.2.2
Summary: Base classes and utilities that are useful for deploying ML models.
Home-page: https://github.com/schmidtbri/ml-base
Author: Brian Schmidt
Author-email: 6666331+schmidtbri@users.noreply.github.com
Project-URL: Documentation, https://schmidtbri.github.io/ml-base/
Project-URL: Code, https://github.com/schmidtbri/ml-base
Project-URL: Tracker, https://github.com/schmidtbri/ml-base/issues
Keywords: machine learning,REST,service,model deployment
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
License-File: LICENSE

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# ml-base

**ml-base** is a package that provides base classes and utilities that are useful for deploying machine learning models.

## Installation 

The easiest way to install ml-base is using pip

```bash
pip install ml-base
```

## Usage

There are several examples of how to use the ml-base framework in the 
[documentation](https://schmidtbri.github.io/ml-base/).

## Development

First, download the source code with this command:

```bash
git clone https://github.com/schmidtbri/ml-base
```

Then create a virtual environment and activate it:

```bash
# go into the project directory
cd ml-base

make venv

source venv/bin/activate
```

Install the dependencies:

```bash
make dependencies
```

## Testing 

To run the unit test suite execute these commands:

```bash
# first install the test dependencies
make test-dependencies

# run the test suite
make test

# clean up the unit tests
make clean-test
```
