jeonghyo.k
3946ff6a25
- prediction/image/ FastAPI 서버 Docker 환경 구성 - Dockerfile: PyTorch 2.1 + CUDA 12.1 기반 GPU 이미지 - docker-compose.yml: GPU 할당 + 데이터 볼륨 마운트 - requirements.txt: 서버 의존성 목록 - .env.example: 환경변수 템플릿 - DOCKER_USAGE.md: 빌드/실행/API 사용법 문서 - Dockerfile에 .dockerignore 제외 폴더 mkdir -p 추가 - .gitignore: prediction/image 결과물 및 모델 가중치(.pth) 제외 추가 - dbInsert_csv.py, dbInsert_shp.py 삭제 (미사용 DB 로직) - api.py: dbInsert import 및 주석 처리된 DB 호출 코드 제거 - aerialRouter.ts: req.params 타입 오류 수정
33 lines
1.1 KiB
Docker
33 lines
1.1 KiB
Docker
ARG PYTORCH="1.11.0"
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ARG CUDA="11.3"
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ARG CUDNN="8"
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FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
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ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0+PTX"
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ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all"
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ENV CMAKE_PREFIX_PATH="$(dirname $(which conda))/../"
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# To fix GPG key error when running apt-get update
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RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
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RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
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RUN apt-get update && apt-get install -y git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx \
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&& apt-get clean \
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&& rm -rf /var/lib/apt/lists/*
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RUN conda clean --all
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# Install MMCV
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ARG PYTORCH
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ARG CUDA
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ARG MMCV
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RUN ["/bin/bash", "-c", "pip install --no-cache-dir mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu${CUDA//./}/torch${PYTORCH}/index.html"]
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# Install MMSegmentation
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RUN git clone https://github.com/open-mmlab/mmsegmentation.git /mmsegmentation
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WORKDIR /mmsegmentation
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ENV FORCE_CUDA="1"
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RUN pip install -r requirements.txt
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RUN pip install --no-cache-dir -e .
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