Get Started#

  • Time to Complete: 30 minutes

  • Programming Language: Python 3

Configure Docker#

To configure Docker:

  1. Run Docker as Non-Root: Follow the steps in Manage Docker as a non-root user.

  2. Configure Proxy (if required):

    • Set up proxy settings for Docker client and containers as described in Docker Proxy Configuration.

    • Example ~/.docker/config.json:

      {
  "proxies": {
    "default": {
      "httpProxy": "http://<proxy_server>:<proxy_port>",
      "httpsProxy": "http://<proxy_server>:<proxy_port>",
      "noProxy": "127.0.0.1,localhost"
    }
  }
}

    • Configure the Docker daemon proxy as per Systemd Unit File.

  3. Enable Log Rotation:

    • Add the following configuration to /etc/docker/daemon.json:

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

    • Reload and restart Docker:

      sudo systemctl daemon-reload
sudo systemctl restart docker

Clone source code#

git clone https://github.com/open-edge-platform/edge-ai-suites.git
cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-multimodal

Deploy with Docker Compose#

  1. Update the following fields in .env.

    • INFLUXDB_USERNAME

    • INFLUXDB_PASSWORD

    • VISUALIZER_GRAFANA_USER

    • VISUALIZER_GRAFANA_PASSWORD

    • MTX_WEBRTCICESERVERS2_0_USERNAME

    • MTX_WEBRTCICESERVERS2_0_PASSWORD

    • HOST_IP

  2. Deploy the sample app, use only one of the following options.

    NOTE:

    • The below make up fails if the above required fields are not populated as per the rules called out in .env file.

    • The sample app is deployed by pulling the pre-built container images of the sample app from the docker hub OR from the internal container registry (login to the docker registry from cli and configure DOCKER_REGISTRY env variable in .env file at edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-multimodal)

    • The CONTINUOUS_SIMULATOR_INGESTION variable in the .env file (for Docker Compose) is set to true by default, enabling continuous looping of simulator data. To ingest the simulator data only once (without looping), set this variable to false.

    • The update rate of the graph and table may lag by a few seconds and might not perfectly align with the video stream, since Grafana’s minimum refresh interval is 5 seconds.

    • The graph and table may initially display “No Data” because the Time Series Analytics Microservice requires some time to install its dependency packages before it can start running.

    cd <PATH_TO_REPO>/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-multimodal
make up

  3. Use the following command to verify that all containers are active and error-free.

    Note: The command make status may show errors in containers like ia-grafana when user have not logged in for the first login OR due to session timeout. Just login again in Grafana and functionality wise if things are working, then ignore user token not found errors along with other minor errors which may show up in Grafana logs.

    cd <PATH_TO_REPO>/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-multimodal
make status

Verify the Weld Defect Detection Results#

  1. Get into the InfluxDB* container.

     docker exec -it ia-influxdb bash

  2. Run following commands to see the data in InfluxDB*.

    NOTE: Please ignore the error message There was an error writing history file: open /.influx_history: read-only file system happening in the InfluxDB shell. This does not affect any functionality while working with the InfluxDB commands

    # For below command, the INFLUXDB_USERNAME and INFLUXDB_PASSWORD needs to be fetched from `.env` file
influx -username <username> -password <passwd>
use datain # database access
show measurements
# Run below query to check and output measurement processed
# by Time Series Analytics microservice
select * from "weld-sensor-anomaly-data"

  3. Check the output in Grafana.

    • Use link https://<host_ip>:3000 to launch Grafana from browser (preferably, chrome browser)

    • Login to the Grafana with values set for VISUALIZER_GRAFANA_USER and VISUALIZER_GRAFANA_PASSWORD in .env file and select Multimodal Weld Defect Detection Dashboard.

      Grafana login

    • After login, click on Dashboard Menu view

    • Select the Multimodal Weld Defect Detection Dashboard. Multimodal Weld Defect Detection Dashboard

    • One will see the below output.

      Anomaly prediction for weld data

Bring down the sample app#

cd <PATH_TO_REPO>/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-multimodal
make down

Check logs - troubleshooting#

Check the container logs to catch any failures:

docker ps
docker logs -f <container_name>
docker logs -f <container_name> | grep -i error

Advanced setup#