Difference between revisions of "Jetson Zoo"
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This page contains instructions for installing various open source add-on packages and frameworks on [[Jetson|NVIDIA Jetson]], in addition to a collection of DNN models for inferencing. | This page contains instructions for installing various open source add-on packages and frameworks on [[Jetson|NVIDIA Jetson]], in addition to a collection of DNN models for inferencing. | ||
− | Below are links to container images and precompiled binaries built for aarch64 (arm64) architecture | + | Below are links to container images and precompiled binaries built for aarch64 (arm64) architecture. These are intended to be installed on top of [https://developer.nvidia.com/embedded/jetpack JetPack]. |
Note that JetPack comes with various pre-installed components such as the L4T kernel, CUDA Toolkit, cuDNN, TensorRT, VisionWorks, OpenCV, GStreamer, Docker, and more. | Note that JetPack comes with various pre-installed components such as the L4T kernel, CUDA Toolkit, cuDNN, TensorRT, VisionWorks, OpenCV, GStreamer, Docker, and more. | ||
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There are ready-to-use ML and data science containers for Jetson hosted on [https://ngc.nvidia.com/catalog/all?orderBy=modifiedDESC&pageNumber=0&query=l4t&quickFilter=&filters= NVIDIA GPU Cloud] (NGC), including the following: | There are ready-to-use ML and data science containers for Jetson hosted on [https://ngc.nvidia.com/catalog/all?orderBy=modifiedDESC&pageNumber=0&query=l4t&quickFilter=&filters= NVIDIA GPU Cloud] (NGC), including the following: | ||
− | * [https://ngc.nvidia.com/catalog/containers/nvidia:l4t-tensorflow l4t-tensorflow] - TensorFlow | + | * [https://ngc.nvidia.com/catalog/containers/nvidia:l4t-tensorflow l4t-tensorflow] - TensorFlow for JetPack 4.4 (and newer) |
− | * [https://ngc.nvidia.com/catalog/containers/nvidia:l4t-pytorch l4t-pytorch] - PyTorch | + | * [https://ngc.nvidia.com/catalog/containers/nvidia:l4t-pytorch l4t-pytorch] - PyTorch for JetPack 4.4 (and newer) |
* [https://ngc.nvidia.com/catalog/containers/nvidia:l4t-ml l4t-ml] - TensorFlow, PyTorch, scikit-learn, scipy, pandas, JupyterLab, ect. | * [https://ngc.nvidia.com/catalog/containers/nvidia:l4t-ml l4t-ml] - TensorFlow, PyTorch, scikit-learn, scipy, pandas, JupyterLab, ect. | ||
+ | |||
+ | If you wish to modify them, the Dockerfiles and build scripts for these containers can be found on [https://github.com/dusty-nv/jetson-containers GitHub]. | ||
+ | |||
+ | There are also following ready-to-use ML containers for Jetson hosted by our partners: | ||
+ | * ONNX Runtime for Jetson: [https://mcr.microsoft.com/azureml/onnxruntime:v.1.4.0-jetpack4.4-l4t-base-r32.4.3 mcr.microsoft.com/azureml/onnxruntime:v.1.4.0-jetpack4.4-l4t-base-r32.4.3] | ||
These containers are highly recommended to reduce the installation time of the frameworks below, and for beginners getting started. | These containers are highly recommended to reduce the installation time of the frameworks below, and for beginners getting started. | ||
− | |||
− | |||
<br/ > | <br/ > | ||
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* Source: [https://github.com/tensorflow/tensorflow https://github.com/tensorflow/tensorflow] | * Source: [https://github.com/tensorflow/tensorflow https://github.com/tensorflow/tensorflow] | ||
* Container: [https://ngc.nvidia.com/catalog/containers/nvidia:l4t-tensorflow l4t-tensorflow] | * Container: [https://ngc.nvidia.com/catalog/containers/nvidia:l4t-tensorflow l4t-tensorflow] | ||
− | * Version: 1.15. | + | * Version: 1.15.3, 2.3 (Python 3.6) |
* Packages: | * Packages: | ||
− | + | {| class="wikitable" style="margin-left:25px" | |
− | + | |- | |
− | + | ! !! TensorFlow 1.x !! TensorFlow 2.x | |
− | + | |- | |
− | + | | JetPack 4.4.1 || [https://developer.download.nvidia.com/compute/redist/jp/v44/tensorflow/tensorflow-1.15.3+nv20.9-cp36-cp36m-linux_aarch64.whl 1.15.3 pip wheel] || [https://developer.download.nvidia.com/compute/redist/jp/v44/tensorflow/tensorflow-2.3.0+nv20.9-cp36-cp36m-linux_aarch64.whl 2.3 pip wheel] | |
− | + | |- | |
+ | | JetPack 4.4 || [https://developer.download.nvidia.com/compute/redist/jp/v44/tensorflow/tensorflow-1.15.2+nv20.6-cp36-cp36m-linux_aarch64.whl 1.15.2 pip wheel] || [https://developer.download.nvidia.com/compute/redist/jp/v44/tensorflow/tensorflow-2.2.0+nv20.6-cp36-cp36m-linux_aarch64.whl 2.2 pip wheel] | ||
+ | |- | ||
+ | | JetPack 4.4 DP || [https://developer.download.nvidia.com/compute/redist/jp/v44/tensorflow/tensorflow-1.15.2+nv20.4-cp36-cp36m-linux_aarch64.whl 1.15.2 pip wheel] || [https://developer.download.nvidia.com/compute/redist/jp/v44/tensorflow/tensorflow-2.1.0+nv20.4-cp36-cp36m-linux_aarch64.whl 2.1 pip wheel] | ||
+ | |- | ||
+ | | JetPack 4.3 || [https://developer.download.nvidia.com/compute/redist/jp/v43/tensorflow/tensorflow-1.15.2+nv20.3-cp36-cp36m-linux_aarch64.whl 1.15.2 pip wheel] || [https://developer.download.nvidia.com/compute/redist/jp/v43/tensorflow/tensorflow-2.1.0+nv20.3-cp36-cp36m-linux_aarch64.whl 2.1 pip wheel] | ||
