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wtrainclient

가상환경 설정

pyenv install 3.8.18
pyenv virtualenv 3.8.18 wtrainclient3.8
pyenv activate wtrainclient3.8

mlflow, minio 실행

cd docker
docker-compose up -d --build

환경 변수 설정

프로젝트를 실행하기 전에 아래의 환경 변수들을 설정해야 합니다:

환경변수 설명 예시
PROFILE 개발/운영 환경설정, 개발환경에서는 모델을 실제로 업르도하지 않는다 운영: "prod" or "production", 개발: 그 외
MLFLOW_S3_ENDPOINT_URL MLflow가 저장소로 사용하고있는 MinIO 엔드포인트 URL http://localhost:9000
MLFLOW_TRACKING_URI MLflow 트래킹 서버의 URI http://localhost:5001
AWS_ACCESS_KEY_ID MinIO 서버 접근을 위한 AWS 호환 액세스 키 minio
AWS_SECRET_ACCESS_KEY MinIO 서버 접근을 위한 AWS 호환 시크릿 액세스 키 miniostorage
RABBIT_ENDPOINT_URL MinIO 서버에 모델 업로드 후 path 를 발행할 RMQ 엔드포인트 URL amqp://guest:guest@localhost:5672/
RABBIT_MODEL_UPLOAD_TOPIC 모델 업로드 path 를 전달할 토픽 train.model.uploaded
TRAIN_ID train_id (학습 서버에서 넣어주는 값) 1
MODEL_NAME model_name (학습 서버에서 넣어주는 값) my_model

Metadata

Release files for wtu-mlflow 0.1.9

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Source distribution for wtu-mlflow 0.1.9
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Built distribution (wheel)

Table of built distributions (wheels) for wtu-mlflow 0.1.9
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wtu_mlflow-0.1.9-py3-none-any.whl Python 3 none any Details

Total release size: 25.6 kB

Release files / wtu-mlflow-0.1.9.tar.gz

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