diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 000000000..9cfcb77b6 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,114 @@ +# Define base image. +FROM nvidia/cudagl:11.3.1-devel + +# Set environment variables. +## Set non-interactive to prevent asking for user inputs blocking image creation. +ENV DEBIAN_FRONTEND=noninteractive +## Set timezone as it is required by some packages. +ENV TZ=Europe/Berlin +## CUDA architectures, required by tiny-cuda-nn. +ENV TCNN_CUDA_ARCHITECTURES=86 +## CUDA Home, required to find CUDA in some packages. +ENV CUDA_HOME="/usr/local/cuda" + +# Install required apt packages. +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + build-essential \ + cmake \ + ffmpeg \ + git \ + libatlas-base-dev \ + libboost-filesystem-dev \ + libboost-graph-dev \ + libboost-program-options-dev \ + libboost-system-dev \ + libboost-test-dev \ + libcgal-dev \ + libeigen3-dev \ + libfreeimage-dev \ + libgflags-dev \ + libglew-dev \ + libgoogle-glog-dev \ + libmetis-dev \ + libprotobuf-dev \ + libqt5opengl5-dev \ + libsuitesparse-dev \ + nano \ + protobuf-compiler \ + python3.8-dev \ + python3-pip \ + qtbase5-dev \ + wget + +# Install GLOG (required by ceres). +RUN git clone --branch v0.6.0 https://github.com/google/glog.git --single-branch && \ + cd glog && \ + mkdir build && \ + cd build && \ + cmake .. && \ + make -j && \ + make install && \ + cd ../.. && \ + rm -r glog +# Add glog path to LD_LIBRARY_PATH. +ENV LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/lib" + +# Install Ceres-solver (required by colmap). +RUN git clone --branch 2.1.0 https://ceres-solver.googlesource.com/ceres-solver.git --single-branch && \ + cd ceres-solver && \ + git checkout $(git describe --tags) && \ + mkdir build && \ + cd build && \ + cmake .. -DBUILD_TESTING=OFF -DBUILD_EXAMPLES=OFF && \ + make -j && \ + make install && \ + cd ../.. && \ + rm -r ceres-solver + +# Install colmap. +RUN git clone --branch 3.7 https://github.com/colmap/colmap.git --single-branch && \ + cd colmap && \ + mkdir build && \ + cd build && \ + cmake .. && \ + make -j && \ + make install && \ + cd ../.. && \ + rm -r colmap + +# Create non root user and setup environment. +RUN useradd -m -d /home/user -u 1000 user + +# Switch to new uer and workdir. +USER 1000:1000 +WORKDIR /home/user + +# Add local user binary folder to PATH variable. +ENV PATH="${PATH}:/home/user/.local/bin" +SHELL ["/bin/bash", "-c"] + +# Upgrade pip and install packages. +RUN python3.8 -m pip install --upgrade pip setuptools pathtools promise +# Install pytorch and submodules. +RUN python3.8 -m pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113 +# Install tynyCUDNN. +RUN python3.8 -m pip install git+https://github.com/NVlabs/tiny-cuda-nn.git#subdirectory=bindings/torch + +# Copy nerfstudio folder and give ownership to user. +ADD . /home/user/nerfstudio +USER root +RUN chown -R user:user /home/user/nerfstudio +USER 1000:1000 + +# Install nerfstudio dependencies. +RUN cd nerfstudio && \ + python3.8 -m pip install -e . && \ + cd .. + +# Change working directory +WORKDIR /workspace + +# Install nerfstudio cli auto completion and enter shell if no command was provided. +CMD ns-install-cli --mode install && /bin/bash + diff --git a/README.md b/README.md index beda2bb56..3050b80dd 100644 --- a/README.md +++ b/README.md @@ -119,6 +119,10 @@ pip install --upgrade pip setuptools pip install -e . ``` +**OR** if you want to skip all installation steps and directly start using nerfstudio, use the docker image: + +See [Installation](https://github.com/nerfstudio-project/nerfstudio/blob/main/docs/quickstart/installation.md) - **Use docker image**. + ## 2. Training your first model! The following will train a _nerfacto_ model, our recommended model for real world scenes. diff --git a/docs/quickstart/installation.md b/docs/quickstart/installation.md index d26eb98a1..a7311ff7b 100644 --- a/docs/quickstart/installation.md +++ b/docs/quickstart/installation.md @@ -63,6 +63,43 @@ pip install -e .[dev] pip install -e .[docs] ``` +## Use docker image +Instead of installing and compiling prerequisites, setting up the environment and installing dependencies, a ready to use docker image is provided. \ +### Prerequisites +Docker ([det docker](https://docs.docker.com/get-docker/)) and nvidia GPU drivers ([get nvidia drivers](https://www.nvidia.de/Download/index.aspx?lang=de)), capable of working with CUDA 11.3, must be installed. +The docker image can then either be pulled from [here](https://hub.docker.com/r/dromni/nerfstudio/tags) (replace with the actual version, e.g. 0.1.10) +```bash +docker pull dromni/nerfstudio: +``` +or be built from the repository using +```bash +docker build --tag nerfstudio -f Dockerfile . +``` +### Using an interactive container +The docker container can be launched with an interactive terminal where nerfstudio commands can be entered as usual. Some parameters are required and some are strongly reocmmended for usage as following: +```bash +docker run --gpus all \ # Give the container access to nvidia GPU (required). + -v /folder/of/your/data:/workspace/ \ # Mount a folder from the local machine into the container to be able to process them (required). + -v /home//.cache/:/home/user/.cache/ \ # Mount cache folder to avoid re-downloading of models everytime (recommended). + -p 7007:7007 \ # Map port from local machine to docker container (required to access the web interface/UI). + --rm \ # Remove container after it is closed (recommended). + -it \ # Start container in interactive mode. + nerfstudio # Docker image name +``` +### Call nerfstudio commands directly +Besides, the container can also directly be used by adding the nerfstudio command to the end. +```bash +docker run --gpus all -v /folder/of/your/data:/workspace/ -v /home//.cache/:/home/user/.cache/ -p 7007:7007 --rm # Parameters. + nerfstudio \ # Docker image name + ns-process-data video --data /workspace/video.mp4 # Smaple command of nerfstudio. +``` +### Note +- The container works on Linux and Windows, depending on your OS some additional setup steps might be required to provide access to your GPU inside containers. +- Everything inside the container, what is not in a mounted folder (workspace in the above example), will be permanently removed after destroying the container. Always do all your tasks and output folder in workdir! +- The user inside the container is called user and is mapped to the local user with ID 1000 (usually the first non-root user on Linux systems). +- The container currently is based on nvidia:cudagl-11.3.1, consequently it comes with CUDA 11.3 which must be supported by the nvidia driver. No local CUDA installation is required or will be affected by using the docker image. + + ## Installation FAQ - [TinyCUDA installation errors out with cuda mismatch](tiny-cuda-error)