+ | |} | ||
* Supports: JetPack >= 4.2 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier) | * Supports: JetPack >= 4.2 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier) | ||
* Install Guide: [https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html#prereqs Installing TensorFlow on Jetson] | * Install Guide: [https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html#prereqs Installing TensorFlow on Jetson] | ||
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<source lang="bash"> | <source lang="bash"> | ||
# install prerequisites | # install prerequisites | ||
− | $ sudo apt-get install libhdf5-serial-dev hdf5-tools libhdf5-dev zlib1g-dev zip libjpeg8-dev | + | $ sudo apt-get install libhdf5-serial-dev hdf5-tools libhdf5-dev zlib1g-dev zip libjpeg8-dev liblapack-dev libblas-dev gfortran |
# install and upgrade pip3 | # install and upgrade pip3 | ||
$ sudo apt-get install python3-pip | $ sudo apt-get install python3-pip | ||
− | $ sudo pip3 install -U pip testresources setuptools | + | $ sudo pip3 install -U pip testresources setuptools=49.6.0 |
# install the following python packages | # install the following python packages | ||
− | $ sudo pip3 install -U numpy==1.16.1 future==0. | + | $ sudo pip3 install -U numpy==1.16.1 future==0.18.2 mock==3.0.5 h5py==2.10.0 keras_preprocessing==1.1.1 keras_applications==1.0.8 gast==0.2.2 futures protobuf pybind11 |
# to install TensorFlow 1.15 for JetPack 4.4: | # to install TensorFlow 1.15 for JetPack 4.4: | ||
$ sudo pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v44 ‘tensorflow<2’ | $ sudo pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v44 ‘tensorflow<2’ | ||
− | # or install the latest version of TensorFlow (2. | + | # or install the latest version of TensorFlow (2.3) for JetPack 4.4: |
$ sudo pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v44 tensorflow | $ sudo pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v44 tensorflow | ||
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* Source: [https://github.com/pytorch/pytorch https://github.com/pytorch/pytorch] | * Source: [https://github.com/pytorch/pytorch https://github.com/pytorch/pytorch] | ||
* Container: [https://ngc.nvidia.com/catalog/containers/nvidia:l4t-pytorch l4t-pytorch] | * Container: [https://ngc.nvidia.com/catalog/containers/nvidia:l4t-pytorch l4t-pytorch] | ||
− | * Version: PyTorch v1.0.0 - v1. | + | * Version: PyTorch v1.0.0 - v1.8.0 |
* Packages: | * Packages: | ||
− | {{spaces|6}} '''JetPack 4.4 | + | {{spaces|6}} '''JetPack 4.4 / 4.4.1 / 4.5''' |
+ | |||
+ | {{spaces|6}} * [https://nvidia.box.com/shared/static/p57jwntv436lfrd78inwl7iml6p13fzh.whl PyTorch v1.8.0 pip wheel] (Python 3.6) <br /> | ||
+ | {{spaces|6}} * [https://nvidia.box.com/shared/static/cs3xn3td6sfgtene6jdvsxlr366m2dhq.whl PyTorch v1.7.0 pip wheel] (Python 3.6) <br /> | ||
+ | {{spaces|6}} * [https://nvidia.box.com/shared/static/9eptse6jyly1ggt9axbja2yrmj6pbarc.whl PyTorch v1.6.0 pip wheel] (Python 3.6) | ||
+ | |||
+ | |||
+ | {{spaces|6}} '''JetPack 4.4 Developer Preview''' | ||
{| class="wikitable" style="margin-left:25px" | {| class="wikitable" style="margin-left:25px" | ||
|- | |- | ||
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| v1.4.0 || [https://nvidia.box.com/v/torch-1-4-cp27-jetson-jp43 pip wheel] || [https://nvidia.box.com/v/torch-1-4-cp36-jetson-jp43 pip wheel] | | v1.4.0 || [https://nvidia.box.com/v/torch-1-4-cp27-jetson-jp43 pip wheel] || [https://nvidia.box.com/v/torch-1-4-cp36-jetson-jp43 pip wheel] | ||
|} | |} | ||
− | * As per the [https://github.com/pytorch/pytorch/releases PyTorch Release Notes], Python 2 is not longer supported | + | * As per the [https://github.com/pytorch/pytorch/releases PyTorch Release Notes], Python 2 is not longer supported as of PyTorch v1.5 and newer. |
* Supports: JetPack >= 4.2 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier) | * Supports: JetPack >= 4.2 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier) | ||
* Forum Topic: [https://devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/ devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/] | * Forum Topic: [https://devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/ devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/] | ||
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<span style="color: white; background-color: #7BCC70; padding: 5px">'''note''' — the PyTorch and Caffe2 projects have merged, so installing PyTorch will also install Caffe2</span> | <span style="color: white; background-color: #7BCC70; padding: 5px">'''note''' — the PyTorch and Caffe2 projects have merged, so installing PyTorch will also install Caffe2</span> | ||
<source lang="bash"> | <source lang="bash"> | ||
− | # | + | # install OpenBLAS and OpenMPI |
− | $ sudo apt-get install libopenblas-base | + | $ sudo apt-get install libopenblas-base libopenmpi-dev |
