W-Train Utils for MLflow Triton Plugin
Project description
w-train-utils-mlflow-triton-plugin
가상환경 설정
pyenv install 3.8.18
pyenv virtualenv 3.8.18 wtrainclient3.8
pyenv activate wtrainclient3.8
Triton Inference Server 실행
$ docker run --rm -p8000:8000 -p8001:8001 -p8002:8002 \
-e AWS_ACCESS_KEY_ID=<AccessKey> \
-e AWS_SECRET_ACCESS_KEY=<SecretKey> \
nvcr.io/nvidia/tritonserver:24.01-py3 \
tritonserver --model-repository=s3://https://kitech-minio-api.wimcorp.dev:443/triton \
--model-control-mode=explicit \
--log-verbose=1
환경 변수 설정
프로젝트를 실행하기 전에 아래의 환경 변수들을 설정해야 합니다:
환경변수 | 설명 | 예시 |
---|---|---|
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 |
TRITON_URL | Triton Inference Server 의 grpc 엔드포인트 URL | http://localhost:8001 |
TRITON_MODEL_REPO | Triton Inference Server 의 모델저장소 URL | s3://http://localhost:9000/triton |
패키지 빌드 및 업로드
# 필요한 의존성 설치
pip install wheel setuptools twine
vi ~/.pypirc
[distutils]
index-servers =
pypi
pypi-repository
[pypi]
username = __token__
password = <token>
[pypi-repository]
repository: https://<domain>/repository/<pypi-hosted>/
username: <username>
password: <password>
sh scripts/build.sh
sh scripts/deploy.sh
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