kopia lustrzana https://github.com/OpenDroneMap/ODM
Piero Toffanin
GitHub
commit
6a3f7005f0
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@ -0,0 +1,32 @@
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name: Publish Docker GPU Images
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on:
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push:
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branches:
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- master
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tags:
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- v*
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jobs:
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build:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v2
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- name: Set up QEMU
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uses: docker/setup-qemu-action@v1
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v1
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- name: Login to DockerHub
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uses: docker/login-action@v1
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with:
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username: ${{ secrets.DOCKERHUB_USERNAME }}
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password: ${{ secrets.DOCKERHUB_TOKEN }}
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- name: Build and push Docker image
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id: docker_build
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uses: docker/build-push-action@v2
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with:
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file: ./gpu.Dockerfile
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platforms: linux/amd64
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push: true
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tags: opendronemap/odm:gpu
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40
README.md
40
README.md
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@ -99,6 +99,46 @@ snap run opendronemap
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Snap packages will be kept up-to-date automatically, so you don't need to update ODM manually.
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## GPU Acceleration
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ODM has support for doing SIFT feature extraction on a GPU, which is about 2x faster CPU on a consumer laptop. To use this feature, you need to use the `opendronemap/odm:gpu` docker image instead of `opendronemap/odm` and you need to pass the `--gpus all` flag to docker:
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```
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docker run -ti --rm -v c:/Users/youruser/datasets:/datasets --gpus all opendronemap/odm:gpu --project-path /datasets project
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```
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When you run ODM, if the GPU is recognized, in the first few lines of the output log you should notice:
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```
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[INFO] Writing exif overrides
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[INFO] Maximum photo dimensions: 4000px
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[INFO] Found GPU device: Intel(R) OpenCL HD Graphics
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[INFO] Using GPU for extracting SIFT features
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```
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The implementation is OpenCL-based, so the acceleration should work with most graphics card (not just NVIDIA).
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If you have an NVIDIA card, you can test that docker is recognizing the GPU by running:
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```
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docker run --rm --gpus all nvidia/cuda:10.0-base nvidia-smi
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```
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If you see an output that looks like this:
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```
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Fri Jul 24 18:51:55 2020
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+-----------------------------------------------------------------------------+
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| NVIDIA-SMI 440.82 Driver Version: 440.82 CUDA Version: 10.2 |
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|-------------------------------+----------------------+----------------------+
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| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
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| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
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```
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You're in good shape!
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See https://github.com/NVIDIA/nvidia-docker and https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker for information on docker/NVIDIA setup.
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## WSL or WSL2 Install
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Note: This requires that you have installed WSL already by following [the instructions on Microsoft's Website](https://docs.microsoft.com/en-us/windows/wsl/install-win10).
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@ -9,7 +9,7 @@ ExternalProject_Add(${_proj_name}
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#--Download step--------------
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DOWNLOAD_DIR ${SB_DOWNLOAD_DIR}
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GIT_REPOSITORY https://github.com/OpenDroneMap/OpenSfM/
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GIT_TAG 244
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GIT_TAG 246
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#--Update/Patch step----------
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UPDATE_COMMAND git submodule update --init --recursive
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#--Configure step-------------
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2
VERSION
2
VERSION
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@ -1 +1 @@
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2.4.5
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2.4.7
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@ -120,6 +120,9 @@ install() {
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pip install --ignore-installed -r requirements.txt
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if [ ! -z "$GPU_INSTALL" ]; then
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pip install --ignore-installed -r requirements.gpu.txt
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fi
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if [ ! -z "$PORTABLE_INSTALL" ]; then
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echo "Replacing g++ and gcc with our scripts for portability..."
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@ -0,0 +1,49 @@
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FROM nvidia/cuda:11.2.0-runtime-ubuntu20.04 AS builder
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# Env variables
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ENV DEBIAN_FRONTEND=noninteractive \
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PYTHONPATH="$PYTHONPATH:/code/SuperBuild/install/lib/python3.8/dist-packages:/code/SuperBuild/src/opensfm" \
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LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/code/SuperBuild/install/lib"
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# Prepare directories
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WORKDIR /code
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# Copy everything
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COPY . ./
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# Run the build
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RUN PORTABLE_INSTALL=YES GPU_INSTALL=YES bash configure.sh install
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# Clean Superbuild
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RUN bash configure.sh clean
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### END Builder
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### Use a second image for the final asset to reduce the number and
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# size of the layers.
