Contents

About Open Edge Insights for Industrial

Open Edge Insights for Industrial (Open EII) is a set of pre-validated ingredients for integrating video and time series data analytics on edge compute nodes. Open EII includes modules to enable data collection, storage, and analytics for both time series and video data.

Note

In this document, you will find labels of ‘Edge Insights for Industrial (EII)’ for file names, paths, code snippets, and so on. Consider the references of EII as Open EII. This is due to the product name change of EII as Open EII.

Minimum System Requirements

The following are the minimum system requirements to run Open EII:

System Requirement

Details

Processor

8th generation Intel® CoreTM processor onwards with Intel® HD Graphics or Intel® Xeon® processor

RAM

Minimum 16 GB

Hard drive

Minimum 128 GB and Recommended 256 GB

Operating system

Ubuntu 18.04 or Ubuntu 20.04

Note

  • To use Open EII, ensure that you are connected to the internet.

  • The recommended RAM capacity for video analytics pipeline is 16 GB. The recommended RAM for time series analytics pipeline is 4 GB with Intel® Atom processors.

  • Open EII is validated on Ubuntu 18.04 and Ubuntu 20.04. You can install Open EII stack on other Linux distributions with support for docker-ce and docker-compose tools.

Install Open Edge Insights for Industrial from ESH

Download and install EII by following the guide at https://www.intel.com/content/www/us/en/develop/documentation/edge-insights-industrial-doc/top/get-started-guide.html.

If one wishes to explore other capabilities of EII apart from the default vision and timeseries use cases they get to see from above ESH installation, they can follow the following sections to configure EII further:

Task 1: Install Prerequisites

The pre_requisites.sh script automates the installation and configuration of all the prerequisites required for building and running the Open EII stack. The prerequisites are as follows:

  • docker daemon

  • docker client

  • docker-compose

  • Python packages

The pre-requisites.sh script performs the following:

  • Checks if docker and docker-compose is installed in the system. If required, it uninstalls the older version and installs the correct version of docker and docker-compose.

  • Configures the proxy settings for the docker client and docker daemon to connect to the internet.

  • Configures the proxy settings system-wide (/etc/environment) and for docker. If a system is running behind a proxy, then the script prompts users to enter the proxy address to configure the proxy settings.

  • Configures proxy setting for /etc/apt/apt.conf to enable apt updates and installations.

Note

  • The recommended version of the docker-compose is 1.29.0. In versions older than 1.29.0, the video use case docker-compose.yml files and the device_cgroup_rules command may not work.

  • To use versions older than docker-compose 1.29.0, in the ia_video_ingestion and ia_video_analytics services, comment out the device_cgroup_rules command.

  • You can comment out the device_cgroup_rules command in the ia_video_ingestion and ia_video_analytics services to use versions older than 1.29.0 of docker-compose. This can result in limited inference and device support. The following code sample shows how the device_cgroup_rules commands are commented out:

    ia_video_ingestion:
 ...
  #device_cgroup_rules:
     #- 'c 189:* rmw'
     #- 'c 209:* rmw'

After modifying the docker-compose.yml file, refer to the Using the Builder script section. Before running the services using the docker-compose up command, rerun the builder.py script.

Run the Prerequisites Script

To run the prerequisite script, execute the following commands:

cd [WORKDIR]/IEdgeInsights/build
sudo -E ./pre_requisites.sh --help
  Usage :: sudo -E ./pre_requisites.sh [OPTION...]
  List of available options...
  --proxy         proxies, required when the gateway/edge node running Open EII (or any of Open EII profile) is connected behind proxy
  --help / -h         display this help and exit

Note

If the –proxy option is not provided, then script will run without proxy. Different use cases are as follows:

  • Runs without proxy

    sudo -E ./pre_requisites.sh

  • Runs with proxy

    sudo -E ./pre_requisites.sh --proxy="proxy.intel.com:891"

Optional Steps

  • If required, you can enable full security for production deployments. Ensure that the host machine and docker daemon are configured per the security recommendation. For more info, see build/docker_security_recommendation.md.

  • If required, you can enable log rotation for docker containers using any of the following methods:

Method 1

Set the logging driver as part of the docker daemon. This applies to all the docker containers by default.

  1. Configure the json-file driver as the default logging driver. For more info, see JSON File logging driver. The sample json-driver configuration that can be copied to /etc/docker/daemon.json is as follows:

      {
    "log-driver": "json-file",
    "log-opts": {
    "max-size": "10m",
    "max-file": "5"
    }
}

  2. Run the following command to reload the docker daemon:

    sudo systemctl daemon-reload

  3. Run the following command to restart docker:

    sudo systemctl restart docker
Method 2

Set logging driver as part of docker compose which is container specific. This overwrites the first option (i.e /etc/docker/daemon.json). The following example shows how to enable the logging driver only for the video_ingestion service:

  ia_video_ingestion:
    ...
    ...
    logging:
      driver: json-file
      options:
      max-size: 10m
max-file: 5

Task 2: Generate Deployment and Configuration Files

After downloading Open EII from the release package or git, run the commands mentioned in this section from the [WORKDIR]/IEdgeInsights/build/ directory.

