Run Multiple Apps#

Time to Complete: 30 minutes
Programming Language: Python 3

Prerequisites#

System Requirements

Overview#

This tutorial demonstrates how to simultaneously deploy and manage multiple industrial edge AI vision applications using Docker Compose. You’ll learn to configure and run multiple instances of the same application or different applications in parallel, with each instance operating its own isolated DLStreamer Pipeline Server and associated services, all accessible through dedicated NGINX proxy configurations.

What you’ll learn:

How to configure multiple application instances with unique port assignments
How to independently deploy, start, stop, and monitor multiple running applications
How to access and view streams from each application instance

Set up the Applications#

Clone the edge-ai-suites repository and navigate to the industrial-edge-insights-vision directory:

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

Create a config.yml file to define your application instances and their unique port configurations. Add the following sample contents and save.

Example:
```
pallet-defect-detection:
  pdd1:
    NGINX_HTTP_PORT: 8080
    NGINX_HTTPS_PORT: 8443
    COTURN_UDP_PORT: 3478
    MINIO_SERVER_PORT: 8001
  pdd2:
    NGINX_HTTP_PORT: 9080
    NGINX_HTTPS_PORT: 9443
    COTURN_UDP_PORT: 3479
    MINIO_SERVER_PORT: 9001

weld-porosity:
  weld1:
    NGINX_HTTP_PORT: 10080
    NGINX_HTTPS_PORT: 10443
    COTURN_UDP_PORT: 3480
    MINIO_SERVER_PORT: 10001
```
Note: A sample configuration file sample_config.yml is provided to help users understand the multi-instance setup and get started. This configuration defines three example instances with identifiers: pdd1, pdd2, and weld1. The accompanying sample scripts utilize these identifiers to perform operations on individual application instances.

Edit the environment variables below in .env_<SAMPLE_APP> files for all sample apps present in config.yml.

For the example above, modify the envs for pallet-defect-detection and weld-porosity i.e. env_pallet-defect-detection and .env_weld-porosity

HOST_IP=<HOST_IP>   # IP address of server where DL Streamer Pipeline Server is running.

MINIO_ACCESS_KEY=   # MinIO service & client access key e.g. intel1234
MINIO_SECRET_KEY=   # MinIO service & client secret key e.g. intel1234

MTX_WEBRTCICESERVERS2_0_USERNAME=<username>  # WebRTC credentials e.g. intel1234
MTX_WEBRTCICESERVERS2_0_PASSWORD=<password>

Install pre-requisites for all the instances:
```
./setup.sh
```
This does the following:
- Parses through the config.yml
- Downloads resources for each instance
- Creates a folder temp_apps/<SAMPLE_APP>/<INSTANCE_NAME> that contains configs folder, .env file and payload.json
- Updates and adds the ports mentioned in config.yml to the respective .env file
- Sets executable permissions for scripts

Deploy the Applications#

Deploy all the application instances#

Deploy all the instances given in config.yml:
```
./run.sh up
```
It starts all the containers for each instance in config.yml
Verify all containers are running:
```
docker ps
```

Start AI Pipelines#

Start pipeline for all instances#

Fetch the list of pipelines for all instances:

./sample_list.sh

Example Output:

-------------------------------------------
Status of: pdd1 (SAMPLE_APP: pallet-defect-detection)
-------------------------------------------
Environment variables loaded from /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd1/.env
Running sample app: pallet-defect-detection
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Getting list of loaded pipelines...
Loaded pipelines:
[
{
   "description": "DL Streamer Pipeline Server pipeline",
   "name": "user_defined_pipelines",
   "parameters": {
      "properties": {
      "detection-properties": {
         "element": {
            "format": "element-properties",
            "name": "detection"
         }
      }
        ...
-------------------------------------------
Status of: pdd2 (SAMPLE_APP: pallet-defect-detection)
-------------------------------------------
Environment variables loaded from /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd2/.env
Running sample app: pallet-defect-detection
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Getting list of loaded pipelines...
Loaded pipelines:
[
{
   "description": "DL Streamer Pipeline Server pipeline",
   "name": "user_defined_pipelines",
   "parameters": {
      "properties": {
      "detection-properties": {
         "element": {
            "format": "element-properties",
            "name": "detection"
         }
      }
      ...
-------------------------------------------
Status of: weld1 (SAMPLE_APP: weld-porosity)
-------------------------------------------
Environment variables loaded from /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/weld-porosity/weld1/.env
Running sample app: weld-porosity
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Getting list of loaded pipelines...
Loaded pipelines:
[
{
   "description": "DL Streamer Pipeline Server pipeline",
   "name": "user_defined_pipelines",
   "parameters": {
      "properties": {
      "classification-properties": {
         "element": {
            "format": "element-properties",
            "name": "classification"
         }
      }
      ...
]

Start pipeline for all instances in config.yml:

./sample_start.sh

Important: Before you run sample_start.sh script, make sure that jq is installed on your system. See the troubleshooting guide for more details.

