How to use an AI Model and Video File of your own#

You can bring your own model and run this sample application the same way as how we bring in the weld porosity classification model. You can also bring your own video file source. Please see below for details:

Important If you have previously run the setup for the sample app using setup.sh, default sample model and video are downloaded under resource/<app_name> in your repo directory. You can manually add the model and video of your choice and keep it in this structure. For compose based deployment, the entire resources directory is volume mounted and made available to pipeline server. However for helm, you need to manually copy those to the container.

For docker compose based deployment#

The weld porosity classification model is placed as below in the repository under resources/weld-porosity/models. You can also find the input video file source for inference under videos in the same directory level.

resources/
- weld-porosity/
  - models/
    - weld-porosity/
      - deployment/
        
        Classification/
        
        model/
        
        model.bin
        
        model.xml
  - videos/
    - welding.avi
Note You can organize the directory structure for models for different use cases.

The resources folder containing both the model and video file is volume mounted into DL Streamer Pipeline Server in docker-compose.yml (present in the repository) file as follows.
```
volumes:
- ./resources/${SAMPLE_APP}/:/home/pipeline-server/resources/
```
The value of ${SAMPLE_APP} is fetched from the .env file specifying the particular sample app you are running.
Since this is a classficiation model, ensure to use gvaclassify in the pipeline. For example: See the weld_porosity_classification pipeline in pipeline-server-config.json (present in the repository) where gvadetect is used.
The pipeline-server-config.json is volume mounted into DL Streamer Pipeline Server in docker-compose.yml as follows:
```
volumes:
- ./apps/${SAMPLE_APP}/configs/pipeline-server-config.json:/home/pipeline-server/config.json
```

Provide the model path and video file path in the REST/curl command for starting an inferencing workload. Example:

    curl http://<HOST_IP>:8080/pipelines/user_defined_pipelines/weld_porosity_classification -X POST -H 'Content-Type: application/json' -d '{
        "source": {
            "uri": "file:///home/pipeline-server/resources/videos/welding.avi",
            "type": "uri"
        },
        "destination": {
            "frame": {
                "type": "webrtc",
                "peer-id": "samplestream"
            }
        },
        "parameters": {
            "classification-properties": {
                "model": "/home/pipeline-server/resources/models/weld-porosity/deployment/Classification/model/model.xml",
                "device": "CPU"
            }
        }
    }'

For helm chart based deployment#

You can bring your own model and run this sample application the same way as how we bring in the weld porosity classification model as follows:

The weld porosity classification model is placed as below in the repository under resources/weld-porosity/models. You can also find the input video file source for inference under videos in the same directory level.

resources/
- weld-porosity/
  - models/
    - weld-porosity/
      - deployment/
        
        Classification/
        
        model/
        
        model.bin
        
        model.xml
  - videos/
    - welding.avi
Note You can organize the directory structure for models for different use cases.

Copy the resources such as video and model from local directory to the to the dlstreamer-pipeline-server pod to make them available for application while launching pipelines.

NOTE It is assumed that the sample app is already deployed in the cluster

# Below is an example for Weld Porosity classification. Please adjust the source path of models and videos appropriately for other sample applications.
POD_NAME=$(kubectl get pods -n apps -o jsonpath='{.items[*].metadata.name}' | tr ' ' '\n' | grep deployment-dlstreamer-pipeline-server | head -n 1)

kubectl cp resources/weld-porosity/videos/warehouse.avi $POD_NAME:/home/pipeline-server/resources/videos/ -c dlstreamer-pipeline-server -n apps

kubectl cp resources/weld-porosity/models/* $POD_NAME:/home/pipeline-server/resources/models/ -c dlstreamer-pipeline-server -n apps

Please update imagePullPolicy as imagePullPolicy: IfNotPresent in values.yaml in order to use the above built image.

Since this is a classification model, ensure to use gvaclassify in the pipeline. For example: See the weld_porosity_classification pipeline in pipeline-server-config.json (present in the repository) where gvaclassify is used.

The pipeline-server-config.json is volume mounted into DL Streamer Pipeline Server in provision-configmap.yaml as follows:

apiVersion: v1
kind: ConfigMap
metadata:
  namespace: {{ .Values.namespace }}
  name: dlstreamer-pipeline-server-config-input
data:
  config.json: |-
{{ .Files.Get "config.json" | indent 4 }}

Provide the model path and video file path in the REST/curl command for starting an inferencing workload. Example:

    curl http://<HOST_IP>:30107/pipelines/user_defined_pipelines/weld_porosity_classification -X POST -H 'Content-Type: application/json' -d '{
        "source": {
            "uri": "file:///home/pipeline-server/resources/videos/welding.avi",
            "type": "uri"
        },
        "destination": {
            "frame": {
                "type": "webrtc",
                "peer-id": "samplestream"
            }
        },
        "parameters": {
            "classification-properties": {
                    "model": "/home/pipeline-server/resources/models/weld-porosity/deployment/Classification/model/model.xml",
                    "device": "CPU"
            }
        }
    }'

How to use an AI Model and Video File of your own#

For docker compose based deployment#

For helm chart based deployment#

This Page