jetson-containers
aws-lambda-docker-serverless-inference
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jetson-containers | aws-lambda-docker-serverless-inference | |
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10 | 1 | |
1,536 | 91 | |
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9.9 | 4.0 | |
9 days ago | about 2 months ago | |
Python | Jupyter Notebook | |
MIT License | MIT No Attribution |
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jetson-containers
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Install ros 2 humble on jetson orin
https://github.com/dusty-nv/jetson-containers This one might be helpful
I would suggest looking here: https://github.com/dusty-nv/jetson-containers Dusty builds a number of Ros2 containers so might be worth seeing if you can get it to work using some of his build scripts.
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troubles trying to install ros2 on jetson nano
Ah yeah it's just upgrading to Ubuntu 2004 then running through the commands in this dockerfile: https://github.com/dusty-nv/jetson-containers/blob/master/Dockerfile.ros.foxy
Finally, if you don't want the full ros2 desktop on the nano (you may struggle with memory anyway) then jetson containers can run foxy etc with your existing jetpack version. https://github.com/dusty-nv/jetson-containers
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Raspberry Pi4 Good Enough for SLAM?
I had to modify this dockerfile to get pangolin to work, but now it is the ORB_SLAM2_CODA portion that I cannot figure out.
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How to build ZED 2i Camera x ROS2 Foxy x Nvidia Jetson x Ubuntu 18.04 via Docker
# Based on https://gitlab.com/nvidia/container-images/l4t-base/-/blob/master/Dockerfile.l4t # https://github.com/dusty-nv/jetson-containers/blob/master/Dockerfile.ros.foxy # https://github.com/codustry/jetson-containers/blob/master/Dockerfile.ros.foxy ARG L4T_MINOR_VERSION=5.0 FROM codustry/ros:foxy-ros-base-l4t-r32.${L4T_MINOR_VERSION} # # ZED Jetson # https://github.com/stereolabs/zed-docker/blob/master/3.X/jetpack_4.X/devel/Dockerfile # ARG ZED_SDK_MAJOR=3 ARG ZED_SDK_MINOR=5 ARG JETPACK_MAJOR=4 ARG JETPACK_MINOR=5 #This environment variable is needed to use the streaming features on Jetson inside a container ENV LOGNAME root ENV DEBIAN_FRONTEND noninteractive RUN apt-get update -y && apt-get install --no-install-recommends lsb-release wget less udev sudo apt-transport-https build-essential cmake openssh-server libv4l-0 libv4l-dev v4l-utils binutils xz-utils bzip2 lbzip2 curl ca-certificates libegl1 python3 -y && \ echo "# R32 (release), REVISION: 5.0" > /etc/nv_tegra_release ; \ wget -q --no-check-certificate -O ZED_SDK_Linux_JP.run https://download.stereolabs.com/zedsdk/${ZED_SDK_MAJOR}.${ZED_SDK_MINOR}/jp${JETPACK_MAJOR}${JETPACK_MINOR}/jetsons && \ chmod +x ZED_SDK_Linux_JP.run ; ./ZED_SDK_Linux_JP.run silent skip_tools && \ rm -rf /usr/local/zed/resources/* \ rm -rf ZED_SDK_Linux_JP.run && \ rm -rf /var/lib/apt/lists/* #This symbolic link is needed to use the streaming features on Jetson inside a container RUN ln -sf /usr/lib/aarch64-linux-gnu/tegra/libv4l2.so.0 /usr/lib/aarch64-linux-gnu/libv4l2.so # # Configure Enviroment for ROS RUN echo 'source /opt/ros/foxy/install/setup.bash' >> ~/.bashrc # RUN echo "source /opt/ros/eloquent/setup.bash" >> ~/.bashrc RUN echo 'source /usr/share/colcon_cd/function/colcon_cd.sh' >> ~/.bashrc # RUN echo "export _colcon_cd_root=~/ros2_install" >> ~/.bashrc # echo $LD_LIBRARY_PATH RUN echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.2/targets/aarch64-linux/lib/stubs:/opt/ros/foxy/install/lib' >> ~/.bashrc WORKDIR /root/Downloads RUN wget https://developer.nvidia.com/embedded/L4T/r32_Release_v5.0/T186/Tegra186_Linux_R32.5.0_aarch64.tbz2 RUN tar xf Tegra186_Linux_R32.5.0_aarch64.tbz2 RUN cd Linux_for_Tegra && \ sed -i 's/config.tbz2\"/config.tbz2\" --exclude=etc\/hosts --exclude=etc\/hostname/g' apply_binaries.sh && \ sed -i 's/install --owner=root --group=root \"${QEMU_BIN}\" \"${L4T_ROOTFS_DIR}\/usr\/bin\/\"/#install --owner=root --group=root \"${QEMU_BIN}\" \"${L4T_ROOTFS_DIR}\/usr\/bin\/\"/g' nv_tegra/nv-apply-debs.sh && \ sed -i 's/LC_ALL=C chroot . mount -t proc none \/proc/ /g' nv_tegra/nv-apply-debs.sh && \ sed -i 's/umount ${L4T_ROOTFS_DIR}\/proc/ /g' nv_tegra/nv-apply-debs.sh && \ sed -i 's/chroot . \// /g' nv_tegra/nv-apply-debs.sh && \ ./apply_binaries.sh -r / --target-overlay RUN rm -rf Tegra210_Linux_R32.4.4_aarch64.tbz2 && \ rm -rf Linux_for_Tegra && \ echo "/usr/lib/aarch64-linux-gnu/tegra" > /etc/ld.so.conf.d/nvidia-tegra.conf && ldconfig WORKDIR /usr/local/zed ENV CUDA_HOME=/usr/local/cuda WORKDIR /root/ros2_ws/src/ RUN source /opt/ros/foxy/install/setup.bash && cd ../ && colcon build --symlink-install RUN git clone https://github.com/stereolabs/zed-ros2-wrapper.git RUN git clone https://github.com/ros/diagnostics.git && cd diagnostics && git checkout foxy WORKDIR /root/ros2_ws RUN source /opt/ros/foxy/install/setup.bash && source $(pwd)/install/local_setup.bash && rosdep update && \ rosdep install --from-paths src --ignore-src -r --rosdistro ${ROS_DISTRO} -y && \ colcon build --symlink-install --cmake-args " -DCMAKE_BUILD_TYPE=Release" " -DCMAKE_LIBRARY_PATH=/usr/local/cuda/lib64/stubs" " -DCUDA_CUDART_LIBRARY=/usr/local/cuda/lib64/stubs" " -DCMAKE_CXX_FLAGS='-Wl,--allow-shlib-undefined'" && \ echo source $(pwd)/install/local_setup.bash >> ~/.bashrc && \ source ~/.bashrc
aws-lambda-docker-serverless-inference
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AWS - NLP newsletter - 2021. Aug.
GitHub: Train a BlazingText text classification algorithm in SageMaker, inference with AWS Lambda
What are some alternatives?
amazon-sagemaker-examples - Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠Amazon SageMaker.
docker-images - Official source of container configurations, images, and examples for Oracle products and projects
ganbert-pytorch - Enhancing the BERT training with Semi-supervised Generative Adversarial Networks in Pytorch/HuggingFace
zed-ros2-wrapper - ROS 2 wrapper for the ZED SDK
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
AiDungeon2-Docker-ROCm - Runs an AIDungeon2 fork in Docker on AMD ROCm hardware.
tensorflow-on-arm - TensorFlow for Arm
keytotext - Keywords to Sentences
zed-docker - Docker images for the ZED SDK
python-machine-learning-book-3rd-edition - The "Python Machine Learning (3rd edition)" book code repository
multi-label-sentiment-classifier - How to build a multi-label sentiment classifiers with Tez and PyTorch
IntelligentEdgeHOL - The IntelligentEdgeHOL walks through the process of deploying an Azure IoT Edge module to an Nvidia Jetson Nano device to allow for detection of objects in YouTube videos, RTSP streams, or an attached web cam