# Python 2.7 (download pip wheel from above) | # Python 2.7 (download pip wheel from above) | ||
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# Python 3.6 (download pip wheel from above) | # Python 3.6 (download pip wheel from above) | ||
− | $ pip3 install Cython | + | $ sudo apt-get install python3-pip |
− | + | pip3 install Cython | |
+ | pip3 install numpy torch-1.6.0-cp36-cp36m-linux_aarch64.whl | ||
</source> | </source> | ||
As per the the [https://github.com/pytorch/pytorch/releases/tag/v1.4.0 PyTorch 1.4 Release Notes], Python 2 support is now deprecated and PyTorch 1.4 is the last version to support Python 2. | As per the the [https://github.com/pytorch/pytorch/releases/tag/v1.4.0 PyTorch 1.4 Release Notes], Python 2 support is now deprecated and PyTorch 1.4 is the last version to support Python 2. | ||
+ | |||
+ | == ONNX Runtime == | ||
+ | |||
+ | [[File:ONNX-Runtime-logo.svg|200px|right]] | ||
+ | |||
+ | * Website: https://microsoft.github.io/onnxruntime/ | ||
+ | * Source: https://github.com/microsoft/onnxruntime | ||
+ | * Container: [https://mcr.microsoft.com/azureml/onnxruntime:v.1.4.0-jetpack4.4-l4t-base-r32.4.3 mcr.microsoft.com/azureml/onnxruntime:v.1.4.0-jetpack4.4-l4t-base-r32.4.3] | ||
+ | * Version: 1.4.0, 1.5.2, 1.6.0, 1.7.0 | ||
+ | * Packages: | ||
+ | ** onnxruntime 1.4.0 (JetPack 4.4/4.4.1/4.5) | ||
+ | *** [https://nvidia.box.com/shared/static/8sc6j25orjcpl6vhq3a4ir8v219fglng.whl 1.4.0 pip wheel (Python 3.6)] | ||
+ | ** onnxruntime 1.5.2 (JetPack 4.4/4.4.1/4.5) | ||
+ | *** [https://nvidia.box.com/shared/static/6lxbakd8zaf2p5nkgcn7ih116iqm1wzm.whl 1.5.2 pip wheel (Python 3.6)] | ||
+ | ** onnxruntime 1.6.0 (JetPack 4.4/4.4.1/4.5) | ||
+ | *** [https://nvidia.box.com/shared/static/49fzcqa1g4oblwxr3ikmuvhuaprqyxb7.whl 1.6.0 pip wheel (Python 3.6)] | ||
+ | *** [https://nvidia.box.com/shared/static/6rnx40ztx5874v3nvw4fkjzho3h0g3qx.whl 1.6.0 pip wheel (Python 3.7)] | ||
+ | *** [https://nvidia.box.com/shared/static/49fzcqa1g4oblwxr3ikmuvhuaprqyxb7.whl 1.6.0 pip wheel (Python 3.8)] | ||
+ | ** onnxruntime 1.7.0 (JetPack 4.4/4.4.1/4.5) | ||
+ | *** [https://nvidia.box.com/shared/static/ukszbm1iklzymrt54mgxbzjfzunq7i9t.whl 1.7.0 pip wheel (Python 3.6)] | ||
+ | *** [https://nvidia.box.com/shared/static/jhedu966zv1m68t5xcltqckbej63btf3.whl 1.7.0 pip wheel (Python 3.7)] | ||
+ | *** [https://nvidia.box.com/shared/static/8xgbee5ghhb92i9rrcr04yymg0n3x3t0.whl 1.7.0 pip wheel (Python 3.8)] | ||
+ | *** [https://nvidia.box.com/shared/static/sa6lmumikihkfnvx5eup40o4audhqgz4.whl 1.7.0 pip wheel (Python 3.9)] | ||
+ | * Supports: JetPack >= 4.4 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier) | ||
+ | * Forum Support: https://github.com/microsoft/onnxruntime | ||
+ | * Build from Source: Refer to [https://github.com/microsoft/onnxruntime/blob/master/BUILD.md#nvidia-jetson-tx1tx2nanoxavier these] instructions | ||
+ | * ONNX Runtime 1.7.0 Install instructions | ||
+ | <source lang="bash"> | ||
+ | # Download pip wheel from location mentioned above | ||
+ | $ wget https://nvidia.box.com/shared/static/ukszbm1iklzymrt54mgxbzjfzunq7i9t.whl -O onnxruntime_gpu-1.7.0-cp36-cp36m-linux_aarch64.whl | ||
+ | |||
+ | # Install pip wheel | ||
+ | $ pip3 install onnxruntime_gpu-1.7.0-cp36-cp36m-linux_aarch64.whl | ||
+ | </source> | ||
== MXNet == | == MXNet == | ||
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* Build from Source: [https://forums.developer.nvidia.com/t/i-was-unable-to-compile-and-install-mxnet-on-the-jetson-nano-is-there-an-official-installation-tutorial/72259/8 v1.6] | [https://forums.developer.nvidia.com/t/i-was-unable-to-compile-and-install-mxnet1-5-with-tensorrt-on-the-jetson-nano-is-there-someone-have-compile-it-please-help-me-thank-you/111303/27 v1.7] | * Build from Source: [https://forums.developer.nvidia.com/t/i-was-unable-to-compile-and-install-mxnet-on-the-jetson-nano-is-there-an-official-installation-tutorial/72259/8 v1.6] | [https://forums.developer.nvidia.com/t/i-was-unable-to-compile-and-install-mxnet1-5-with-tensorrt-on-the-jetson-nano-is-there-someone-have-compile-it-please-help-me-thank-you/111303/27 v1.7] | ||
− | + | '''MXNet 1.7 Install Instructions:''' | |
<source lang="bash"> | <source lang="bash"> | ||
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</source> | </source> | ||
− | + | '''MXNet 1.4 / 1.6 Install Instructions:''' | |
<source lang="bash"> | <source lang="bash"> | ||
# Python 2.7 | # Python 2.7 | ||
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* Source: [https://github.com/dusty-nv/jetson-inference https://github.com/dusty-nv/jetson-inference] | * Source: [https://github.com/dusty-nv/jetson-inference https://github.com/dusty-nv/jetson-inference] | ||
* Supports: Jetson Nano, TX1, TX2, Xavier NX, AGX Xavier | * Supports: Jetson Nano, TX1, TX2, Xavier NX, AGX Xavier | ||
− | * Build from Source | + | * [https://github.com/dusty-nv/jetson-inference/blob/master/docs/aux-docker.md Run the Container] |
+ | |||
+ | <source lang="bash"> | ||