Use the Builder Script

To use the Builder script, run the following command:

python3 builder.py -h
usage: builder.py [-h] [-f YML_FILE] [-v VIDEO_PIPELINE_INSTANCES]
                    [-d OVERRIDE_DIRECTORY]
optional arguments:
    -h, --help            show this help message and exit
    -f YML_FILE, --yml_file YML_FILE
                        Optional config file for list of services to include.
                        Eg: python3 builder.py -f video-streaming.yml
                        (default: None)
    -v VIDEO_PIPELINE_INSTANCES, --video_pipeline_instances VIDEO_PIPELINE_INSTANCES
                        Optional number of video pipeline instances to be
                        created. Eg: python3 builder.py -v 6 (default:
                        1)
    -d OVERRIDE_DIRECTORY, --override_directory OVERRIDE_DIRECTORY
                        Optional directory consisting of benchmarking
                        configs to be present in each app directory. Eg:
                        python3 builder.py -d benchmarking (default:
                        None)

Generate Consolidated Files for All Applicable Services of Open Edge Insights for Industrial

Using the Builder tool, Open EII auto-generates the configuration files that are required for deploying the Open EII services on a single node or multiple nodes. The Builder tool auto-generates the consolidated files by getting the relevant files from the Open EII service directories that are required for different Open EII use-cases. The Builder tool parses the top-level directories under the IEdgeInsights directory to generate the consolidated files.

The following table shows the list of consolidated files and their details:

Table: Consolidated files

file name

Description

docker-compose.yml

Consolidated docker-compose.yml file used to launch Open EII docker containers in each single node using the docker-compose tool

docker-compose.override.yml

Consolidated docker-compose-dev.override.yml of every app that is generated only in DEV mode for Open EII deployment on a given single node using docker-compose tool

docker-compose-build.yml

Consolidated docker-compose-build.yml file having Open EII base images and depends_on and build keys required for building Open EII services

docker-compose-push.yml

Consolidated docker-compose-push.yml file (same as docker-compose.yml file with just dummy build key added), used for pushing Open EII services to docker registry

eii_config.json

Consolidated config.json of every app which will be put into etcd during provisioning

values.yaml

Consolidated values.yaml of every app inside helm-eii/eii-deploy directory, which is required to deploy Open EII services via helm

Template yaml files

Files copied from helm/templates directory of every app to helm-eii/eii-deploy/templates directory, which are required to deploy Open EII services via helm

Note

  • If you modify an individual Open EII app/service directory files, make sure to re-run the builder.py script before running the Open EII stack to regenerate the updated consolidated files as above.

  • Manual editing of consolidated files is not recommended. Instead make changes to the respective files in the Open EII app/service directories and use the builder.py script to generate the consolidated files.

  • Enter the secret credentials in the # Service credentials section of the .env([WORK_DIR]/IEdgeInsights/build/.env) file if you are trying to run that Open EII app/service. It is mandatory to enter the service credentials. If the required credentials are not present, the builder.py script would be prompting till all the required credentails are entered. Protect the .env([WORK_DIR]/IEdgeInsights/build/.env) file from being read by other users by applying a suitable file access mask.

  • Special characters “~:’+[/@^{%(-”*|,&<`}._=}!>;?#$)" are not allowed in INFLUXDB_PASSWORD.

  • The length for the MINIO_ACCESS_KEY and the MINIO_SECRET_KEY must be minimum of 8 characters.

  • The builder_config.json([WORK_DIR]/IEdgeInsights/build/builder_config.json) is the config file for the builder.py script and it contains the following keys:

    • subscriber_list: This key contains a list of services that act as a subscriber to the stream being published.

    • publisher_list: This key contains a list of services that publishes a stream of data.

    • include_services: This key contains the mandatory list of services. These services should be included when the Builder is run without the -f flag.

    • exclude_services: This key contains the mandatory list of services. These services should be excluded when the Builder is run without the -f flag.

    • increment_rtsp_port: This is a Boolean key. It increments the port number for the RTSP stream pipelines.

To generate the consolidated files, run the following command:

python3 builder.py

Generate Consolidated Files for a Subset of Open Edge Insights for Industrial Services

Builder uses a yml file for configuration. The config yml file consists of a list of services to include. You can mention the service name as the path relative to IEdgeInsights or full path to the service in the config yml file. To include only a certain number of services in the Open EII stack, you can add the -f or yml_file flag of builder.py. You can find the examples of yml files for different use cases as follows:

  • Azure([WORK_DIR]/IEdgeInsights/build/usecases/video-streaming-azure.yml)

    The following example shows running Builder with the -f flag:

    python3 builder.py -f usecases/video-streaming.yml

  • Main Use Cases

Usecase

yaml file

Video + Timeseries

build/usecases/video-timeseries.yml([WORK_DIR]/IEdgeInsights/build/usecases/video-timeseries.yml)

Video

build/usecases/video.yml([WORK_DIR]/IEdgeInsights/build/usecases/video.yml)

Timeseries

build/usecases/time-series.yml([WORK_DIR]/IEdgeInsights/build/usecases/time-series.yml)

