[Fix] point cloud export broken in Dockerfile (#1148)

* Added Dockerfile to build docker image including all dependencies and nerfstudio, added description on how to user docker.

* Move docker description to Installation.md.

* Fix mask_filename indices after splitting train set.

* Rebase DOckerfile to newer image for updated libraries.

* Update docker container documentation.

Co-authored-by: Nicolas Zunhammer <[email protected]>

Dockerfile

@@ -1,5 +1,5 @@
 # Define base image.
-FROM nvidia/cudagl:11.3.1-devel
+FROM nvidia/cuda:11.7.1-devel-ubuntu22.04
 
 # Set environment variables.
 ## Set non-interactive to prevent asking for user inputs blocking image creation.
@@ -11,7 +11,7 @@ ENV TCNN_CUDA_ARCHITECTURES=86
 ## CUDA Home, required to find CUDA in some packages.
 ENV CUDA_HOME="/usr/local/cuda"
 
-# Install required apt packages.
+# Install required apt packages and clear cache afterwards.
 RUN apt-get update && \
     apt-get install -y --no-install-recommends \
     build-essential \
@@ -36,10 +36,11 @@ RUN apt-get update && \
     libsuitesparse-dev \
     nano \
     protobuf-compiler \
-    python3.8-dev \
+    python3.10-dev \
     python3-pip \
     qtbase5-dev \
-    wget
+    wget && \
+    rm -rf /var/lib/apt/lists/*
 
 # Install GLOG (required by ceres).
 RUN git clone --branch v0.6.0 https://github.com/google/glog.git --single-branch && \
@@ -50,7 +51,7 @@ RUN git clone --branch v0.6.0 https://github.com/google/glog.git --single-branch
     make -j && \
     make install && \
     cd ../.. && \
-    rm -r glog
+    rm -rf glog
 # Add glog path to LD_LIBRARY_PATH.
 ENV LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/lib"
 
@@ -64,18 +65,19 @@ RUN git clone --branch 2.1.0 https://ceres-solver.googlesource.com/ceres-solver.
     make -j && \
     make install && \
     cd ../.. && \
-    rm -r ceres-solver
+    rm -rf 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 .. && \
+    cmake .. -DCUDA_ENABLED=ON \
+        	 -DCUDA_NVCC_FLAGS="--std c++14" && \
     make -j && \
     make install && \
     cd ../.. && \
-    rm -r colmap
+    rm -rf colmap
 
 # Create non root user and setup environment.
 RUN useradd -m -d /home/user -u 1000 user
@@ -89,11 +91,11 @@ 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
+RUN python3.10 -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
+RUN python3.10 -m pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu116
 # Install tynyCUDNN.
-RUN python3.8 -m pip install git+https://github.com/NVlabs/tiny-cuda-nn.git#subdirectory=bindings/torch
+RUN python3.10 -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
@@ -103,7 +105,7 @@ USER 1000:1000
 
 # Install nerfstudio dependencies.
 RUN cd nerfstudio && \
-    python3.8 -m pip install -e . && \
+    python3.10 -m pip install -e . && \
     cd ..
 
 # Change working directory

docs/quickstart/installation.md

@@ -66,7 +66,7 @@ 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 ([get 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.
+Docker ([get 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 11.7, must be installed.
 The docker image can then either be pulled from [here](https://hub.docker.com/r/dromni/nerfstudio/tags) (replace <version> with the actual version, e.g. 0.1.10)
 ```bash
 docker pull dromni/nerfstudio:<version>
@@ -89,15 +89,18 @@ docker run --gpus all \                                         # Give the conta
 ### 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/<YOUR_USER>/.cache/:/home/user/.cache/ -p 7007:7007 --rm # Parameters.
+docker run --gpus all -v /folder/of/your/data:/workspace/ -v /home/<YOUR_USER>/.cache/:/home/user/.cache/ -p 7007:7007 --rm -it # 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.
+- Paths on Windows use backslash '\\' while unix based systems use a frontslash '/' for paths, where backslashes might require an escape character depending on where they are used (e.g. C:\\\\folder1\\\\folder2...). Ensure to use the correct paths when mounting folders or providing paths as parameters.
 - 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.
+- 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/cuda:11.7.1-devel-ubuntu22.04, consequently it comes with CUDA 11.7 which must be supported by the nvidia driver. No local CUDA installation is required or will be affected by using the docker image.
+- The docker image (respectively Ubuntu 22.04) comes with Python3.10, no older version of Python is installed.
+- If you call the container with commands directly, you still might want to add the interactive terminal ('-it') flag to get live log outputs of the nerfstudio scripts. In case the container is used in an automated environment the flag should be discarded.
 
 
 ## Installation FAQ