Output:

No pipeline specified. Starting the first pipeline.

------------------------------------------
Processing instance: pdd1 from SAMPLE_APP: pallet-defect-detection
------------------------------------------
Environment variables loaded from /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd1/.env
Running sample app: pallet-defect-detection
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Loading payload from /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd1/payload.json
Payload loaded successfully.
Starting first pipeline: pallet_defect_detection
Launching pipeline: pallet_defect_detection
Extracting payload for pipeline: pallet_defect_detection
Found 1 payload(s) for pipeline: pallet_defect_detection
Payload for pipeline 'pallet_defect_detection'. Response: "fd6e6f9f0b1811f191504920480d2bc6"

------------------------------------------
Processing instance: pdd2 from SAMPLE_APP: pallet-defect-detection
------------------------------------------
Environment variables loaded from /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd2/.env
Running sample app: pallet-defect-detection
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Loading payload from /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd2/payload.json
Payload loaded successfully.
Starting first pipeline: pallet_defect_detection
Launching pipeline: pallet_defect_detection
Extracting payload for pipeline: pallet_defect_detection
Found 1 payload(s) for pipeline: pallet_defect_detection
Payload for pipeline 'pallet_defect_detection'. Response: "fd798f9f0b1811f1a42f1bffe3f91352"

------------------------------------------
Processing instance: weld1 from SAMPLE_APP: weld-porosity
------------------------------------------
Environment variables loaded from /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/weld-porosity/weld1/.env
Running sample app: weld-porosity
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Loading payload from /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/weld-porosity/weld1/payload.json
Payload loaded successfully.
Starting first pipeline: weld_porosity_classification
Launching pipeline: weld_porosity_classification
Extracting payload for pipeline: weld_porosity_classification
Found 1 payload(s) for pipeline: weld_porosity_classification
Payload for pipeline 'weld_porosity_classification'. Response: "fd84ad550b1811f1b60c936d4c42ec17"

Access WebRTC stream:

The inference stream can be viewed on WebRTC, in a browser, at the following url depending on the SAMPLE_APP:

Note: The NGINX_HTTPS_PORT is different for each instance of the sample app. For example, for the sample config mentioned previously, the instance pdd1 has nginx port set to 8443, pdd2 set to 9443 & weld1 set to 10443.
```
https://<HOST_IP>:<NGINX_HTTPS_PORT>/mediamtx/pdd/              # Pallet Defect Detection
https://<HOST_IP>:<NGINX_HTTPS_PORT>/mediamtx/weld/             # Weld Porosity
```

Start pipeline for a particular instance only#

Fetch the list of pipeline for <INSTANCE_NAME>:

./sample_list.sh -i <INSTANCE_NAME>

Example Output:

Instance name set to: pdd1
Found SAMPLE_APP: pallet-defect-detection for INSTANCE_NAME: pdd1
Environment variables loaded from /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd1/.env
Running sample app: pallet-defect-detection
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Getting list of loaded pipelines...
Loaded pipelines:
[
{
   "description": "DL Streamer Pipeline Server pipeline",
   "name": "user_defined_pipelines",
   "parameters": {
      "properties": {
      "detection-properties": {
         "element": {
            "format": "element-properties",
            "name": "detection"
         }
      }
        ...
]

Start the pipeline for <INSTANCE_NAME>:

./sample_start.sh -i <INSTANCE_NAME> -p <PIPELINE_NAME>

Output:

Instance name set to: pdd1
Starting specified pipeline(s)...
Found SAMPLE_APP: pallet-defect-detection for INSTANCE_NAME: pdd1
Environment variables loaded from /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd1/.env
Running sample app: pallet-defect-detection
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Loading payload from /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd1/payload.json
Payload loaded successfully.
Starting pipeline: pallet_defect_detection
Launching pipeline: pallet_defect_detection
Extracting payload for pipeline: pallet_defect_detection
Found 1 payload(s) for pipeline: pallet_defect_detection
Payload for pipeline 'pallet_defect_detection'. Response: "44e46ef30b1911f1a2e34920480d2bc6"

Access WebRTC stream:

Open a browser and navigate to:

https://<HOST_IP>:<NGINX_HTTPS_PORT>/mediamtx/<peer-id of SAMPLE_APP>/

Start pipeline for a particular instance from a custom payload.json#

Fetch the list of pipeline for <INSTANCE_NAME>:

./sample_list.sh -i <INSTANCE_NAME>

Example Output:

Instance name set to: pdd1
Found SAMPLE_APP: pallet-defect-detection for INSTANCE_NAME: pdd1
Environment variables loaded from /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd1/.env
Running sample app: pallet-defect-detection
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Getting list of loaded pipelines...
Loaded pipelines:
[
{
   "description": "DL Streamer Pipeline Server pipeline",
   "name": "user_defined_pipelines",
   "parameters": {
      "properties": {
      "detection-properties": {
         "element": {
            "format": "element-properties",
            "name": "detection"
         }
      }
        ...
]

Start the pipeline for <INSTANCE_NAME> where pipeline is loaded from :

./sample_start.sh -i <INSTANCE_NAME> --payload <file> -p <PIPELINE_NAME>

Output:

Instance name set to: pdd1
Custom payload file set to: custom_payload.json
Starting specified pipeline(s)...
Found SAMPLE_APP: pallet-defect-detection for INSTANCE_NAME: pdd1
Environment variables loaded from /home/intel/ird_instance/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd1/.env
Running sample app: pallet-defect-detection
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Loading payload from payload.json
Payload loaded successfully.
Starting pipeline: pallet_defect_detection_gpu
Launching pipeline: pallet_defect_detection_gpu
Extracting payload for pipeline: pallet_defect_detection_gpu
Found 1 payload(s) for pipeline: pallet_defect_detection_gpu
Payload for pipeline 'pallet_defect_detection_gpu' posted successfully. Response: "4f57b996ff5311f0aa82fa869454672b"

Access WebRTC stream:

Open a browser and navigate to:

https://<HOST_IP>:<NGINX_HTTPS_PORT>/mediamtx/<peer-id of SAMPLE_APP>/

Monitor Applications#

Check Pipeline Status#

Check status of all instances:

./sample_status.sh

Output:

No arguments provided. Fetching status for all pipeline instances.
Config file found. Fetching status for all instances defined in /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/config.yml
Processing instance: pdd1 from sample app: pallet-defect-detection
Environment variables loaded from /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd1/.env
Running sample app: pallet-defect-detection
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
[
{
   "avg_fps": 30.000878701220802,
   "elapsed_time": 97.19715881347656,
   "id": "fd6e6f9f0b1811f191504920480d2bc6",
   "message": "",
   "start_time": 1771233739.0876606,
   "state": "COMPLETED"
},
{
   "avg_fps": 25.66489579665379,
   "elapsed_time": 5.9224841594696045,
   "id": "772ac89c0b1911f1ad464920480d2bc6",
   "message": "",
   "start_time": 1771233942.9697065,
   "state": "RUNNING"
}
]
Processing instance: pdd2 from sample app: pallet-defect-detection
Environment variables loaded from /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd2/.env
Running sample app: pallet-defect-detection
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
[
{
   "avg_fps": 30.000722249076027,
   "elapsed_time": 97.19766521453857,
   "id": "fd798f9f0b1811f1a42f1bffe3f91352",
   "message": "",
   "start_time": 1771233739.292792,
   "state": "COMPLETED"
},
{
   "avg_fps": 30.15376702990521,
   "elapsed_time": 6.035728454589844,
   "id": "7737e4570b1911f1baee1bffe3f91352",
   "message": "",
   "start_time": 1771233942.8786583,
   "state": "RUNNING"
}
]
Processing instance: weld1 from sample app: weld-porosity
Environment variables loaded from /home/intel/IRD/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/weld-porosity/weld1/.env
Running sample app: weld-porosity
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
[
{
   "avg_fps": 30.001865362861874,
   "elapsed_time": 22.465271472930908,
   "id": "fd84ad550b1811f1b60c936d4c42ec17",
   "message": "",
   "start_time": 1771233738.8075953,
   "state": "COMPLETED"
},
{
   "avg_fps": 30.126401681877375,
   "elapsed_time": 6.008018255233765,
   "id": "7749719d0b1911f1bef0936d4c42ec17",
   "message": "",
   "start_time": 1771233942.9226382,
   "state": "RUNNING"
}
]

Check status of only a particular instance. You may refer to the config.yml for the instance names
```
./sample_status.sh -i <INSTANCE_NAME>
```

Check status of a particular instance_id of an instance

./sample_status.sh -i <INSTANCE_NAME> --id <INSTANCE_ID>

View Container Logs#

View dlsps logs of an instance.

docker compose -p <INSTANCE_NAME> logs -f dlstreamer-pipeline-server

Stop Applications#

Stop Pipeline Instances#

Stop all pipelines:

./sample_stop.sh

Output:

No pipelines specified. Stopping all pipeline instances

-------------------------------------------
Processing instance: pdd1 (SAMPLE_APP: pallet-defect-detection)
-------------------------------------------
Environment variables loaded from /home/intel/ird_instance/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd1/.env
Running sample app: pallet-defect-detection
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Instance list fetched successfully. HTTP Status Code: 200
Found 1 running pipeline instances.
Stopping pipeline instance with ID: 5401c83aff5611f0aa82fa869454672b
Pipeline instance with ID '5401c83aff5611f0aa82fa869454672b' stopped successfully. Response: {
"avg_fps": 30.016161405973335,
"elapsed_time": 16.457796335220337,
"id": "5401c83aff5611f0aa82fa869454672b",
"message": "",
"start_time": 1769940669.0616643,
"state": "RUNNING"
}

-------------------------------------------
Processing instance: pdd2 (SAMPLE_APP: pallet-defect-detection)
-------------------------------------------
Environment variables loaded from /home/intel/ird_instance/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd2/.env
Running sample app: pallet-defect-detection
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Instance list fetched successfully. HTTP Status Code: 200
Found 1 running pipeline instances.
Stopping pipeline instance with ID: 541d02bcff5611f0aafbda07c19c7336
Pipeline instance with ID '541d02bcff5611f0aafbda07c19c7336' stopped successfully. Response: {
"avg_fps": 30.02096208342521,
"elapsed_time": 16.42185068130493,
"id": "541d02bcff5611f0aafbda07c19c7336",
"message": "",
"start_time": 1769940669.2755027,
"state": "RUNNING"
}

-------------------------------------------
Processing instance: weld1 (SAMPLE_APP: weld-porosity)
-------------------------------------------
Environment variables loaded from /home/intel/ird_instance/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/weld-porosity/weld1/.env
Running sample app: weld-porosity
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Instance list fetched successfully. HTTP Status Code: 200
Found 1 running pipeline instances.
Stopping pipeline instance with ID: 543ae1c4ff5611f0ad167a01fcf9b4b5
Pipeline instance with ID '543ae1c4ff5611f0ad167a01fcf9b4b5' stopped successfully. Response: {
"avg_fps": 30.030312784843783,
"elapsed_time": 16.416736602783203,
"id": "543ae1c4ff5611f0ad167a01fcf9b4b5",
"message": "",
"start_time": 1769940669.4774928,
"state": "RUNNING"
}

Stop pipelines of given instance:

./sample_stop.sh -i <INSTANCE_NAME>

Output:

Found SAMPLE_APP: pallet-defect-detection for INSTANCE_NAME: pdd2
Environment variables loaded from /home/intel/ird_instance/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd2/.env
Running sample app: pallet-defect-detection
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Instance list fetched successfully. HTTP Status Code: 200
Found 1 running pipeline instances.
Stopping pipeline instance with ID: af709322ff5611f0aafbda07c19c7336
Pipeline instance with ID 'af709322ff5611f0aafbda07c19c7336' stopped successfully. Response: {
"avg_fps": 30.10730708662458,
"elapsed_time": 7.938256025314331,
"id": "af709322ff5611f0aafbda07c19c7336",
"message": "",
"start_time": 1769940822.4814367,
"state": "RUNNING"
}

Stop pipelines of an instance with given instance_id:

./sample_stop.sh -i <INSTANCE_NAME> --id <INSTANCE_ID>

Output:

Found SAMPLE_APP: pallet-defect-detection for INSTANCE_NAME: pdd1
Environment variables loaded from /home/intel/ird_instance/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/temp_apps/pallet-defect-detection/pdd1/.env
Running sample app: pallet-defect-detection
Using default deployment - curl commands will use: <HOST_IP>:<NGINX_HTTPS_PORT>
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Stopping pipeline instance with ID: af5741baff5611f0aa82fa869454672b
Pipeline instance with ID 'af5741baff5611f0aa82fa869454672b' stopped successfully. Response: {
"avg_fps": 30.00011463267954,
"elapsed_time": 97.19963002204895,
"id": "af5741baff5611f0aa82fa869454672b",
"message": "",
"start_time": 1769940822.2892969,
"state": "COMPLETED"
}

Stop Docker Containers#

Stop containers of all instances:
```
./run.sh down
```
Verify all containers are stopped:
```
docker ps
```

Run Multiple Apps#

Prerequisites#

Overview#

Set up the Applications#

Deploy the Applications#

Deploy all the application instances#

Start AI Pipelines#

Start pipeline for all instances#

Start pipeline for a particular instance only#

Start pipeline for a particular instance from a custom payload.json#

Monitor Applications#

Check Pipeline Status#

View Container Logs#

Stop Applications#

Stop Pipeline Instances#

Stop Docker Containers#

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