  • Video Pipeline Sub Use Cases

Usecase

yaml file

Video streaming

build/usecases/video-streaming.yml([WORK_DIR]/IEdgeInsights/build/usecases/video-streaming.yml)

Video streaming and historical

build/usecases/video-streaming-storage.yml([WORK_DIR]/IEdgeInsights/build/usecases/video-streaming-storage.yml)

Video streaming with AzureBridge

build/usecases/video-streaming-azure.yml([WORK_DIR]/IEdgeInsights/build/usecases/video-streaming-azure.yml)

Video streaming and custom udfs

build/usecases/video-streaming-all-udfs.yml([WORK_DIR]/IEdgeInsights/build/usecases/video-streaming-all-udfs.yml)

When you run the multi-instance config, a build/multi_instance directory is created in the build directory. Based on the number of video_pipeline_instances specified, that many directories of VideoIngestion and VideoAnalytics are created in the build/multi_instance directory.

The following section provides an example for running the Builder to generate the multi-instance boiler plate config for 3 streams of video-streaming use case.

Generate Multi-instance Config Using the Builder

If required, you can generate the multi-instance docker-compose.yml and config.json files using the Builder. You can use the -v or video_pipeline_instances flag of the Builder to generate boiler plate config for the multiple-stream use cases. The -v or video_pipeline_instances flag creates the multi-stream boiler plate config for the docker-compose.yml and eii_config.json files.

The following example shows running builder to generate the multi-instance boiler plate config for 3 streams of video-streaming use case:

python3 builder.py -v 3 -f usecases/video-streaming.yml

Using the previous command for 3 instances, the build/multi_instance directory consists of the following directories

  • VideoIngestion1

  • VideoIngestion2

  • VideoIngestion3

  • VideoAnalytics1

  • VideoAnalytics2

  • VideoAnalytics3

Initially each directory will have the default config.json and the docker-compose.yml files that are present within the VideoIngestion and the VideoAnalytics directories.

      ./build/multi_instance/
      |-- VideoAnalytics1
      |   |-- config.json
      |   `-- docker-compose.yml
      |-- VideoAnalytics2
      |   |-- config.json
      |   `-- docker-compose.yml
      |-- VideoAnalytics3
      |   |-- config.json
      |   `-- docker-compose.yml
      |-- VideoIngestion1
      |   |-- config.json
      |   `-- docker-compose.yml
      |-- VideoIngestion2
      |   |-- config.json
      |   `-- docker-compose.yml
      `-- VideoIngestion3
          |-- config.json
          `-- docker-compose.yml

You can edit the config of each of these streams within the ``build/multi_instance`` directory. To generate the consolidated ``docker compose`` and ``eii_config.json`` file, rerun the ``builder.py`` command.

Note

  • The multi-instance feature support of Builder works only for the video pipeline i.e., usecases/video-streaming.yml use case alone and not with any other use case yml files like usecases/video-streaming-storage.yml and so on. Also, it doesn’t work for cases without the -f switch. The previous example will work with any positive number for -v. To learn more about using the multi-instance feature with the DiscoverHistory tool, see Multi-instance feature support for the builder script with the DiscoverHistory tool.

  • If you are running the multi-instance config for the first time, it is recommended not to change the default config.json file and the docker-compose.yml file in the VideoIngestion and VideoAnalytics directories.

  • If you are not running the multi-instance config for the first time, the existing config.json and docker-compose.yml files in the build/multi_instance directory will be used to generate the consolidated eii-config.json and docker-compose files.

  • The docker-compose.yml files present within the build/multi_instance directory will have the updated service_name, container_name, hostname, AppName, ports and secrets for that respective instance.

  • The config.json file in the build/multi_instance directory will have the updated Name, Type, Topics, Endpoint, PublisherAppname, ServerAppName, and AllowedClients for the interfaces section and incremented RTSP port number for the config section of that respective instance.

  • Ensure that all the containers are down before running the multi-instance configuration. Run the docker-compose down command before running builder.py for the multi-instance configuration.

Generate Benchmarking Config Using Builder

Use the -d or the override_directory flag to provide a different set of docker-compose.yml and config.json files other than the existing files in every service directory. The -d or the override_directory flag indicates to search for the required set of files within a directory provided by the flag.

For example, to pick files from a directory named benchmarking, you can run the following command:

python3 builder.py -d benchmarking

Note

  • If you use the override directory feature of the builder then include all the 3 files mentioned in the previous example. If you do not include a file in the override directory, then the Builder will omit that service in the final config that is generated.

  • Adding the AppName of the subscriber container or client container in the subscriber_list of builder_config.json allows you to spawn a single subscriber container or client container that is subscribing or receiving on multiple publishers or server containers.

  • Multiple containers specified by the -v flag is spawned for services that are not mentioned in the subscriber_list. For example, if you run Builder with –v 3 option and Visualizer is not added in the subscriber_list of builder_config.json then 3 instances of Visualizer are spawned. Each instance subscribes to 3 VideoAnalytics services. If Visualizer is added in the subscriber_list of builder_config.json, a single Visualizer instance subscribing to 3 multiple VideoAnalytics is spawned.

