# Run User Defined Function (UDF) Pipelines

## Prerequisite

Ensure to build/pull the DL Streamer Pipeline Server extended image.

[Build instructions](../get-started/build-from-source.md)

Pull DL Streamer Pipeline Server extended image from [dockerhub](https://hub.docker.com/r/intel/dlstreamer-pipeline-server)
- Ensure to update the `DLSTREAMER_PIPELINE_SERVER_IMAGE` value in `[WORKDIR]/edge-ai-libraries/microservices/dlstreamer-pipeline-server/docker/.env` file accordingly, in order to run the pulled image.

## Steps
DL Streamer Pipeline Server supports `udfloader` element which allows the user to write an
User Defined Function (UDF) that can transform video frames and/or manipulate metadata. You
can do this by adding an element called `udfloader`. You can try a simple udfloader pipeline
by replacing the following sections in [WORKDIR]/edge-ai-libraries/microservices/dlstreamer-pipeline-server/configs/default/config.json with the following

- replace `"pipeline"` section with

    ```sh
    "pipeline": "{auto_source} ! decodebin ! videoconvert ! video/x-raw,format=RGB ! udfloader name=udfloader ! videoconvert ! video/x-raw,format=NV12 ! gvametapublish file-path=/tmp/results.jsonl ! appsink name=destination",
    ```

- replace `"properties"` section with

    ```sh
    "properties": {
        "udfloader": {
            "element": {
                "name": "udfloader",
                "property": "config",
                "format": "json"
            },
            "type": "object"
        }
    }
    ```

- add `"udfs"` section in config (after `"parameters"`)

    ```sh
    "udfs": {
        "udfloader": [
            {
                "name": "python.geti_udf.geti_udf",
                "type": "python",
                "device": "CPU",
                "visualize": "true",
                "deployment": "./resources/models/geti/pallet_defect_detection/deployment",
                "metadata_converter": "null"
            }
        ]
    }
    ```

Save the `config.json` and restart DL Streamer Pipeline Server.
Ensure that the changes made to the `config.json` are reflected in the container by volume mounting (as mentioned in this [document](./change-dlstreamer-pipeline.md)) it.

```sh
    cd [WORKDIR]/edge-ai-libraries/microservices/dlstreamer-pipeline-server/docker/
    docker compose down
    docker compose up
```

Now to start this pipeline, run below Curl request

```sh
curl http://localhost:8080/pipelines/user_defined_pipelines/pallet_defect_detection -X POST -H 'Content-Type: application/json' -d '{
    "source": {
        "uri": "file:///home/pipeline-server/resources/videos/warehouse.avi",
        "type": "uri"
    },
    "destination": {
        "metadata": {
            "type": "file",
            "path": "/tmp/results.jsonl",
            "format": "json-lines"
        },
        "frame": {
            "type": "rtsp",
            "path": "pallet_defect_detection",
            "overlay": false
        }
    },
    "parameters": {
        "udfloader": {
            "udfs": [
                {
                    "name": "python.geti_udf.geti_udf",
                    "type": "python",
                    "device": "CPU",
                    "visualize": "true",
                    "deployment": "./resources/models/geti/pallet_defect_detection/deployment",
                    "metadata_converter": "null"
                }
            ]
        }
    }
}'
```

> **Note:** The `"udfloader"` config needs to be present in either `config.json` or in the curl
> command. It is not needed in both places. However, if specified in both places, then the config
> in curl command will override the config present in `config.json`.

We should see the metadata results in `/tmp/results.jsonl`.

For more details on UDF, you can refer to the [UDF writing guide](../advanced-guide/detailed_usage/udf/UDF_writing_guide.md).