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Clara Deploy Operator Development Kit [Deprecated]

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A starter kit to develop Clara operators from scratch or package existing inference containers as Clara operators



Latest Version



April 4, 2023

Compressed Size

341.41 MB

Clara Deploy SDK is being consolidated into Clara Holoscan SDK

More info

Clara Deploy Operator Development Kit

The Operator Development Kit (ODK) is a small software package containing the ingredients necessary to develop Clara operators from scratch, test them locally (using Triton Inference server when the operator performs inference), and deploying the developed operator in a Clara deployment. The ODK includes a working version of a COVID classification model implemented from scratch using MONAI.

For more details follow the detailed steps in the Quickstart tab.

Overview of the Operator Development Kit

The Operator Development Kit (ODK) contains the minimal set of assets for a user to:

  • be able to develop a Clara operator from scratch, or
  • to migrate an existing container image into a Clara operator.

Package Contents

Download the ODK from NGC and unzip the package locally using Note: You can download the file with Guest account or using your NGC account.

Once you have downloaded the ODK asset, then unzip the file into a directory.

unzip -d app_operator

The operator development package should contain the following.

└─ app_operator
 ├─ Dockerfile
 ├─ requirements.txt
 ├─ def
 | └─ one-operator-pipeline.yml
 └─ local
 ├─ input
 | ├─ volume-covid19-A-0000.nii.gz
 | ├─ volume-covid19-A-0001.nii.gz
 | └─ volume-covid19-A-0010.nii.gz
 ├─ output
 └─ models
 └─ covid
 ├─ 1
 │ └─
 └─ config.pbtxt
  • Dockerfile builds the operator container. This build script can be used to build the operator container, or to package an existing custom inference container image as a Clara pipeline operator (please see Packaging existing container to run in Clara below).
  • is the Python script that is executed when the operator is instantiated. The script reads NIFTI files from an input folder, applies simple transformations to each file to prepare them for inference, and uses the Triton Client library to send the inference to the COVID-19 classification model deployed to Triton, outputting the result of each inference request in the output folder.
  • is a Bash script that initiates packaging the application into a container.
  • run-local is a Bash script that initiates a local (stand-alone) containerized operator. For to run successfully the user must first build the operator's container image using and run to make the model available to the operator for inference.
  • is a Bash script that starts a local containerized Triton Inference Server.
  • requirements.txt lists all the libraries needed for to run.
  • pipelines contains the operator and pipeline (one-operator-pipeline.yaml) definitions necessary to deploy the pipeline and operator in Clara Deploy. Note that Clara Deploy only requires the deployment of pipelines, and will pull any necessary container images at runtime. Therefore, ensure any required containers have been pulled locally, or are available for Clara Deploy to pull at runtime. When pulling a container requires authentication (aka logon), it is recommended that these containers are pull prior to attempting to create a Clara pipeline-job which requires them.
  • local is a directory containing the model artifacts necessary to run locally in the state it is distributed.
  • will mount the local/models folder to the Triton Inference Server container, making the models available for inference.
  • run-local will mount the local/input folder into the operator container to allow the operator container to read *.nii.gz and send it for inference.
  • Start with the Quickstart instructions after completing setup and run the sample operator and Triton inference server on your machine. This will give you and idea of running an application and verifying that it works as expected. You don't need Clara for this, but you will need a machine with access to a GPU.

  • Next, using the example after completing the setup instructions for Clara Deploy, follow the instructions on the Advanced tab and incorporate the operator into a Clara pipeline creating a Clara Deploy operator pipeline and deploy.

  • Once you are comfortable with the sample model (COVID-19 example) try building your own Clara operator first validating the application with with the standalone process then reuse the
  • Dockerfile and script with modifications to package your application in a container
  • one-operator-pipeline.yml file with modifications as a templete to include your container in pipeline.
  • See the Advanced tab for more details on how to make modifications to provided components for implementing a custom application into a Clara Deploy operator pipeline.

Suggested Reading

Release Notes, the Getting Started Guide, and the SDK itself are available at the NVIDIA Developer forum: (

For answers to any questions you may have about this release, visit the NVIDIA Devtalk forum: (


An End User License Agreement is included with the product. By pulling and using the Clara Deploy asset on NGC, you accept the terms and conditions of these licenses.