Add the Open Edge Insights for Industrial Services

This section provides information about adding a new service, subscribing to the VideoAnalytics([WORK_DIR]/IEdgeInsights/VideoAnalytics), and publishing it on a new port. Add a service to the Open EII stack as a new directory in the IEdgeInsights([WORK_DIR]/IEdgeInsights/) directory. The Builder registers and runs any service present in its own directory in the IEdgeInsights([WORK_DIR]/IEdgeInsights/) directory. The directory should contain the following:

  • A docker-compose.yml file to deploy the service as a docker container. The AppName is present in the environment section in the docker-compose.yml file. Before adding the AppName to the main build/eii_config.json, it is appended to the config and interfaces as /AppName/config and /AppName/interfaces.

  • A config.json file that contains the required config for the service to run after it is deployed. The config.json consists of the following:

    • A config section, which includes the configuration-related parameters that are required to run the application.

    • An interfaces section, which includes the configuration of how the service interacts with other services of the Open EII stack.

Note

For more information on adding new Open EII services, refer to the Open EII sample apps at Samples written in C++, Python, and Golang using the Open EII core libraries.

The following example shows:

  • How to write the config.json for any new service

  • Subscribe to VideoAnalytics

  • Publish on a new port

{
    "config": {
        "paramOne": "Value",
        "paramTwo": [1, 2, 3],
        "paramThree": 4000,
        "paramFour": true
    },
    "interfaces": {
        "Subscribers": [
            {
                "Name": "default",
                "Type": "zmq_tcp",
                "EndPoint": "127.0.0.1:65013",
                "PublisherAppName": "VideoAnalytics",
                "Topics": [
                    "camera1_stream_results"
                ]
            }
        ],
        "Publishers": [
            {
                "Name": "default",
                "Type": "zmq_tcp",
                "EndPoint": "127.0.0.1:65113",
                "Topics": [
                    "publish_stream"
                ],
                "AllowedClients": [
                    "ClientOne",
                    "ClientTwo",
                    "ClientThree"
                ]
            }
        ]
    }
}

The config.json file consists of the following key and values:

  • value of the config key is the config required by the service to run.

  • value of the interfaces key is the config required by the service to interact with other services of Open EII stack over the Message Bus.

  • the Subscribers value in the interfaces section denotes that this service should act as a subscriber to the stream being published by the value specified by PublisherAppName on the endpoint mentioned in value specified by EndPoint on topics specified in value of Topic key.

  • the Publishers value in the interfaces section denotes that this service publishes a stream of data after obtaining and processing it from VideoAnalytics. The stream is published on the endpoint mentioned in value of EndPoint key on topics mentioned in the value of Topics key.

  • the services mentioned in the value of AllowedClients are the only clients that can subscribe to the published stream, if it is published securely over the Message Bus.

Note

  • Like the interface keys, Open EII services can also have Servers and Clients interface keys. For more information, refer config.json([WORK_DIR]/IEdgeInsights/VideoIngestion/config.json) of the VideoIngestion service and config.json([WORK_DIR]/IEdgeInsights/tools/SWTriggerUtility/config.json) of SWTriggerUtility tool.

  • For more information on the interfaces key responsible for the Message Bus endpoint configuration, refer common/libs/ConfigMgr/README.md#interfaces.

  • For the etcd secrets configuration, in the new Open EII service or app docker-compose.yml file, add the following volume mounts with the right AppName env value:

...
 volumes:
   - ./Certificates/[AppName]:/run/secrets/[AppName]:ro
   - ./Certificates/rootca/cacert.pem:/run/secrets/rootca/cacert.pem:ro

Distribute the Open Edge Insights for Industrial Container Images

The Open EII services are available as pre-built container images in the Docker Hub at https://hub.docker.com/u/openedgeinsights. To access the services that are not available in the Docker Hub, build from source before running the docker-compose up -d command. For example:

# Update the DOCKER_REGISTRY value in [WORKDIR]/IEdgeInsights/build/.env as DOCKER_RESISTRY=<docker_registry> (Make sure `docker login <docker_registry>` to the docker registry works)
cd [WORKDIR]/IEdgeInsights/build
# Base images that need to be built
docker-compose -f docker-compose-build.yml build ia_eiibase
docker-compose -f docker-compose-build.yml build ia_common
# Assuming here that the `python3 builder.py` step has been executed and ia_kapacitor
# Service exists in the generated compose files
docker-compose -f docker-compose-build.yml build ia_kapacitor
docker-compose up -d
# Push all the applicable Open EII images to <docker_registry>. Ensure to use the same DOCKER_REGISTRY value on the deployment machine while deployment
docker-compose -f docker-compose-push.yml push

The list of pre-built container images that are accessible at https://hub.docker.com/u/openedgeinsights is as follows:

  • Provisioning images

    • openedgeinsights/ia_configmgr_agent

  • Common Open EII images applicable for video and time series use cases

    • openedgeinsights/ia_etcd_ui

    • openedgeinsights/ia_influxdbconnector

    • openedgeinsights/ia_rest_export

    • openedgeinsights/ia_opcua_export

  • Video pipeline images

    • openedgeinsights/ia_video_ingestion

    • openedgeinsights/ia_video_analytics

    • openedgeinsights/ia_web_visualizer

    • openedgeinsights/ia_visualizer

    • openedgeinsights/ia_imagestore

    • openedgeinsights/ia_azure_bridge

    • openedgeinsights/ia_azure_simple_subscriber

  • Time series pipeline images

    • openedgeinsights/ia_grafana

    • openedgeinsights/ia_telegraf

    • openedgeinsights/ia_kapacitor

Note

Additionally, we have openedgeinsights/ia_edgeinsights_src image available at https://hub.docker.com/u/openedgeinsights which consists of source code of the GPL/LGPL/AGPL components of the Open EII stack.

