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 2020.12.16rc42

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 2020.12.16rc42
File Size Uploaded
sktmls-2020.12.16rc42.tar.gz 462.4 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for sktmls 2020.12.16rc42
File Interpreter ABI Platform
sktmls-2020.12.16rc42-py3-none-any.whl Python 3 none any Details

Total release size: 1.1 MB

Release files / sktmls-2020.12.16rc42.tar.gz

Download URL sktmls-2020.12.16rc42.tar.gz
Size 462.4 kB
Tags Source
SHA-256 checksum
How to use checksums
1e3dee6196d30cd8f689ef85c57b85510ea1a97cd9eb55f8ba6d7a15665bfa87
BLAKE2b-256 checksum
How to use checksums
f5d50072e5383c8f71711de2940b8b9fa9306b1d7cca59c620a5e56624683a5b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.0.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.8.6

Release files / sktmls-2020.12.16rc42-py3-none-any.whl

Download URL sktmls-2020.12.16rc42-py3-none-any.whl
Size 637.3 kB
Tags Python 3
SHA-256 checksum
How to use checksums
3d7f25f70790a767a450ae6ef009f68d16db0d116a9374e506e83b613488f5c2
BLAKE2b-256 checksum
How to use checksums
eb7272df6ccacdf467383e25da71a7f36fdad619bf3125167e67969efc2685c1
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.0.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.8.6

Release history Release notifications | RSS feed

This release

2020.12.16rc42 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