mls-model-registry (sktmls)
Contents
Description
A Python package for MLS model registry.
This python package includes
- Customized prediction pipelines inheriting MLSModel
- Model uploader to AWS S3 for meta management and online prediction
Installation
Installation is automatically done by training containers in YE. If you want to install manually for local machines,
# develop
pip install --index-url https://test.pypi.org/simple/ --no-deps sktmls
# production
pip install sktmls
How to use
- MLS Docs: https://ab.sktmls.com/docs/model-registry
- sktmls Docs: https://sktaiflow.github.io/mls-sdk/sktmls
Development
Requirements for development
- Python 3.6
- requirements.txt
- requirements-dev.txt
Local model registry
To enable all model related features in local environment, you need to create a directory models in your home directory.
$ cd ~/
$ mkdir models
Python environment
First you need to do the followings
$ python -V # Check if the version is 3.6.
$ python -m venv env # Create a virtualenv.
$ . env/bin/activate # Activate the env.
$ pip install --upgrade pip
$ pip install --upgrade setuptools
$ pip install --upgrade wheel
$ pip install -r requirements.txt # Install required packages.
$ pip install -r requirements-dev.txt # Install required dev packages.
Documents generation
Before a commit, generate documents if any docstring has been changed
rm -rf docs
pdoc --html --config show_source_code=False -f -o ./docs sktmls
Version
sktmls package version is automatically genereated followd by a production release on format YY.MM.DD
We use Calendar Versioning. For version available, see the tags on this repository.
1 maintainer
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