gaussian splatting

Dockerfile

@@ -1,155 +1,63 @@
-# syntax=docker/dockerfile:1
-ARG UBUNTU_VERSION=22.04
-ARG NVIDIA_CUDA_VERSION=11.8.0
-# CUDA architectures, required by Colmap and tiny-cuda-nn. Use >= 8.0 for faster TCNN.
-ARG CUDA_ARCHITECTURES="90;89;86;80;75;70;61"
-ARG NERFSTUDIO_VERSION=""
-
-# Pull source either provided or from git.
-FROM scratch as source_copy
-ONBUILD COPY . /tmp/nerfstudio
-FROM alpine/git as source_no_copy
-ARG NERFSTUDIO_VERSION
-ONBUILD RUN git clone --branch ${NERFSTUDIO_VERSION} --recursive https://github.com/nerfstudio-project/nerfstudio.git /tmp/nerfstudio
-ARG NERFSTUDIO_VERSION
-FROM source_${NERFSTUDIO_VERSION:+no_}copy as source
-
-FROM nvidia/cuda:${NVIDIA_CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} as builder
-ARG CUDA_ARCHITECTURES
-ARG NVIDIA_CUDA_VERSION
-ARG UBUNTU_VERSION
+FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04
 
 ENV DEBIAN_FRONTEND=noninteractive
-ENV QT_XCB_GL_INTEGRATION=xcb_egl
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends --no-install-suggests \
-        git \
-        wget \
-        ninja-build \
-        build-essential \
-        libboost-program-options-dev \
-        libboost-filesystem-dev \
-        libboost-graph-dev \
-        libboost-system-dev \
-        libeigen3-dev \
-        libflann-dev \
-        libfreeimage-dev \
-        libmetis-dev \
-        libgoogle-glog-dev \
-        libgtest-dev \
-        libsqlite3-dev \
-        libglew-dev \
-        qtbase5-dev \
-        libqt5opengl5-dev \
-        libcgal-dev \
-        libceres-dev \
-        python3.10-dev \
-        python3-pip
-
-# Build and install CMake
-RUN wget https://github.com/Kitware/CMake/releases/download/v3.31.3/cmake-3.31.3-linux-x86_64.sh \
-    -q -O /tmp/cmake-install.sh \
-    && chmod u+x /tmp/cmake-install.sh \
-    && mkdir /opt/cmake-3.31.3 \
-    && /tmp/cmake-install.sh --skip-license --prefix=/opt/cmake-3.31.3 \
-    && rm /tmp/cmake-install.sh \
-    && ln -s /opt/cmake-3.31.3/bin/* /usr/local/bin
-
-# Build and install GLOMAP.
-RUN git clone https://github.com/colmap/glomap.git && \
-    cd glomap && \
-    git checkout "1.0.0" && \
-    mkdir build && \
-    cd build && \
-    mkdir -p /build && \
-    cmake .. -GNinja "-DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCHITECTURES}" \
-        -DCMAKE_INSTALL_PREFIX=/build/glomap && \
-    ninja install -j1 && \
-    cd ~
-
-# Build and install COLMAP.
-RUN git clone https://github.com/colmap/colmap.git && \
-    cd colmap && \
-    git checkout "3.9.1" && \
-    mkdir build && \
-    cd build && \
-    mkdir -p /build && \
-    cmake .. -GNinja "-DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCHITECTURES}" \
-        -DCMAKE_INSTALL_PREFIX=/build/colmap && \
-    ninja install -j1 && \
-    cd ~
-
-# Upgrade pip and install dependencies.
-# pip install torch==2.2.2 torchvision==0.17.2 --index-url https://download.pytorch.org/whl/cu118 && \
-RUN pip install --no-cache-dir --upgrade pip 'setuptools<70.0.0' && \
-    pip install --no-cache-dir torch==2.1.2+cu118 torchvision==0.16.2+cu118 'numpy<2.0.0' --extra-index-url https://download.pytorch.org/whl/cu118 && \
-    git clone --branch master --recursive https://github.com/cvg/Hierarchical-Localization.git /opt/hloc && \
-    cd /opt/hloc && git checkout v1.4 && python3.10 -m pip install --no-cache-dir . && cd ~ && \
-    TCNN_CUDA_ARCHITECTURES="${CUDA_ARCHITECTURES}" pip install --no-cache-dir "git+https://github.com/NVlabs/tiny-cuda-nn.git@b3473c81396fe927293bdfd5a6be32df8769927c#subdirectory=bindings/torch" && \
-    pip install --no-cache-dir pycolmap==0.6.1 pyceres==2.1 omegaconf==2.3.0
-
-# Install gsplat and nerfstudio.
-# NOTE: both are installed jointly in order to prevent docker cache with latest
-# gsplat version (we do not expliticly specify the commit hash).
-#
-# We set MAX_JOBS to reduce resource usage for GH actions:
-# - https://github.com/nerfstudio-project/gsplat/blob/db444b904976d6e01e79b736dd89a1070b0ee1d0/setup.py#L13-L23
-COPY --from=source /tmp/nerfstudio/ /tmp/nerfstudio
-RUN export TORCH_CUDA_ARCH_LIST="$(echo "$CUDA_ARCHITECTURES" | tr ';' '\n' | awk '$0 > 70 {print substr($0,1,1)"."substr($0,2)}' | tr '\n' ' ' | sed 's/ $//')" && \