+ | $ git clone --recursive https://github.com/dusty-nv/jetson-inference | ||
+ | $ cd jetson-inference | ||
+ | $ docker/run.sh | ||
+ | </source> | ||
+ | |||
+ | * [https://github.com/dusty-nv/jetson-inference/blob/master/docs/building-repo-2.md Build from Source] | ||
<source lang="bash"> | <source lang="bash"> | ||
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|- | |- | ||
| YOLO-v3 || [http://cocodataset.org COCO] || 608x608 || 80 || Darknet || <code>Custom</code> || {{Yes}} || [https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps/tree/restructure/yolo#trt-yolo-app trt-yolo-app] || [https://pjreddie.com/darknet/yolo/ YOLO] | | YOLO-v3 || [http://cocodataset.org COCO] || 608x608 || 80 || Darknet || <code>Custom</code> || {{Yes}} || [https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps/tree/restructure/yolo#trt-yolo-app trt-yolo-app] || [https://pjreddie.com/darknet/yolo/ YOLO] | ||
+ | |- | ||
+ | | YOLO-v4 || [http://cocodataset.org COCO] || 608x608 || 80 || Darknet || <code>Custom</code> || {{Yes}} || [https://jkjung-avt.github.io/tensorrt-yolov4/ tensorrt-yolov4] || [https://pjreddie.com/darknet/yolo/ YOLO] | ||
|- | |- | ||
| Tiny YOLO-v3 || [http://cocodataset.org COCO] || 416x416 || 80 || Darknet || <code>Custom</code> || {{Yes}} || [https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps/tree/restructure/yolo#trt-yolo-app trt-yolo-app] || [https://pjreddie.com/darknet/yolo/ YOLO] | | Tiny YOLO-v3 || [http://cocodataset.org COCO] || 416x416 || 80 || Darknet || <code>Custom</code> || {{Yes}} || [https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps/tree/restructure/yolo#trt-yolo-app trt-yolo-app] || [https://pjreddie.com/darknet/yolo/ YOLO] | ||
|- | |- | ||
| Tiny YOLO-v3 || [http://cocodataset.org COCO] || 416x416 || 80 || Darknet || <code>Custom</code> || {{Yes}} || [https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps/tree/restructure/yolo#trt-yolo-app trt-yolo-app] || [https://pjreddie.com/darknet/yolo/ YOLO] | | Tiny YOLO-v3 || [http://cocodataset.org COCO] || 416x416 || 80 || Darknet || <code>Custom</code> || {{Yes}} || [https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps/tree/restructure/yolo#trt-yolo-app trt-yolo-app] || [https://pjreddie.com/darknet/yolo/ YOLO] | ||
+ | |- | ||
+ | | Tiny YOLO-v4 || [http://cocodataset.org COCO] || 416x416 || 80 || Darknet || <code>Custom</code> || {{Yes}} || [https://jkjung-avt.github.io/tensorrt-yolov4/ tensorrt-yolov4] || [https://pjreddie.com/darknet/yolo/ YOLO] | ||
|- | |- | ||
| Faster-RCNN || [http://host.robots.ox.ac.uk/pascal/VOC/ Pascal VOC] || 500x375 || 21 || Caffe || <code>caffemodel</code> || {{Yes}} || [https://docs.nvidia.com/deeplearning/sdk/tensorrt-sample-support-guide/index.html#fasterrcnn_sample TensorRT sample] || [https://github.com/rbgirshick/py-faster-rcnn GitHub] | | Faster-RCNN || [http://host.robots.ox.ac.uk/pascal/VOC/ Pascal VOC] || 500x375 || 21 || Caffe || <code>caffemodel</code> || {{Yes}} || [https://docs.nvidia.com/deeplearning/sdk/tensorrt-sample-support-guide/index.html#fasterrcnn_sample TensorRT sample] || [https://github.com/rbgirshick/py-faster-rcnn GitHub] | ||
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* Website: [https://opencv.org/ https://opencv.org/] | * Website: [https://opencv.org/ https://opencv.org/] | ||
* Source: [https://github.com/opencv/opencv https://github.com/opencv/opencv] | * Source: [https://github.com/opencv/opencv https://github.com/opencv/opencv] | ||
− | * Version: 3.3.1 (JetPack <= 4.2.x), 4.1 (JetPack 4.3, JetPack 4.4) | + | * Version: 3.3.1 (JetPack <= 4.2.x), 4.1.1 (JetPack 4.3, JetPack 4.4, JetPack 4.5) |
* Supports: Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier | * Supports: Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier | ||
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* Website: [http://ros.org/ http://ros.org/] | * Website: [http://ros.org/ http://ros.org/] | ||
* Source: [https://github.com/ros https://github.com/ros] | * Source: [https://github.com/ros https://github.com/ros] | ||
− | * Version: ROS Melodic | + | * Version: ROS Melodic, ROS Noetic, ROS2 Eloquent, ROS2 Foxy |
* Supports: JetPack >= 4.2 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier) | * Supports: JetPack >= 4.2 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier) | ||
* Installation: [http://wiki.ros.org/melodic/Installation/Ubuntu http://wiki.ros.org/melodic/Installation/Ubuntu] | * Installation: [http://wiki.ros.org/melodic/Installation/Ubuntu http://wiki.ros.org/melodic/Installation/Ubuntu] | ||
+ | * Containers: [https://github.com/dusty-nv/jetson-containers#ros-containers https://github.com/dusty-nv/jetson-containers#ros-containers] | ||
<source lang="bash"> | <source lang="bash"> | ||
− | # | + | # install ROS Melodic |
$ sudo apt-add-repository universe | $ sudo apt-add-repository universe | ||
$ sudo apt-add-repository multiverse | $ sudo apt-add-repository multiverse | ||