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FROM nvidia/cuda:11.2.0-runtime-ubuntu20.04
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# Env variables
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ENV DEBIAN_FRONTEND=noninteractive \
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PYTHONPATH="$PYTHONPATH:/code/SuperBuild/install/lib/python3.8/dist-packages:/code/SuperBuild/src/opensfm" \
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LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/code/SuperBuild/install/lib"
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WORKDIR /code
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# Copy everything we built from the builder
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COPY --from=builder /code /code
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# Copy the Python libraries installed via pip from the builder
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COPY --from=builder /usr/local /usr/local
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# Install OpenCL Drivers
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RUN apt update && apt install -y nvidia-opencl-icd-340 intel-opencl-icd
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# Install shared libraries that we depend on via APT, but *not*
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# the -dev packages to save space!
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RUN bash configure.sh installruntimedepsonly \
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&& apt-get clean \
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&& rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
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# Entry point
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ENTRYPOINT ["python3", "/code/run.py"]
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from opendm import log
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from repoze.lru import lru_cache
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@lru_cache(maxsize=None)
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def has_gpus():
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try:
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import pyopencl
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except:
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log.ODM_INFO("PyOpenCL is missing (not a GPU build)")
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return False
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try:
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platforms = pyopencl.get_platforms()
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for p in platforms:
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log.ODM_INFO("Found GPU device: %s" % p.name)
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return len(platforms) > 0
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except Exception as e:
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return False
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@ -17,7 +17,7 @@ from opensfm.actions import undistort
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from opensfm.dataset import DataSet
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from opensfm import report
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from opendm.multispectral import get_photos_by_band
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from opendm.gpu import has_gpus
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class OSFMContext:
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def __init__(self, opensfm_project_path):
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log.ODM_WARNING("Using BOW matching, will use HAHOG feature type, not SIFT")
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feature_type = "HAHOG"
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# GPU acceleration?
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if has_gpus() and feature_type == "SIFT":
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log.ODM_INFO("Using GPU for extracting SIFT features")
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log.ODM_INFO("--min-num-features will be ignored")
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feature_type = "SIFT_GPU"
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config.append("feature_type: %s" % feature_type)
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if has_alt:
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silx>=0.12.0
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pyopencl==2021.1.1
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@ -90,6 +90,7 @@ fi
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export PORT="${PORT:=3000}"
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export QTC="${QTC:=NO}"
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export IMAGE="${IMAGE:=opendronemap/nodeodm}"
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export GPU="${GPU:=NO}"
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if [ -z "$DATA" ]; then
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echo "Usage: DATA=/path/to/datasets [VARS] $0"
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echo " DATA Path to directory that contains datasets for testing. The directory will be mounted in /datasets. If you don't have any, simply set it to a folder outside the ODM repository."
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echo " PORT Port to expose for NodeODM (default: $PORT)"
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echo " IMAGE Docker image to use (default: $IMAGE)"
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echo " GPU Enable GPU support (default: $GPU)"
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echo " QTC When set to YES, installs QT Creator for C++ development (default: $QTC)"
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exit 1
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fi
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echo "Expose port: $PORT"
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echo "QT Creator: $QTC"
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echo "Image: $IMAGE"
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echo "GPU: $GPU"
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if [ ! -e "$HOME"/.odm-dev-home ]; then
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mkdir -p "$HOME"/.odm-dev-home
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USER_ID=$(id -u)
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GROUP_ID=$(id -g)
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USER=$(id -un)
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GPU_FLAG=""
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if [[ "$GPU" != "NO" ]]; then
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GPU_FLAG="--gpus all"
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fi
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xhost + || true
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docker run -ti --entrypoint bash --name odmdev -v $(pwd):/code -v "$DATA":/datasets -p $PORT:3000 --privileged -e DISPLAY -e LANG=C.UTF-8 -e LC_ALL=C.UTF-8 -v="/tmp/.X11-unix:/tmp/.X11-unix:rw" -v="$HOME/.odm-dev-home:/home/$USER" $IMAGE -c "/code/start-dev-env.sh --setup $USER $USER_ID $GROUP_ID $QTC"
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docker run -ti --entrypoint bash --name odmdev -v $(pwd):/code -v "$DATA":/datasets -p $PORT:3000 $GPU_FLAG --privileged -e DISPLAY -e LANG=C.UTF-8 -e LC_ALL=C.UTF-8 -v="/tmp/.X11-unix:/tmp/.X11-unix:rw" -v="$HOME/.odm-dev-home:/home/$USER" $IMAGE -c "/code/start-dev-env.sh --setup $USER $USER_ID $GROUP_ID $QTC"
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exit 0
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