How to Run a Reference Pipeline

Clara Deploy SDK comes bundled with a set of reference pipelines. This section provides general guidelines for executing them on your local development machine. For information specific to a reference pipeline, please view the documentation for that pipeline.

Download the Reference Pipeline

Refer to Setup section for downloading the pipeline.

Run Reference Pipelines by Sending DICOM Instances Over the Network

Many reference pipelines are triggered by the Clara DICOM Adaptor after it has received DICOM instances from an external sender to one of its mapped AE Titles.

This section explains the process with the example Advanced DICOM Integration Pipeline.

External DICOM Sender and DICOM Receiver

You need an external DICOM Service Class User (SCU) application to send images to the Clara DICOM Adapter (acting as a DICOM SCP). Similarly, when your pipeline finishes executing, you may want to send the output to an external DICOM receiver.

For this example, we will use the open-source DICOM toolkit DCMTK to provide the external DICOM sender and DICOM receiver functionality.

Install DCMTK

Install DCMTK utilities with the following command:

sudo apt-get install dcmtk

Setup an External DICOM Receiver

You can use the storescp utility as an external DICOM receiver. Open a terminal and run the following commands to set up a DICOM Receiver. This will create a directory on your machine where DICOM images produced by Clara Deploy SDK will be stored. We will refer to that directory as the "dicom-destination-directory".

mkdir dicom-destination-directory
cd dicom-destination-directory
sudo storescp -v --fork -aet MYPACS 1004

MYPACS: The Application Entity (AE) title of the external DICOM Receiver
1004: The port number where storescp will listen for any incoming DICOM traffic. Note that the IP address will be the same as that of the local system.

Setup an External DICOM Sender

You can use the storescu utility from DCMTK as a DICOM sender.

Customize the Pipeline

Before publishing a pipeline, you can modify the default settings in the pipeline definition YAML to configure the resource request and orchestration mode. We will use the advanced-dicom-integration-pipeline.yaml to show the steps.

Customize GPU request

If multiple GPUs are available, the number of GPUs requested for an operator and/or pipeline depedent service (e.g. Triton Inference Server) can be customized.

The GPU request for Triton Inference Server is set to 1 by default, as shown below:

- name: trtis
  # Triton Inference Server, required by this AI application.
    container:
      image: nvcr.io/nvidia/tritonserver
      tag: 20.07-v1-py3
      command: ["tritonserver", "--model-repository=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models"]
    requests:
      gpu: 1

With the default setting, the Triton Inference Server can only access one GPU. When more GPUs are available and intended to increase inference performance, the GPU request number may be customized to match the desired number (e.g. 4), as shown below:

- name: trtis
  # Triton Inference Server, required by this AI application.
    container:
      image: nvcr.io/nvidia/tritonserver
      tag: 20.07-v1-py3
      command: ["tritonserver", "--model-repository=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models"]
    requests:
      gpu: 4

Note: If another pipeline with a lesser number of GPUs for the Triton Inference Server has been published and triggered, the changes made here will not affect that instance of Triton Inference Server.

Customize the Orchestration Mode

In this release of the Clara Deploy Platform, Clara Pipeline Driver orchestration is supported, and other orchestration mode, namely Argo mode, has been deprecated.

Create the Pipeline

Please refer to the Setup section to create this Pipeline.

Tip: Note the pipeline ID as it will be used in a later section to define a new pipeline mapping in the DICOM adapter.

Configure the Clara DICOM Adapter

When you send images to the Clara Deploy SDK, the DICOM Adapter component acts as the internal DICOM Service Class Provider or a receiving agent.

Please refer to Clara DICOM Adapter for setup instructions.

Trigger the Pipeline

A pipeline is triggered when a DICOM series is sent to the AE title of the Clara DICOM Adapter associated with that pipeline. To trigger this pipeline, you will need a set of relevant DICOM images that can be used as source input data.

For this pipeline, a sample test dataset in DICOM format is available within the package in <unzipped input>/dcm.

Open a terminal and go to the directory that contains all your DICOM images.
Trigger the pipeline by pushing DICOM data to the proper AE title:

storescu -v +sd +r -xb -aet "DCM4CHEE" -aec "BrainSeg" AA.BBB.CC.DD 104 ./

Replace "AA.BBB.CC.DD" with the IP of your local machine.

When the above step is successfully completed, you should see the following result in the terminal:

I: Received Store Response (Success) <br>
I: Releasing Association

Check Job Status and Download/View Payloads in Clara Console/Dashboard

With Clara Orchestrator (API version >= 0.4.0)

Go to the Clara Management Console using a web browser: The URL is: <IP of the machine>:32002
Go to the JOBS view. You should see a job with a name that includes the name of the job you specified
You can inspect the status of each operator inside that job

Please check Clara Management Console to see the web application in detail.

Verify That the External DICOM Receiver Received Your Images

If your pipeline outputs DICOM images and sends them to an external DICOM receiver, you may want to verify that the images were indeed received. If you used the storescp utility from DCMTK as mentioned earlier, use a terminal to navigate to the "dicom-destination-directory" and verify whether the expected images are stored there. Also, you may want to use a third-party DICOM viewer to view those images.