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* Website: [https://developer.nvidia.com/isaac-sdk https://developer.nvidia.com/isaac-sdk] | * Website: [https://developer.nvidia.com/isaac-sdk https://developer.nvidia.com/isaac-sdk] | ||
− | * Version: 2019.2, 2019.3, 2020.1 | + | * Version: 2019.2, 2019.3, 2020.1, 2020.2 |
− | * Supports: JetPack 4.2.x, JetPack 4.3 (Jetson Nano / TX2 / Xavier) | + | * Supports: JetPack 4.2.x, JetPack 4.3, JetPack 4.4 (Jetson Nano / TX2 / Xavier) |
* Downloads: [https://developer.nvidia.com/isaac/downloads https://developer.nvidia.com/isaac/downloads] | * Downloads: [https://developer.nvidia.com/isaac/downloads https://developer.nvidia.com/isaac/downloads] | ||
* Documentation: [https://docs.nvidia.com/isaac https://docs.nvidia.com/isaac] | * Documentation: [https://docs.nvidia.com/isaac https://docs.nvidia.com/isaac] | ||
Line 409: | Line 473: | ||
* Source: [https://github.com/aws/aws-greengrass-core-sdk-c https://github.com/aws/aws-greengrass-core-sdk-c] | * Source: [https://github.com/aws/aws-greengrass-core-sdk-c https://github.com/aws/aws-greengrass-core-sdk-c] | ||
* Version: v1.9.1 | * Version: v1.9.1 | ||
− | * Supports: JetPack 4.2.x, JetPack 4.3 (Jetson Nano / TX1 / TX2 / Xavier) | + | * Supports: JetPack 4.2.x, JetPack 4.3, JetPack 4.4 (Jetson Nano / TX1 / TX2 / Xavier) |
* Forum Thread: [https://devtalk.nvidia.com/default/topic/1052324/jetson-nano/jetson-nano-aws-greengrass-/post/5341970/#5341970 https://devtalk.nvidia.com/default/topic/1052324/#5341970] | * Forum Thread: [https://devtalk.nvidia.com/default/topic/1052324/jetson-nano/jetson-nano-aws-greengrass-/post/5341970/#5341970 https://devtalk.nvidia.com/default/topic/1052324/#5341970] | ||
Line 449: | Line 513: | ||
* Website: [https://developer.nvidia.com/deepstream-sdk https://developer.nvidia.com/deepstream-sdk] | * Website: [https://developer.nvidia.com/deepstream-sdk https://developer.nvidia.com/deepstream-sdk] | ||
− | * Version: 5.0 | + | * Version: 5.0 |
* Supports: JetPack >= 4.2 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier) | * Supports: JetPack >= 4.2 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier) | ||
* FAQ: [https://developer.nvidia.com/deepstream-faq https://developer.nvidia.com/deepstream-faq] | * FAQ: [https://developer.nvidia.com/deepstream-faq https://developer.nvidia.com/deepstream-faq] | ||
Line 468: | Line 532: | ||
* Installed by default in JetPack-L4T | * Installed by default in JetPack-L4T | ||
− | + | Launch your container with <code>--runtime nvidia</code> to enable GPU-passthrough. | |
+ | Launch your container with <code>--volume /tmp/argus_socket:/tmp/argus_socket</code> to enable access to MIPI CSI cameras. | ||
− | + | See [https://github.com/NVIDIA/nvidia-docker/wiki/NVIDIA-Container-Runtime-on-Jetson https://github.com/NVIDIA/nvidia-docker/wiki/NVIDIA-Container-Runtime-on-Jetson] for more documentation on using Docker on Jetson. | |
− | / | ||
− | / | ||
− | / | ||
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− | |||
− | |||
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− | docker | ||
− | |||
To enable IPVLAN for Docker Swarm mode: [https://blog.hypriot.com/post/nvidia-jetson-nano-build-kernel-docker-optimized/ https://blog.hypriot.com/post/nvidia-jetson-nano-build-kernel-docker-optimized/] | To enable IPVLAN for Docker Swarm mode: [https://blog.hypriot.com/post/nvidia-jetson-nano-build-kernel-docker-optimized/ https://blog.hypriot.com/post/nvidia-jetson-nano-build-kernel-docker-optimized/] |
Revision as of 18:41, 5 March 2021
This page contains instructions for installing various open source add-on packages and frameworks on NVIDIA Jetson, in addition to a collection of DNN models for inferencing.
Below are links to container images and precompiled binaries built for aarch64 (arm64) architecture. These are intended to be installed on top of JetPack.
Note that JetPack comes with various pre-installed components such as the L4T kernel, CUDA Toolkit, cuDNN, TensorRT, VisionWorks, OpenCV, GStreamer, Docker, and more.
Machine Learning
Jetson is able to natively run the full versions of popular machine learning frameworks, including TensorFlow, PyTorch, Caffe2, Keras, and MXNet.
There are also helpful deep learning examples and tutorials available, created specifically for Jetson - like Hello AI World and JetBot.
Docker Containers
There are ready-to-use ML and data science containers for Jetson hosted on NVIDIA GPU Cloud (NGC), including the following:
- l4t-tensorflow - TensorFlow for JetPack 4.4 (and newer)
- l4t-pytorch - PyTorch for JetPack 4.4 (and newer)
- l4t-ml - TensorFlow, PyTorch, scikit-learn, scipy, pandas, JupyterLab, ect.
If you wish to modify them, the Dockerfiles and build scripts for these containers can be found on GitHub.
There are also following ready-to-use ML containers for Jetson hosted by our partners:
- ONNX Runtime for Jetson: mcr.microsoft.com/azureml/onnxruntime:v.1.4.0-jetpack4.4-l4t-base-r32.4.3
These containers are highly recommended to reduce the installation time of the frameworks below, and for beginners getting started.
