threestudio 三维模型生成

docker  run   --gpus all  --name=test   -p 7860:7860  -itd  nvcr.io/nvidia/cuda:11.8.0-devel-ubuntu22.04
docker exec  -it  test bash

apt update && apt install python3 -y && apt install python3-pip  -y && apt install git  -y   && apt-get install ffmpeg libsm6 libxext6  -y && apt-get install libgl1 -y && cp   /usr/bin/python3  /usr/bin/python && git  clone https://github.com/threestudio-project/threestudio.git  && cd threestudio && python3 -m pip install --upgrade pip &&  pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118 &&  pip install  -r  requirements.txt &&  pip install  fastapi==0.86 && pip install    gradio==3.48.0  &&  pip install  pydantic &&  pip install   Schema  &&  pip install -U gradio

运行程序
 python  gradio_app.py  launch  --listen

############

官方给出的Dockerfile 

# Reference:
# https://github.com/cvpaperchallenge/Ascender
# https://github.com/nerfstudio-project/nerfstudio

FROM nvidia/cuda:11.8.0-devel-ubuntu22.04

ARG USER_NAME=dreamer
ARG GROUP_NAME=dreamers
ARG UID=1000
ARG GID=1000

# Set compute capability for nerfacc and tiny-cuda-nn
# See https://developer.nvidia.com/cuda-gpus and limit number to speed-up build
ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0 7.5 8.0 8.6 8.9 9.0+PTX"
ENV TCNN_CUDA_ARCHITECTURES=90;89;86;80;75;70;61;60
# Speed-up build for RTX 30xx
# ENV TORCH_CUDA_ARCH_LIST="8.6"
# ENV TCNN_CUDA_ARCHITECTURES=86
# Speed-up build for RTX 40xx
# ENV TORCH_CUDA_ARCH_LIST="8.9"
# ENV TCNN_CUDA_ARCHITECTURES=89

ENV CUDA_HOME=/usr/local/cuda
ENV PATH=${CUDA_HOME}/bin:/home/${USER_NAME}/.local/bin:${PATH}
ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
ENV LIBRARY_PATH=${CUDA_HOME}/lib64/stubs:${LIBRARY_PATH}

# apt install by root user
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
    build-essential \
    curl \
    git \
    libegl1-mesa-dev \
    libgl1-mesa-dev \
    libgles2-mesa-dev \
    libglib2.0-0 \
    libsm6 \
    libxext6 \
    libxrender1 \
    python-is-python3 \
    python3.10-dev \
    python3-pip \
    wget \
    && rm -rf /var/lib/apt/lists/*

# Change user to non-root user
RUN groupadd -g ${GID} ${GROUP_NAME} \
    && useradd -ms /bin/sh -u ${UID} -g ${GID} ${USER_NAME}
USER ${USER_NAME}

RUN pip install --upgrade pip setuptools ninja
RUN pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --index-url https://download.pytorch.org/whl/cu118
# Install nerfacc and tiny-cuda-nn before installing requirements.txt
# because these two installations are time consuming and error prone
RUN pip install git+https://github.com/KAIR-BAIR/[email protected]
RUN pip install git+https://github.com/NVlabs/tiny-cuda-nn.git#subdirectory=bindings/torch

COPY requirements.txt /tmp
RUN cd /tmp && pip install -r requirements.txt
WORKDIR /home/${USER_NAME}/threestudio

############

docker-compose

services:
  threestudio:
    build:
      context: ../
      dockerfile: docker/Dockerfile
      args:
        # you can set environment variables, otherwise default values will be used
        USER_NAME: ${HOST_USER_NAME:-dreamer}  # export HOST_USER_NAME=$USER
        GROUP_NAME: ${HOST_GROUP_NAME:-dreamers}
        UID: ${HOST_UID:-1000}  # export HOST_UID=$(id -u)
        GID: ${HOST_GID:-1000}  # export HOST_GID=$(id -g)
      shm_size: '4gb'
    environment:
      NVIDIA_DISABLE_REQUIRE: 1  # avoid wrong `nvidia-container-cli: requirement error`
    tty: true
    volumes:
        - ../:/home/${HOST_USER_NAME:-dreamer}/threestudio
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              capabilities: [gpu]