{"id":39228,"date":"2021-01-22T14:07:20","date_gmt":"2021-01-22T06:07:20","guid":{"rendered":"\/blog\/?p=39228"},"modified":"2021-01-22T14:07:22","modified_gmt":"2021-01-22T06:07:22","slug":"how-to-run-cluster-management-software-k3s-on-nvidia-jetson","status":"publish","type":"post","link":"https:\/\/www.seeedstudio.com\/blog\/2021\/01\/22\/how-to-run-cluster-management-software-k3s-on-nvidia-jetson\/","title":{"rendered":"How to run Cluster Management Software K3s on NVIDIA Jetson?"},"content":{"rendered":"\n

Ever thought of learning how to run a cluster management software in your IoT applications? This tutorial will take you through everything from the purpose why we’re choosing K3s to how you can run a complete test! This original tutorial is by GPUSolution.<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

Without further ado, let’s first talk about the purpose of using k3s:<\/p>\n\n\n\n

We\u2019ll be using Kubernetes lightweight management tool K3S, to build a Docker container cluster using 4 node devices. The purpose of this is as follows:<\/p>\n\n\n\n

  1. The Docker container is trending in the software development, this also includes Artificial Intelligence (AI) application<\/li>
  2. Kubernetes is currently the most commonly used cluster management application in the Docker field<\/li>
  3. Since K3S is a lightweight management tool, there would be fewer resources used, and it\u2019ll be much more convenient to install. Thus, making it suitable for embedded AIOT platform applications <\/li><\/ol>\n\n\n\n
    \n\n\n\n

    Let’s Get Started!<\/h3>\n\n\n\n

    This experiment will use Xavier NX as the master node, and 3 Jetson Nano 4GB as the worker node. All devices will be using Jetpack 4.4.1 version as the development environment, with Docker 1.19 version and Nvidia-docker2 management tool pre-installed.<\/p>\n\n\n\n

    \"\"<\/figure><\/div>\n\n\n\n
    \"\"<\/figure><\/div>\n\n\n\n

    In the process of the experiment, you\u2019ll also be required to download the compatible Jetpack 4.41, NVIDIA l4t-ml:r32.4.4-py3\u2019s mirror from NGC \uff08ngc.nvidia.com\uff09. This mirror supports a variety of Deep learning application frameworks and Jupyter interactive environment with an instruction.<\/p>\n\n\n\n

    \n\n\n\n

    Building K3S cluster using 4 Jetson nodes<\/strong><\/h3>\n\n\n\n

    Description of the cluster environment:<\/h4>\n\n\n\n

    Appoint a node as the Master and the other nodes as the workers in Seeed\u2019s devices. The example below shows the configuration of each node: (Set the IP portion of the table according to your environment)<\/p>\n\n\n\n

    Role<\/strong><\/td>IP<\/strong><\/td>Host Name<\/strong><\/td>Type of Device<\/strong><\/td>Jetpack version<\/strong><\/td><\/tr>
    Master<\/td>xx.xx.xx.30<\/td>Node0<\/td>Xavier NX<\/td>4.4.1<\/td><\/tr>
    Worker<\/td>xx.xx.xx.31<\/td>Node1<\/td>Jetson Nano 4GB<\/td>4.4.1<\/td><\/tr>
    Worker<\/td>xx.xx.xx.32<\/td>Node2<\/td>Jetson Nano 4GB<\/td>4.4.1<\/td><\/tr>
    Worker<\/td>xx.xx.xx.33<\/td>Node3<\/td>Jetson Nano 4GB<\/td>4.4.1<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    <\/div>\n\n\n\n

    Add all 4 IP and Hostname into the 4 nodes\u2019\/etc\/hosts folder<\/p>\n\n\n\n

    127.0.0.1      localhost<\/p>\n\n\n\n

    127.0.1.1      node3<= Set your own hostname<\/p>\n\n\n\n

    # Add all the cluster\nnodes\u2019 IP and Hostname down below<\/p>\n\n\n\n

    xx.xx.xx.30     node0<\/p>\n\n\n\n

    xx.xx.xx.31     node1<\/p>\n\n\n\n

    xx.xx.xx.32     node2<\/p>\n\n\n\n

    xx.xx.xx.33     node3<\/p>\n\n\n\n

    (Archive) <\/p>\n\n\n\n

    By doing this, you will be able to directly use every nodes\u2019 hostname instead of memorising their IP when you are performing operation later.<\/p>\n\n\n\n

    <\/div>\n\n\n\n

    Using K3S to build Nano management cluster<\/h4>\n\n\n\n
    • Install K3S Server on the Master (node0):<\/li><\/ul>\n\n\n\n

      The executing commands are as follows:
      curl -sfL https:\/\/get.k3s.io | INSTALL_K3S_EXEC=”–docker” sh -s \u2013<\/p>\n\n\n\n

      \"\"<\/figure><\/div>\n\n\n\n

      Check if the installation is completed:<\/p>\n\n\n\n

      docker images<\/p>\n\n\n\n

      \"\"<\/figure><\/div>\n\n\n\n

      sudo kubectl get node<\/p>\n\n\n\n

      \"\"<\/figure><\/div>\n\n\n\n

      To test whether you\u2019re able perform calculation, execute the third-party packaged cuda devicequery container: <\/p>\n\n\n\n

      sudo kubectl run -it nvidia –image=jitteam\/devicequery –restart=Never<\/p>\n\n\n\n

      If everything went well, the screenshot below will be shown:<\/p>\n\n\n\n

      \"\"<\/figure><\/div>\n\n\n\n
      • Install K3S agent on the 3 workers (node1\/node2\/node3): <\/li>
      • 1. First, locate the k3s server token on the Master (node0) and execute the following commands sudo cat \/var\/lib\/rancher\/k3s\/server\/node-token<\/li><\/ul>\n\n\n\n

