Metadata-Version: 2.1
Name: sktmls
Version: 2020.12.2
Summary: MLS SDK
Home-page: https://github.com/sktaiflow/mls-sdk
Author: SKTMLS
Author-email: mls@sktai.io
License: UNKNOWN
Description: # mls-model-registry (sktmls)
        
        ## Contents
        
        - [Description](#description)
        - [How to use](#how-to-use)
        - [Development](#development)
          - [Requirements for development](#requirements-for-development)
          - [Local model registry](#local-model-registry)
          - [Python environment](#python-environment)
          - [Documents generation](#documents-generation)
        - [Version](#version)
        
        ## 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,
        
        ```bash
        # 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.
        
        ```bash
        $ cd ~/
        $ mkdir models
        ```
        
        ### Python environment
        
        First you need to do the followings
        
        ```bash
        $ python -V # Check if the version is 3.6.
        $ python -m venv env # Create a virtualenv.
        $ . env/bin/activate # Activate the env.
        $ 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
        
        ```bash
        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](https://calver.org). For version available, see the [tags on this repository](https://github.com/sktaiflow/mls-model-registry/releases).  
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