List of Open Edge Insights for Industrial Services

Based on requirement, you can include or exclude the following Open EII services in the [WORKDIR]/IEdgeInsights/build/docker-compose.yml file:

Task 3: Build and Run the Open Edge Insights for Industrial Video and Time Series Use Cases

Note

  • For running the Open EII services in the IPC mode, ensure that the same user is mentioned in the publisher services and subscriber services.

  • If the publisher service is running as root such as VI, VA, then the subscriber service should also run as root. For example, in the docker-compose.ymlfile, if you have specified user: ${EII_UID} in the publisher service, then specify the same user: ${EII_UID} in the subscriber service. If you have not specified a user in the publisher service, then don’t specify the user in the subscriber service.

  • If services need to be running in multiple nodes in the TCP mode of communication, msgbus subscribers, and clients of AppName are required to configure the “EndPoint” in config.json with the HOST_IP and the PORT under Subscribers/Publishers or Clients/Servers interfaces section.

  • Ensure that the port is being exposed in the docker-compose.yml of the respective AppName. For example, if the "EndPoint": <HOST_IP>:65012 is configured in the config.json file, then expose the port 65012 in the docker-compose.yml file of the ia_video_ingestion service.

ia_video_ingestion:
  ...
  ports:
    - 65012:65012

Run all the following Open EII build and commands from the [WORKDIR]/IEdgeInsights/build/ directory. Open EII supports the following use cases to run the services mentioned in the docker_compose.yml file. Refer to Task 2 to generate the docker_compose.yml file based on a specific use case. For more information and configuration, refer to the [WORK_DIR]/IEdgeInsights/README.md file.

Build the Open Edge Insights for Industrial Stack

Note

  • This is an optional step if you want to use the Open EII pre-built container images and don’t want to build from source. For more details, refer: Distribution of OEI container images

  • Base Open EII services like ia_eiibase, ia_video_common, and so on, are needed only at the build time and not at the runtime.

Run the following command to build all Open EII services in the build/docker-compose-build.yml along with the base Open EII services.

docker-compose -f docker-compose-build.yml build

If any of the services fails during the build, then run the following command to build the service again:

docker-compose -f docker-compose-build.yml build --no-cache <service name>

Run Open Edge Insights for Industrial Services

Note

Ensure to run docker-compose down from the build([WORK_DIR]/IEdgeInsights/build) directory before you bring up the Open EII stack. This helps to remove running containers and avoid any sync issues where other services have come up before ia_configmgr_agent container has completed the provisioning step. If the images tagged with the EII_VERSION label, as in the build/.env([WORK_DIR]/IEdgeInsights/build/.env) do not exist locally in the system but are available in the Docker Hub, then the images will be pulled during the docker-compose upcommand.

Open Edge Insights for Industrial Provisioning

The Open EII provisioning is taken care by the ia_configmgr_agent service that gets launched as part of the Open EII stack. For more details on the ConfigMgr Agent component, refer to the Readme.

Start Open Edge Insights for Industrial in Dev Mode

Note

  • By default, Open EII is provisioned in the secure mode.

  • It is recommended not to use Open EII in the Dev mode in a production environment. In the Dev mode, all security features, communication to and from the etcd server over the gRPC protocol, and the communication between the Open EII services/apps over the ZMQ protocol are disabled.

  • By default, the Open EII empty certificates folder Certificates([WORK_DIR]/IEdgeInsights/Certificates]) will be created in the DEV mode. This happens because of docker bind mounts but it is not an issue.

  • The EII_INSTALL_PATH in the build/.env([WORK_DIR]/IEdgeInsights/build/.env) remains protected both in the DEV and the PROD mode with the Linux group permissions.

Starting Open EII in the Dev mode eases the development phase for System Integrators (SI). In the Dev mode, all components communicate over non-encrypted channels. To enable the Dev mode, set the environment variable DEV_MODE to true in the [WORK_DIR]/IEdgeInsights/build/.env file. The default value of this variable is false.

To provision Open EII in the developer mode, complete the following steps:

  1. Update DEV_MODE=true in [WORK_DIR]/IEdgeInsights/build/.env.

  2. Rerun the build/builder.py to regenerate the consolidated files.

Start Open Edge Insights for Industrial in Profiling Mode

The Profiling mode is used for collecting the performance statistics in Open EII. In this mode, each Open EII component makes a record of the time needed for processing any single frame. These statistics are collected in the visualizer where System Integrators (SIs) can see the end-to-end processing time and the end-to-end average time for individual frames.

