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makina-runway

Project description

Software Development Kit

  • support only python

Goal

  • mlflow를 사용하는 사용자들이 import만 변경하여 runway에 logging하는 방식을 제공한다.
  • runway sdk 는 mlflow 의 interface/arguments 를 최대한 맞추는 방향으로 구현해야 한다.
# import mlflow
from runway import mlflow

with mlflow.start_run() as run:
    mlflow.log_param()

mlflow.sklearn.save_model()

Install

  • makina 사내 pypi server에서 install 합니다.
  1. pip install

    # pip install
    pip install --index-url http://pypi.makina.rocks/simple/ --trusted-host pypi.makina.rocks runway
    
    pyproject.toml의 version 수정 후 테스트 시
    pip install --index-url http://pypi.makina.rocks/simple/ --trusted-host pypi.makina.rocks mrx-runway=={{version}}
    
  2. poetry 사용

    1. pyproject.toml에 사내 pypi source 추가

      [[tool.poetry.source]]
      name = "mrx"
      url = "http://pypi.makina.rocks/simple/"
      default = true
      
    2. poetry add & install

      poetry add runway
      poetry install
      

Publish

  • 정식 version은 runway repo의 tag로 관리됨
  • pyproject.toml 파일의 version은 developer의 test를 위한 pseudo-version 임
  • local에서 publish test는 make command 사용
    # version patch, build & publish 한꺼번에 수행
    make publish-dev
    

How to test in local environment

  • sdk 개발시 local 에서 unit test 실행방식은 아래와 같습니다. utest 시에는 다양한 mlframework package 가 설치되니, 별도의 명령어보다는 makefile 의 recipe 를 사용하시기 바랍니다.
make utest
```bash

## How to develop
- sdk 개발을 위한 `python` 환경을 하나  생성합니다.
- edit 모드로 sdk를 설치 합니다.
```bash
pip install -e .
  • /etc/hosts에 minio관련 host를 추가합니다.
  • misc/gw.yaml, misc/vs.yaml을 수정하여 배포합니다.
  • prepare_develop.sh파일에 BACKEND_URL, MLFLOW_URL, MINIO_URL를 환경에 맞게 수정합니다.
  • prepare_develop.sh를 실행합니다.
./prepare_develop.sh
  • 실행
DEPLOY_TARGET=dev python examples/sample_log_model.py

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