TensorFlow
- Website: https://tensorflow.org
- Source: https://github.com/tensorflow/tensorflow
- Container: l4t-tensorflow
- Version: 1.15.3, 2.3 (Python 3.6)
- Packages:
TensorFlow 1.x | TensorFlow 2.x | |
---|---|---|
JetPack 4.4.1 | 1.15.3 pip wheel | 2.3 pip wheel |
JetPack 4.4 | 1.15.2 pip wheel | 2.2 pip wheel |
JetPack 4.4 DP | 1.15.2 pip wheel | 2.1 pip wheel |
JetPack 4.3 | 1.15.2 pip wheel | 2.1 pip wheel |
- Supports: JetPack >= 4.2 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier)
- Install Guide: Installing TensorFlow on Jetson
- Forum Topic: devtalk.nvidia.com/default/topic/1048776/jetson-nano/official-tensorflow-for-jetson-nano-/
- Build from Source: https://devtalk.nvidia.com/default/topic/1055131/jetson-agx-xavier/building-tensorflow-1-13-on-jetson-xavier/
# install prerequisites
$ sudo apt-get install libhdf5-serial-dev hdf5-tools libhdf5-dev zlib1g-dev zip libjpeg8-dev liblapack-dev libblas-dev gfortran
# install and upgrade pip3
$ sudo apt-get install python3-pip
$ sudo pip3 install -U pip testresources setuptools=49.6.0
# install the following python packages
$ sudo pip3 install -U numpy==1.16.1 future==0.18.2 mock==3.0.5 h5py==2.10.0 keras_preprocessing==1.1.1 keras_applications==1.0.8 gast==0.2.2 futures protobuf pybind11
# to install TensorFlow 1.15 for JetPack 4.4:
$ sudo pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v44 ‘tensorflow<2’
# or install the latest version of TensorFlow (2.3) for JetPack 4.4:
$ sudo pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v44 tensorflow
PyTorch (Caffe2)
- Website: https://pytorch.org/
- Source: https://github.com/pytorch/pytorch
- Container: l4t-pytorch
- Version: PyTorch v1.0.0 - v1.8.0
- Packages:
JetPack 4.4 / 4.4.1 / 4.5
* PyTorch v1.8.0 pip wheel (Python 3.6)
* PyTorch v1.7.0 pip wheel (Python 3.6)
* PyTorch v1.6.0 pip wheel (Python 3.6)
JetPack 4.4 Developer Preview
Python 2.7 | Python 3.6 | |
---|---|---|
v1.2.0 | pip wheel | pip wheel |
v1.3.0 | pip wheel | pip wheel |
v1.4.0 | pip wheel | pip wheel |
v1.5.0 | pip wheel |
JetPack 4.2 / 4.3
Python 2.7 | Python 3.6 | |
---|---|---|
v1.0.0 | pip wheel | pip wheel |
v1.1.0 | pip wheel | pip wheel |
v1.2.0 | pip wheel | pip wheel |
v1.3.0 | pip wheel | pip wheel |
v1.4.0 | pip wheel | pip wheel |
- As per the PyTorch Release Notes, Python 2 is not longer supported as of PyTorch v1.5 and newer.
- Supports: JetPack >= 4.2 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier)
- Forum Topic: devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/
- Build from Source: https://devtalk.nvidia.com/default/topic/1049071/#5324123
note — the PyTorch and Caffe2 projects have merged, so installing PyTorch will also install Caffe2
# install OpenBLAS and OpenMPI
$ sudo apt-get install libopenblas-base libopenmpi-dev
# Python 2.7 (download pip wheel from above)
$ pip install future torch-1.4.0-cp27-cp27mu-linux_aarch64.whl
# Python 3.6 (download pip wheel from above)
$ sudo apt-get install python3-pip
pip3 install Cython
pip3 install numpy torch-1.6.0-cp36-cp36m-linux_aarch64.whl
As per the the PyTorch 1.4 Release Notes, Python 2 support is now deprecated and PyTorch 1.4 is the last version to support Python 2.
ONNX Runtime
- Website: https://microsoft.github.io/onnxruntime/
- Source: https://github.com/microsoft/onnxruntime
- Container: mcr.microsoft.com/azureml/onnxruntime:v.1.4.0-jetpack4.4-l4t-base-r32.4.3
- Version: 1.4.0, 1.5.2, 1.6.0, 1.7.0
- Packages:
- onnxruntime 1.4.0 (JetPack 4.4/4.4.1/4.5)
- onnxruntime 1.5.2 (JetPack 4.4/4.4.1/4.5)
- onnxruntime 1.6.0 (JetPack 4.4/4.4.1/4.5)
- onnxruntime 1.7.0 (JetPack 4.4/4.4.1/4.5)
- Supports: JetPack >= 4.4 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier)
- Forum Support: https://github.com/microsoft/onnxruntime
- Build from Source: Refer to these instructions
- ONNX Runtime 1.7.0 Install instructions
# Download pip wheel from location mentioned above
$ wget https://nvidia.box.com/shared/static/ukszbm1iklzymrt54mgxbzjfzunq7i9t.whl -O onnxruntime_gpu-1.7.0-cp36-cp36m-linux_aarch64.whl
# Install pip wheel
$ pip3 install onnxruntime_gpu-1.7.0-cp36-cp36m-linux_aarch64.whl
MXNet
- Website: https://mxnet.apache.org/
- Source: https://github.com/apache/incubator-mxnet
- Version: 1.4, 1.6, 1.7
- Packages:
Python 2.7 | Python 3.6 | |
---|---|---|
v1.4 (JetPack 4.2.x) | pip wheel | pip wheel |
v1.6 (JetPack 4.3) | pip wheel | pip wheel |
v1.7 (JetPack 4.4) | - | forum post |
- Supports: JetPack >= 4.2 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier)
- Forum Topics: v1.4 | v1.6 | v1.7
- Build from Source: v1.6 | v1.7
MXNet 1.7 Install Instructions:
$ wget https://raw.githubusercontent.com/AastaNV/JEP/master/MXNET/autoinstall_mxnet.sh
$ sudo chmod +x autoinstall_mxnet.sh
$ ./autoinstall_mxnet.sh <Nano/TX1/TX2/Xavier>
MXNet 1.4 / 1.6 Install Instructions:
# Python 2.7
sudo apt-get install -y git build-essential libatlas-base-dev libopencv-dev graphviz python-pip
sudo pip install mxnet-1.4.0-cp27-cp27mu-linux_aarch64.whl
# Python 3.6
sudo apt-get install -y git build-essential libatlas-base-dev libopencv-dev graphviz python3-pip
sudo pip install mxnet-1.4.0-cp36-cp36m-linux_aarch64.whl
Keras
- Website: https://keras.io/
- Source: https://github.com/keras-team/keras
- Version: 2.2.4
- Forum Topic: https://devtalk.nvidia.com/default/topic/1049362/#5325752
First, install TensorFlow from above.
# beforehand, install TensorFlow (https://eLinux.org/Jetson_Zoo#TensorFlow)
$ sudo apt-get install -y build-essential libatlas-base-dev gfortran
$ sudo pip install keras
Hello AI World
- Website: https://developer.nvidia.com/embedded/twodaystoademo
- Source: https://github.com/dusty-nv/jetson-inference
- Supports: Jetson Nano, TX1, TX2, Xavier NX, AGX Xavier
- Run the Container
$ git clone --recursive https://github.com/dusty-nv/jetson-inference
$ cd jetson-inference
$ docker/run.sh
# download the repo
$ git clone --recursive https://github.com/dusty-nv/jetson-inference
$ cd jetson-inference
# configure build tree
$ mkdir build
$ cd build
$ cmake ../
# build and install
$ make -j$(nproc)
$ sudo make install
$ sudo ldconfig
Model Zoo
Below are various DNN models for inferencing on Jetson with support for TensorRT. Included are links to code samples with the model and the original source.