To enable the Profiling mode, in the [WORK_DIR]/IEdgeInsights/build/.env file, set the environment variable PROFILING to true.

Run Provisioning Service and Rest of the Open Edge Insights for Industrial Stack Services

Note

  • Use the Etcd UI to modify the service configs post starting the Open EII services.

  • As seen in the build/eii_start.sh([WORK_DIR]/IEdgeInsights/build/eii_start.sh), Open EII provisioning and deployment happens in a 2 step process where you need to wait for the initialization of the provisioning container (ia_configmgr_agent) before bringing up the rest of the stack. Don’t use commands like docker-compose restart as it will randomly restart all the services leading to issues. To restart any service, use command like docker-compose restart [container_name] or docker restart [container_name].

# The optional TIMEOUT argument passed below is in seconds and if not provided it will wait
# till the "Provisioning is Done" message show up in `ia_configmgr_agent` logs before
# bringing up rest of the Open EII stack
cd [WORK_DIR]/IEdgeInsights/build
./eii_start.sh [TIMEOUT]

# To start the built-in visualizer service, run this command once in the terminal
xhost +

On successful run, the Visualizer UI displays the results of video analytics for all video use cases.

Push the Required Open Edge Insights for Industrial Images to Docker Registry

Note

By default, if DOCKER_REGISTRY is empty in build/.env([WORK_DIR]/IEdgeInsights/build/.env) then the images are published to hub.docker.com. Ensure to remove openedgeinsights/ org from the image names while pushing to Docker Hub. Repository names or image names with multiple slashes are not supported. This limitation doesn’t exist in other docker registries like the Azure Container Registry (ACR), Harbor registry, and so on.

Run the following command to push all the Open EII service docker images in the build/docker-compose-push.yml. Ensure to update the DOCKER_REGISTRY value in the .env([WORK_DIR]/IEdgeInsights/build/.env) file.

docker-compose -f docker-compose-push.yml push

Video Pipeline Analytics

This section provides more information about working with the video pipeline.

Enable Camera-based Video Ingestion

For detailed description on configuring different types of cameras and filter algorithms, refer to the VideoIngestion/README.md.

Use Video Accelerators in Ingestion and Analytics Containers

Open EII supports running inference on CPU, GPU, MYRIAD (NCS2), and HDDL devices by accepting the device value (“CPU”|”GPU”|”MYRIAD”|”HDDL”), part of the udf object configuration in the udfs key. The device field in the UDF config of udfs key in the VideoIngestion and VideoAnalytics configs can be updated at runtime via EtcdUI([WORK_DIR]/IEdgeInsights/EtcdUI) interface, the VideoIngestion and VideoAnalytics services will auto-restart. For more details on the UDF config, refer common/udfs/README.md.

Note

There is an initial delay of up to ~30 seconds while running inference on GPU (only for the first frame) as dynamically certain packages get created during runtime.

To Run on the USB Devices

For actual deployment if USB camera is required then mount the device node of the USB camera for ia_video_ingestion service. When multiple USB cameras are connected to host machine, the required camera should be identified with the device node and mounted.

For example, mount the two USB cameras connected to the host machine with device node as video0 and video1.

ia_video_ingestion:
  ...
  devices:
    - "/dev/dri"
    - "/dev/video0:/dev/video0"
    - "/dev/video1:/dev/video1"

Note

/dev/dri is required for graphic drivers.

To Run on the MYRIAD Devices

Note

In the IPC mode, when publisher such as ia_video_ingestion, ia_video_analytics, or custom_udfs is running with the root user permissions then the subscribers such as ia_visualizer, ia_imagestore, or ia_influxdbconnector should also run as root.

At runtime, use the root user permissions to run inference on a MYRIAD device. To enable the root user at runtime in ia_video_ingestion, ia_video_analytics, or custom UDF services, add user: root in the respective docker-compose.yml file. Refer to the following example:

ia_video_ingestion:
  ...
  user: root

Note

In the IPC mode when publisher (example, ia_video_ingestion, ia_video_analytics, or custom_udfs) is running with the root user permissions then the subscribers (For example ia_visualizer, ia_imagestore, ia_influxdbconnectorm, ia_video_profiler etc.) should also run as root by adding user: root in the respective docker-compose.yml file. To enable root user at runtime in ia_video_analytics or custom UDF services based on ia_video_analytics, set user: root in the respective docker-compose.yml file. Refer the following example:

ia_video_analytics:
  ...
  user: root
Troubleshooting Issues for the MYRIAD (NCS2) Devices

