Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

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

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 "numpy>=1.19.4,<1.20" # Install numpy to avoid a requirement error.
$ 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.

Release files for sktmls 2020.12.16rc44

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for sktmls 2020.12.16rc44
File Size Uploaded
sktmls-2020.12.16rc44.tar.gz 462.7 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for sktmls 2020.12.16rc44
File Interpreter ABI Platform
sktmls-2020.12.16rc44-py3-none-any.whl Python 3 none any Details

Total release size: 1.1 MB

Release files / sktmls-2020.12.16rc44.tar.gz

Download URL sktmls-2020.12.16rc44.tar.gz
Size 462.7 kB
Tags Source
SHA-256 checksum
How to use checksums
012860d9b53e869219a7e96bbf2a81786565474c2167e61b51ab5eaa50402460
BLAKE2b-256 checksum
How to use checksums
0337a8715b8d98c6075ad94717a76347d9767710f18818e668ea7926491e5905
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.0 CPython/3.8.7

Release files / sktmls-2020.12.16rc44-py3-none-any.whl

Download URL sktmls-2020.12.16rc44-py3-none-any.whl
Size 637.6 kB
Tags Python 3
SHA-256 checksum
How to use checksums
3badc9b82470e22cf3be77a63e9cb5b8a4c1a2c040bc58c774ed139837b55d6e
BLAKE2b-256 checksum
How to use checksums
02bc4d67982321ed856f88a6a42d80d7cfd9a1c1de43822acf91defc7b5c0c2e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.0 CPython/3.8.7

Release history Release notifications | RSS feed

This release

2020.12.16rc44 This release

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page