Note that many other models are able to run natively on Jetson by using the Machine Learning frameworks like those listed above.
For performance benchmarks, see these resources:
- Jetson Nano Deep Learning Inference Benchmarks
- Jetson TX1/TX2 - NVIDIA AI Inference Technical Overview
- Jetson AGX Xavier Deep Learning Inference Benchmarks
Classification
Network | Dataset | Resolution | Classes | Framework | Format | TensorRT | Samples | Original |
---|---|---|---|---|---|---|---|---|
AlexNet | ILSVRC12 | 224x224 | 1000 | Caffe | caffemodel |
Yes | Hello AI World | BVLC |
GoogleNet | ILSVRC12 | 224x224 | 1000 | Caffe | caffemodel |
Yes | Hello AI World | BVLC |
ResNet-18 | ILSVRC15 | 224x224 | 1000 | Caffe | caffemodel |
Yes | Hello AI World | GitHub |
ResNet-50 | ILSVRC15 | 224x224 | 1000 | Caffe | caffemodel |
Yes | Hello AI World | GitHub |
ResNet-101 | ILSVRC15 | 224x224 | 1000 | Caffe | caffemodel |
Yes | Hello AI World | GitHub |
ResNet-152 | ILSVRC15 | 224x224 | 1000 | Caffe | caffemodel |
Yes | Hello AI World | GitHub |
VGG-16 | ILSVRC14 | 224x224 | 1000 | Caffe | caffemodel |
Yes | Hello AI World | GitHub |
VGG-19 | ILSVRC14 | 224x224 | 1000 | Caffe | caffemodel |
Yes | Hello AI World | GitHub |
Inception-v4 | ILSVRC12 | 299x299 | 1000 | Caffe | caffemodel |
Yes | Hello AI World | GitHub |
Inception-v4 | ILSVRC12 | 299x299 | 1000 | TensorFlow | TF-TRT (UFF) |
Yes | tf_trt_models | TF-slim |
Mobilenet-v1 | ILSVRC12 | 224x224 | 1000 | TensorFlow | TF-TRT (UFF) |
Yes | tf_trt_models | TF-slim |
Object Detection
Network | Dataset | Resolution | Classes | Framework | Format | TensorRT | Samples | Original |
---|---|---|---|---|---|---|---|---|
SSD-Mobilenet-v1 | COCO | 300x300 | 91 | TensorFlow | UFF |
Yes | Hello AI World TRT_object_detection |
TF Zoo |
SSD-Mobilenet-v2 | COCO | 300x300 | 91 | TensorFlow | UFF |
Yes | TF Zoo | |
SSD-Inception-v2 | COCO | 300x300 | 91 | TensorFlow | UFF |
Yes | TF Zoo | |
YOLO-v2 | COCO | 608x608 | 80 | Darknet | Custom |
Yes | trt-yolo-app | YOLO |
YOLO-v3 | COCO | 608x608 | 80 | Darknet | Custom |
Yes | trt-yolo-app | YOLO |
YOLO-v4 | COCO | 608x608 | 80 | Darknet | Custom |
Yes | tensorrt-yolov4 | YOLO |
Tiny YOLO-v3 | COCO | 416x416 | 80 | Darknet | Custom |
Yes | trt-yolo-app | YOLO |
Tiny YOLO-v3 | COCO | 416x416 | 80 | Darknet | Custom |
Yes | trt-yolo-app | YOLO |
Tiny YOLO-v4 | COCO | 416x416 | 80 | Darknet | Custom |
Yes | tensorrt-yolov4 | YOLO |
Faster-RCNN | Pascal VOC | 500x375 | 21 | Caffe | caffemodel |
Yes | TensorRT sample | GitHub |
Segmentation
Network | Dataset | Resolution | Classes | Framework | Format | TensorRT | Samples | Original |
---|---|---|---|---|---|---|---|---|
FCN-ResNet18 | Cityscapes | 2048x1024 | 21 | PyTorch | ONNX |
Yes | Hello AI World | GitHub |
FCN-ResNet18 | Cityscapes | 1024x512 | 21 | PyTorch | ONNX |
Yes | Hello AI World | GitHub |
FCN-ResNet18 | Cityscapes | 512x256 | 21 | PyTorch | ONNX |
Yes | Hello AI World | GitHub |
FCN-ResNet18 | DeepScene | 864x480 | 5 | PyTorch | ONNX |
Yes | Hello AI World | GitHub |
FCN-ResNet18 | DeepScene | 576x320 | 5 | PyTorch | ONNX |
Yes | Hello AI World | GitHub |
FCN-ResNet18 | DeepScene | 864x480 | 5 | PyTorch | ONNX |
Yes | Hello AI World | GitHub |
FCN-ResNet18 | Multi-Human | 640x360 | 21 | PyTorch | ONNX |
Yes | Hello AI World | GitHub |
FCN-ResNet18 | Multi-Human | 512x320 | 21 | PyTorch | ONNX |
Yes | Hello AI World | GitHub |
FCN-ResNet18 | Pascal VOC | 512x320 | 21 | PyTorch | ONNX |
Yes | Hello AI World | GitHub |
FCN-ResNet18 | Pascal VOC | 320x320 | 21 | PyTorch | ONNX |
Yes | Hello AI World | GitHub |
FCN-ResNet18 | SUN RGB-D | 640x512 | 21 | PyTorch | ONNX |
Yes | Hello AI World | GitHub |
FCN-ResNet18 | SUN RGB-D | 512x400 | 21 | PyTorch | ONNX |
Yes | Hello AI World | GitHub |
FCN-Alexnet | Cityscapes | 2048x1024 | 21 | Caffe | caffemodel |
Yes | Hello AI World | GitHub |
FCN-Alexnet | Cityscapes | 1024x512 | 21 | Caffe | caffemodel |
Yes | Hello AI World | GitHub |
FCN-Alexnet | Pascal VOC | 500x356 | 21 | Caffe | caffemodel |
Yes | Hello AI World | GitHub |
U-Net | Carvana | 512x512 | 1 | TensorFlow | UFF |
Yes | Nano Benchmarks | GitHub |
Pose Estimation
Network | Dataset | Resolution | Classes | Framework | Format | TensorRT | Samples | Original |
---|---|---|---|---|---|---|---|---|
ResNet18_att | COCO | 224x224 | 16 | PyTorch | torch2trt | Yes | trt_pose | GitHub |
DenseNet121_att | COCO | 224x224 | 16 | PyTorch | torch2trt | Yes | trt_pose | GitHub |
Computer Vision
OpenCV
- Website: https://opencv.org/
- Source: https://github.com/opencv/opencv