If the NC_ERROR occurs during device initialization of NCS2 stick then use the following workaround. Replug the device for the init, if the NCS2 devices fails to initialize during running Open EII. To check if initialization is successful, run **dmesg** and **lsusb** as follows:

lsusb | grep "03e7" (03e7 is the VendorID and 2485 is one of the  productID for MyriadX)

dmesg > dmesg.txt
[ 3818.214919] usb 3-4: new high-speed USB device number 10 using xhci_hcd
[ 3818.363542] usb 3-4: New USB device found, idVendor=03e7, idProduct=2485
[ 3818.363546] usb 3-4: New USB device strings: Mfr=1, Product=2, SerialNumber=3
[ 3818.363548] usb 3-4: Product: Movidius MyriadX
[ 3818.363550] usb 3-4: Manufacturer: Movidius Ltd.
[ 3818.363552] usb 3-4: SerialNumber: 03e72485
[ 3829.153556] usb 3-4: USB disconnect, device number 10
[ 3831.134804] usb 3-4: new high-speed USB device number 11 using xhci_hcd
[ 3831.283430] usb 3-4: New USB device found, idVendor=03e7, idProduct=2485
[ 3831.283433] usb 3-4: New USB device strings: Mfr=1, Product=2, SerialNumber=3
[ 3831.283436] usb 3-4: Product: Movidius MyriadX
[ 3831.283438] usb 3-4: Manufacturer: Movidius Ltd.
[ 3831.283439] usb 3-4: SerialNumber: 03e72485
[ 3906.460590] usb 3-4: USB disconnect, device number 11

To run on the HDDL Devices

Complete the following steps to run inference on HDDL devices:

  1. Download the full package for OpenVINO toolkit for Linux version “2021 4.2 LTS”

    (OPENVINO_IMAGE_VERSION used in build/.env([WORK_DIR]/IEdgeInsights/build/.env)) from the official website (https://software.intel.com/en-us/openvino-toolkit/choose-download/free-download-linux).

  2. Refer to the following link to install OpenVINO on the hostsystem

    • OpenVINO install: https://docs.openvinotoolkit.org/2021.4/_docs_install_guides_installing_openvino_linux.html#install-openvino

      Note: OpenVINO 2021.4 installation creates a symbolic link to the latest installation with file name as openvino_2021 instead of openvino. You can create a symbolic link with file name as openvino to the latest installation as follows:

      cd /opt/intel
sudo ln -s <OpenVINO latest installation> openvino

      Example: sudo ln -s openvino_2021.4.752 openvino

      Uninstall the older versions of OpenVINO if it is installed on the host system.

  3. Refer to the following link to configure HDDL with root user rights

  4. After completing the HDDL setup, run the following commands with root user rights.

    source /opt/intel/openvino/bin/setupvars.sh
$HDDL_INSTALL_DIR/bin/hddldaemon

Note

  • HDDL Daemon should run in a different terminal or in the background on the host system where inference performed.

  • HDDL use cases were tested on host system with Ubuntu 20.04 kernel 5.13.0-27-generic by configuring and running HDDL daemon with root user rights.

  • HDDL plugin can have the ION driver compatibility issues with some versions of the Ubuntu kernel. If there are compatibility issues, then ION driver may not be installed and hddldaemon will use shared memory. To work with shared memory in docker environment, configure and run HDDL with root user rights.

  • To check the supported Ubuntu kernel versions, refer to the OpenVINO-Release-Notes.

  • For actual deployment, mount only the required devices for services using OpenVINO with HDDL (ia_video_analytics or ia_video_ingestion) in docker-compose.yml file.

  • For example, mount only the Graphics and HDDL ion device for the ia_video_anaytics service. Refer to the following code snippet:

ia_video_analytics:
  ...
  devices:
          - "/dev/dri"
          - "/dev/ion:/dev/ion"
Troubleshooting Issues for the HDDL Devices

To Run on Intel(R) Processor Graphics (GPU/iGPU)

At runtime, use the root user permissions to run inference on a GPU device. To enable root user at runtime in ia_video_ingestion, ia_video_analytics, or custom UDF services, add user: root in the respective docker-compose.yml file. Refer the following example:

ia_video_ingestion:
  ...
  user: root

To enable root user at runtime in ia_video_analytics or any of the custom UDF services based on ia_video_analytics, set user: root in the respective docker-compose.yml file. For example, refer to the following:

ia_video_analytics:
  ...
  user: root

Note

  • In the IPC mode, when the publisher (example, ia_video_ingestion or ia_video_analytics) is running as root then the subscriber (For example ia_visualizer, ia_imagestore, ia_influxdbconnectorm, ia_video_profiler etc.) should also run as root by adding user: root in the respective docker-compose.yml file.

  • If you get a Failed to create plugin for device GPU/ clGetPlatformIDs error message, then check if the host system supports GPU device. Try installing the required drivers from OpenVINO-steps-for-GPU. Certain platforms like TGL can have compatibility issues with the Ubuntu kernel version. Ensure the compatible kernel version is installed.

Custom User Defined Functions

Open EII supports the following custom User Defined Functions (UDFs):

  • Build or run custom UDFs as standalone applications:

    • For running a custom UDF as a standalone application, download the video-custom-udfs repo and refer to the CustomUdfs/README.md.

  • Build or run custom UDFs in VI or VA:

Time Series Analytics

For time series data, a sample analytics flow uses Telegraf for ingestion, Influx DB for storage and Kapacitor for classification. This is demonstrated with an MQTT based ingestion of sample temperature sensor data and analytics with a Kapacitor UDF which does threshold detection on the input values. The services mentioned in build/usecases/time-series.yml([WORK_DIR]/IEdgeInsights/build/usecases/time-series.yml) will be available in the consolidated docker-compose.yml and consolidated build/eii_config.json of the Open EII stack for time series use case when built via builder.py as called out in previous steps. This will enable building of Telegraf and the Kapacitor based analytics containers. More details on enabling this mode can be referred from Kapacitor/README.md The sample temperature sensor can be simulated using the tools/mqtt/README.md application.