        You should be able to see a similar alphabetic string about the length as shown below (it will be different):<\/p>\n\n\n\n

        \"\"<\/figure><\/div>\n\n\n\n

        2. On every worker (node1\/node2.node3), execute
        ” export k3s_token= <node-token alphabetical string “as shown in the previous step<\/p>\n\n\n\n

        \"\"<\/figure><\/div>\n\n\n\n

        export <\/p>\n\n\n\n

        k3s_url=”https:\/\/<IP_OF_MASTER>:6443″#<\/p>\n\n\n\n

        The <IP_OF_MASTER>is node0<\/p>\n\n\n\n

        \"\"<\/figure><\/div>\n\n\n\n

        Then execute the command as\nshown below:<\/p>\n\n\n\n

        curl -sfL https:\/\/get.k3s.io | K3S_URL=${k3s_url} K3S_TOKEN=${k3s_token} sh \u2013<\/p>\n\n\n\n

        \"\"<\/figure><\/div>\n\n\n\n

        *The steps, as shown above, are all executed on the worker nodes<\/em><\/p>\n\n\n\n

        3. Execute the following commands on Master, and check the agent installation:<\/p>\n\n\n\n

        sudo kubectl get nodes<\/p>\n\n\n\n

        \"\"<\/figure>\n\n\n\n

        This would indicate that 3 worker nodes have entered the scope of k3s management, but the roles haven\u2019t been set.<\/p>\n\n\n\n

        4. To set the roles for each worker, execute role setting command on the Master node (mode0)
        sudo kubectl label node node1 node2 node3 node-role.kubernetes.io\/worker=worker<\/p>\n\n\n\n

        \"\"<\/figure>\n\n\n\n

        Then check the status of\nthe node:<\/p>\n\n\n\n

        sudo kubectl get nodes<\/p>\n\n\n\n

        \"\"<\/figure>\n\n\n\n

        With that, you\u2019ve completed building the k3s cluster.<\/p>\n\n\n\n

        5. Check the cluster information an execute the following command:<\/p>\n\n\n\n

        sudo kubectl cluster-info<\/p>\n\n\n\n

        \"\"<\/figure>\n\n\n\n
        \n\n\n\n

        Executing NVIDIA l4t-ml container\u2019s TensorFlow<\/h3>\n\n\n\n
        • Download l4t-ml:r32.4.4-py3\u2019s mirror docker pull nvcr.io\/nvidia\/l4t-ml:r32.4.4-py3<\/li>
        • Type jetson-tf.yaml<\/li><\/ul>\n\n\n\n
          apiVersion: v1\nkind: Pod\nmetadata:\n  name: jetson-tf\nspec:\n  restartPolicy: OnFailure\n  containers:\n  - name: nvidia-l4t-ml\n    image: \"nvcr.io\/nvidia\/l4t-ml:r32.4.4-py3\"\n    command: [ \"\/bin\/bash\", \"-c\", \"--\" ]\n    args: [ \"while true; do sleep 30; done;\" ]<\/code><\/pre>\n\n\n\n
          • Check the pod status and execute:<\/li><\/ul>\n\n\n\n
            sudo kubectl get pod<\/p>\n\n\n\n
            \"\"<\/figure><\/div>\n\n\n\n
            • Once you\u2019ve confirmed that pod (jetson-tf) is running, it\u2019ll be ready for usage. But if its status states \u201cContainerCreating\u201d, wait till it states running<\/li>
            •  To activate this container, execute sudo kubectl exec -it jetson-tf — python3 <\/li><\/ul>\n\n\n\n
              \"\"<\/figure>\n\n\n\n
              Enter container\u2019s puthon3 interactive environment and execute the following codes:<\/p>\n\n\n\n
              from tensorflow.python.client import device_lib\ndevice_lib.list_local_devices()<\/code><\/pre>\n\n\n\n
              \"\"<\/figure><\/div>\n\n\n\n
              This would then display the GPUs supported by Tensorflow in the k3s container.<\/p>\n\n\n\n
              <\/div>\n\n\n\n
              For a complete test, you can further execute the following code in Python3:<\/p>\n\n\n\n
              from tensorflow.python.client import device_lib\ndef get_available_gpus():\n    local_device_protos = device_lib.list_local_devices()\nreturn [x.name for x in local_device_protos if x.device_type == 'GPU']\n\nget_available_gpus()<\/code><\/pre>\n\n\n\n
              \"\"<\/figure><\/div>\n\n\n\n
              After executing, you\u2019ll get the following output:<\/p>\n\n\n\n
              \"\"<\/figure><\/div>\n\n\n\n
              \n\n\n\n
              Summary<\/h2>\n\n\n\n
              And that’s all for our tutorial on running K3s on your devices! Do let us know if you’ve enjoyed this article and hope that this has helped you in any sort of way! <\/p>\n","protected":false},"excerpt":{"rendered":"
              Ever thought of learning how to run a cluster management software in your IoT applications?<\/p>\n","protected":false},"author":3529,"featured_media":39255,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_lmt_disableupdate":"","_lmt_disable":"","_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"iawp_total_views":0,"footnotes":""},"categories":[1],"tags":[3621,1827,3478,142],"class_list":["post-39228","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-kubernetes","tag-nvidia-jetson-nano-developer-kit","tag-nvidia-jetson-xavier-nx-developer-kit","tag-open-hardware"],"yoast_head":"\nHow to run Cluster Management Software K3s on NVIDIA Jetson? - Latest News from Seeed Studio<\/title>\n<meta name=\"description\" content=\"Ever thought of learning how to run a cluster management software in your IoT applications? 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