- Version: 3.3.1 (JetPack <= 4.2.x), 4.1.1 (JetPack 4.3, JetPack 4.4, JetPack 4.5)
- Supports: Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier
- OpenCV is included with JetPack, compiled with support for GStreamer. To build a newer version or to enable CUDA support, see these guides:
- nano_build_opencv (GitHub)
- Installing OpenCV 3.4.6
Robotics
ROS
- Website: http://ros.org/
- Source: https://github.com/ros
- Version: ROS Melodic, ROS Noetic, ROS2 Eloquent, ROS2 Foxy
- Supports: JetPack >= 4.2 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier)
- Installation: http://wiki.ros.org/melodic/Installation/Ubuntu
- Containers: https://github.com/dusty-nv/jetson-containers#ros-containers
# install ROS Melodic
$ sudo apt-add-repository universe
$ sudo apt-add-repository multiverse
$ sudo apt-add-repository restricted
# add ROS repository to apt sources
$ sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'
$ sudo apt-key adv --keyserver 'hkp://keyserver.ubuntu.com:80' --recv-key C1CF6E31E6BADE8868B172B4F42ED6FBAB17C654
# install ROS Base
$ sudo apt-get update
$ sudo apt-get install ros-melodic-ros-base
# add ROS paths to environment
sudo sh -c 'echo "source /opt/ros/melodic/setup.bash" >> ~/.bashrc'
NVIDIA Isaac SDK
- Website: https://developer.nvidia.com/isaac-sdk
- Version: 2019.2, 2019.3, 2020.1, 2020.2
- Supports: JetPack 4.2.x, JetPack 4.3, JetPack 4.4 (Jetson Nano / TX2 / Xavier)
- Downloads: https://developer.nvidia.com/isaac/downloads
- Documentation: https://docs.nvidia.com/isaac
Isaac SIM
- Website: https://developer.nvidia.com/isaac-sdk
- Documentation: http://docs.nvidia.com/isaac/isaac_sim/index.html
IoT / Edge
AWS Greengrass
- Website: https://aws.amazon.com/greengrass/
- Source: https://github.com/aws/aws-greengrass-core-sdk-c
- Version: v1.9.1
- Supports: JetPack 4.2.x, JetPack 4.3, JetPack 4.4 (Jetson Nano / TX1 / TX2 / Xavier)
- Forum Thread: https://devtalk.nvidia.com/default/topic/1052324/#5341970
1. Create Greengrass user group:
$ sudo adduser --system ggc_user
$ sudo addgroup --system ggc_group
2. Setup your AWS account and Greengrass group during this page: https://docs.aws.amazon.com/greengrass/latest/developerguide/gg-config.html
After downloading your unique security resource keys to your Jetson that were created in this step, proceed to #3 below.
3. Download the AWS IoT Greengrass Core Software (v1.9.1) for ARMv8 (aarch64):
$ wget https://d1onfpft10uf5o.cloudfront.net/greengrass-core/downloads/1.9.1/greengrass-linux-aarch64-1.9.1.tar.gz
4. Following step #4 from this page, extract Greengrass core and your unique security keys on your Jetson:
$ sudo tar -xzvf greengrass-linux-aarch64-1.9.1.tar.gz -C /
$ sudo tar -xzvf <hash>-setup.tar.gz -C /greengrass # these are the security keys downloaded above
5. Download AWS ATS endpoint root certificate (CA):
$ cd /greengrass/certs/
$ sudo wget -O root.ca.pem https://www.amazontrust.com/repository/AmazonRootCA1.pem
6. Start Greengrass core on your Jetson:
$ cd /greengrass/ggc/core/
$ sudo ./greengrassd start
You should get a message in your terminal Greengrass sucessfully started with PID: xxx
NVIDIA DeepStream
- Website: https://developer.nvidia.com/deepstream-sdk
- Version: 5.0
- Supports: JetPack >= 4.2 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier)
- FAQ: https://developer.nvidia.com/deepstream-faq
- GitHub Samples:
Containers
Docker
- Website: https://docker.com/
- Source: https://github.com/docker
- Version: 18.06
- Support: ≥ JetPack 3.2 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier)
- Installed by default in JetPack-L4T
Launch your container with --runtime nvidia
to enable GPU-passthrough.
Launch your container with --volume /tmp/argus_socket:/tmp/argus_socket
to enable access to MIPI CSI cameras.
See https://github.com/NVIDIA/nvidia-docker/wiki/NVIDIA-Container-Runtime-on-Jetson for more documentation on using Docker on Jetson.
To enable IPVLAN for Docker Swarm mode: https://blog.hypriot.com/post/nvidia-jetson-nano-build-kernel-docker-optimized/
Kubernetes
- Website: https://kubernetes.io/
- Source: https://github.com/kubernetes/
- Support: ≥ JetPack 3.2 (Jetson Nano / TX1 / TX2 / Xavier NX / AGX Xavier)
- Distributions:
- MicroK8s (v1.14)
$ sudo snap install microk8s --classic
- k3s (v0.5.0)
$ wget https://github.com/rancher/k3s/releases/download/v0.5.0/k3s-arm64
- MicroK8s (v1.14)
To configure L4T kernel for K8S: https://medium.com/@jerry_liang/deploy-gpu-enabled-kubernetes-pod-on-nvidia-jetson-nano-ce738e3bcda9
See also: https://medium.com/jit-team/building-a-gpu-enabled-kubernets-cluster-for-machine-learning-with-nvidia-jetson-nano-7b67de74172a