Open Edge Insights for Industrial Multi-node Cluster Deployment

With K8s Orchestrator

You can use any of the following options to deploy Open EII on a multi-node cluster:

  • [Recommended] For deploying through ansible playbook on multiple nodes automatically, refer build/ansible/README.md

  • For information about using helm charts to provision the node and deploy the Open EII services, refer build/helm-eii/README.md

Open Edge Insights for Industrial Tools

The Open EII stack consists of the following set of tools that also run as containers:

Open Edge Insights for Industrial Uninstaller

The Open EII uninstaller script automates the removal of all the Open EII Docker configuration that are installed on a system. The uninstaller performs the following tasks:

  • Stops and removes all the Open EII running and stopped containers.

  • Removes all the Open EII docker volumes.

  • Removes all the Open EII docker images [Optional]

  • Removes all Open EII install directory

To run the uninstaller script, run the following commmand from the [WORKDIR]/IEdgeInsights/build/ directory

./eii_uninstaller.sh -h

Usage: ./eii_uninstaller.sh [-h] [-d] This script uninstalls the previous Open EII version. Where:

-h show the help -d triggers the deletion of docker images (by default it will not trigger)

Example:

  • Run the following command to delete the Open EII containers and volumes:

    ./eii_uninstaller.sh

  • Run the following command to delete the Open EII containers, volumes, and images:

    export EII_VERSION=2.4
./eii_uninstaller.sh -d

The commands in the example will delete the version 2.4 Open EII containers, volumes, and all the docker images.

Debugging Options

Perform the following steps for debugging:

  1. Run the following command to check if all the Open EII images are built successfully:

    docker images|grep ia

  2. You can view all the dependency containers and the Open EII containers that are up and running. Run the following command to check if all containers are running:

    docker ps

  3. Ensure that the proxy settings are correctly configured and restart the docker service if the build fails due to no internet connectivity.

  4. Run the docker ps command to list all the enabled containers that are included in the docker-compose.yml file.

  5. From video ingestion>video analytics>visualizer, check if the default video pipeline with Open EII is working fine.

  6. The /opt/intel/eii root directory gets created - This is the installation path for Open EII:

    • data/ - stores the backup data for persistent imagestore and influxdb

    • sockets/ - stores the IPC ZMQ socket files

The following table displays useful docker-compose and docker commands:

Command

Description

docker-compose -f docker-compose-build.yml build

Builds all the service containers

docker-compose -f docker-compose-build.yml build [serv_cont_name]

Builds a single service container

docker-compose down

Stops and removes the service containers

docker-compose up -d

Brings up the service containers by picking the changes done in the docker-compose.yml file

docker ps

Checks the running containers

docker ps -a

Checks the running and stopped containers

docker stop $(docker ps -a -q)

Stops all the containers

docker rm $(docker ps -a -q)

Removes all the containers. This is useful when you run into issue of already container is in use

[docker compose cli]

For more information refer to the docker documentation

[docker compose reference]

For more information refer to the docker documentation

[docker cli]

For more information refer to the docker documentation

docker-compose run --no-deps [service_cont_name]

To run the docker images separately or one by one. For example: docker-compose run --name ia_video_ingestion --no-deps   ia_video_ingestion to run the VI container and the switch --no-deps will not bring up its dependencies mentioned in the docker-compose file. If the container does not launch, there could be some issue with the entrypoint program. You can override by providing the extra switch --entrypoint /bin/bash before the service container name in the docker-compose run command. This will let you access the container and run the actual entrypoint program from the container’s terminal to root cause the issue. If the container is running and you want to access it then, run the command: docker-compose exec [service_cont_name] /bin/bash or docker exec -it [cont_name] /bin/bash

docker logs -f [cont_name]

Use this command to check logs of containers

docker-compose logs -f

To see all the docker-compose service container logs at once

Web Deployment Tool

Web Deployment Tool is a GUI tool to facilitate Open EII configuration and deployment for single and multiple video streams.

Web Deployment Tool features include:

  • Offers GUI interface to try out Open EII stack for video use case

  • Supports multi-instance feature of VI/VA services

  • Supports an easy way to use or modify existing UDFs or add new UDFs

  • Supports preview to visualize the analysed frames

  • Supports deployment of the tested configuration on other remote nodes via ansible

To learn about launching and using the Web Deployment Tool, refer to the following:

Troubleshooting Guide

  • For any troubleshooting tips related to the Open EII configuration and installation, refer to the TROUBLESHOOT.md guide.

  • If you observe any issues with the Python package installation, then manually install the Python packages as follows:

    Note: To avoid any changes to the Python installation on the system, it is recommended that you use a Python virtual environment to install the Python packages. The details for setting up and using the Python virtual environment is available here: https://www.geeksforgeeks.org/python-virtual-environment/.

    cd [WORKDIR]/IEdgeInsights/build
# Install requirements for builder.py
pip3 install -r requirements.txt