# wtrainclient
## 가상환경 설정
```sh
pyenv install 3.8.18
pyenv virtualenv 3.8.18 wtrainclient3.8
pyenv activate wtrainclient3.8
```
---
## mlflow, minio 실행
```sh
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 |
---
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