-    export MAX_JOBS=4 && \
-    GSPLAT_VERSION="$(sed -n 's/.*gsplat==\s*\([^," '"'"']*\).*/\1/p' /tmp/nerfstudio/pyproject.toml)" && \
-    pip install --no-cache-dir git+https://github.com/nerfstudio-project/gsplat.git@v${GSPLAT_VERSION} && \
-    pip install --no-cache-dir /tmp/nerfstudio 'numpy<2.0.0' && \
-    rm -rf /tmp/nerfstudio
-
-# Fix permissions
-RUN chmod -R go=u /usr/local/lib/python3.10 && \
-    chmod -R go=u /build
-
-#
-# Docker runtime stage.
-#
-FROM nvidia/cuda:${NVIDIA_CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} as runtime
-ARG CUDA_ARCHITECTURES
-ARG NVIDIA_CUDA_VERSION
-ARG UBUNTU_VERSION
-
-LABEL org.opencontainers.image.source = "https://github.com/nerfstudio-project/nerfstudio"
-LABEL org.opencontainers.image.licenses = "Apache License 2.0"
-LABEL org.opencontainers.image.base.name="docker.io/library/nvidia/cuda:${NVIDIA_CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}"
-LABEL org.opencontainers.image.documentation = "https://docs.nerf.studio/"
-
-# Minimal dependencies to run COLMAP binary compiled in the builder stage.
-# Note: this reduces the size of the final image considerably, since all the
-# build dependencies are not needed.
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends --no-install-suggests \
-        libboost-filesystem1.74.0 \
-        libboost-program-options1.74.0 \
-        libc6 \
-        libceres2 \
-        libfreeimage3 \
-        libgcc-s1 \
-        libgl1 \
-        libglew2.2 \
-        libgoogle-glog0v5 \
-        libqt5core5a \
-        libqt5gui5 \
-        libqt5widgets5 \
-        python3.10 \
-        python3.10-dev \
-        build-essential \
-        python-is-python3 \
-        ffmpeg
-
-# Copy packages from builder stage.
-COPY --from=builder /build/colmap/ /usr/local/
-COPY --from=builder /build/glomap/ /usr/local/
-COPY --from=builder /usr/local/lib/python3.10/dist-packages/ /usr/local/lib/python3.10/dist-packages/
-COPY --from=builder /usr/local/bin/ns* /usr/local/bin/
-
-# Install nerfstudio cli auto completion
-RUN /bin/bash -c 'ns-install-cli --mode install'
-
-# Bash as default entrypoint.
-CMD /bin/bash -l
+ENV CONDA_DIR=/opt/conda
+ENV PATH=$CONDA_DIR/bin:$PATH
+
+# setup
+RUN apt-get update && apt-get install -y wget curl bzip2 git && apt-get clean
+
+# miniconda
+RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
+    bash Miniconda3-latest-Linux-x86_64.sh -b -p $CONDA_DIR && \
+    rm Miniconda3-latest-Linux-x86_64.sh
+
+# conda environment
+RUN conda create -n nerfstudio -y python=3.8
+
+# PyTorch, torchvision, ninja 
+RUN /bin/bash -c "source $CONDA_DIR/bin/activate nerfstudio && \
+    python -m pip install --upgrade pip && \
+    pip install ninja torch==2.1.2+cu118 torchvision==0.16.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118"
+
+# cuda arquitectures
+ENV TCNN_CUDA_ARCHITECTURES="70,75,80,86,89"
+
+# tiny-cuda-nn 
+RUN /bin/bash -c "source $CONDA_DIR/bin/activate nerfstudio && \
+    pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch"
+
+# pixi 
+RUN /bin/bash -c "source $CONDA_DIR/bin/activate nerfstudio && \
+    curl -fsSL https://pixi.sh/install.sh | bash && \
+    source ~/.bashrc && \
+    pixi --version"
+
+RUN echo "source activate nerfstudio" >> ~/.bashrc
+
+SHELL ["/bin/bash", "-c"]
+CMD ["bash", "-l"]
+
+# paso 16 desde el shell
+# git clone https://github.com/nerfstudio-project/nerfstudio.git
+# cd nerfstudio
+# pixi run post-install
+# pixi shell
+# apt update
+# apt install libgl1-mesa-glx -y
+# pixi run train-example-nerf
+# http://localhost:7007
+
+# pip install gsplat==1.4.0
+# python -c "import gsplat; print('gsplat installed successfully')"
+
+# Process video data
+# ns-process-data video --data ./pista.MP4 --sfm-tool hloc --output-dir ./processed
+# Train model
+# ns-train splatfacto --pipeline.model.cull_alpha_thresh=0.005 --pipeline.model.stop-screen-size-at=15000 --pipeline.model.use_scale_regularization=True --data ./processed
+# view training
+# ns-viewer --load-config outputs/processed/splatfacto/2025-03-11_210124/config.yml`
+# http://0.0.0.0:7007
+#  remplace 0.0.0.0 -> docker inspect container_id | grep "IPAddress"
+
+# change swap memory to 15 gb (if needed)