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0.8.1-2108.1

Updated for ODK to comply to new resource request requirements for operators
Pipeline uses ARGO orchestration

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Clara Deploy SDK is being consolidated into Clara Holoscan SDK

More info https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/collections/claradeploy

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\nNote: 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 app_operator.zip -d app_operator\n

The operator development package should contain the following.

└─ app_operator\n ├─ Dockerfile\n ├─ main.py\n ├─ build.sh\n ├─ run-local.sh\n ├─ run-triton.sh\n ├─ requirements.txt\n ├─ def\n | └─ one-operator-pipeline.yml\n └─ local\n ├─ input\n | ├─ volume-covid19-A-0000.nii.gz\n | ├─ volume-covid19-A-0001.nii.gz\n | └─ volume-covid19-A-0010.nii.gz\n ├─ output\n └─ models\n └─ covid\n ├─ 1\n │ └─ model.pt\n └─ config.pbtxt\n

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).
main.py 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.
build.sh 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 run-local.sh to run successfully the user must first build the operator's container image using build.sh and run run-triton.sh to make the model available to the operator for inference.
run-triton.sh is a Bash script that starts a local containerized Triton Inference Server.
requirements.txt lists all the libraries needed for main.py to run.
pipelines contains the operator and pipeline (one-operator-pipeline.yaml) definitions necessary to deploy the pipeline and operator in Clara Deploy.\nNote 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 main.py locally in the state it is distributed.
run-triton.sh 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.

Recommended Usage of the ODK

\n
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.
\n
\n
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.
\n

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 build.sh 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.

License

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

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The ODK includes a working version [liver inference pipeline](https://ngc.nvidia.com/catalog/resources/nvidia:clara:clara_ai_livertumor_pipeline) implemented in a simplified manner as a custom operator.\n\nFor more details follow the detailed steps in the \"Quickstart\" tab.\n\n## License\n\nAn [End User License Agreement](https://developer.nvidia.com/nvidia-clara-sdk-license)\nis included with the product. By pulling and using the Clara Deploy asset on NGC, you accept the\nterms and conditions of these licenses.","UPLOAD_COMPLETE","releaseType","createdDate",["D",1629919596833],"updatedDate","totalSizeInBytes",357994187,"totalFileCount",1,"isSigned","digest","malwareScanStatus","isScanned","hasReleaseNotes","setup","","hasSetup","quickStartGuide","# Quick Start\n\n## Package Contents\n\nUnzip the package locally using\n```\nunzip app_operator.zip -d app_operator\n```\n\nThe operator development package should contain the following.\n```\n└─ app_operator\n ├─ Dockerfile\n ├─ main.py\n ├─ build.sh\n ├─ run-local.sh\n ├─ run-triton.sh\n ├─ requirements.txt\n ├─ env.list\n ├─ def\n | └─ one-operator-pipeline.yml\n ├─ lib\n | └─ clara-0.1-py3-none-any.whl\n └─ local\n ├─ input\n | └─ liver_14.nii.gz\n ├─ output\n └─ models\n └─ segmentation_liver_v1\n ├─ 1\n │ └─ model.graphdef\n └─ config.pbtxt\n```\n\n- `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 operator (please see [Packaging Existing Container](#packageanexistingcontainer) below).\n- `main.py` 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 liver segmentation model deployed in Triton, outputting the result of each Triton inference in the output folder.\n- `build.sh` is a Bash script that initiates the docker build.\n- `run-local` is a Bash script that initiates a local (stand-alone) containerized operator. For `run-local.sh` to run successfully the user must first build the operator container image using `build.sh` and run `run-triton.sh` to make the model available to the operator for inference.\n- `run-triton.sh` is a Bash script that initiates a local containerized Triton server.\n- `requirements.txt` lists all the libraries needed for `main.py` to run.\n- `env.list` lists all the environment variable names required to run the Clara operator locally (baremetal or containerized) for development and debugging purposes.\n- `def` contains the operator and pipeline (`one-operator-pipeline.yaml`) definitions necessary to deploy the operator in Clara. Note that Clara will accept only the deployment of pipelines, not operators alone, therefore ensure that the operator is available to a container repository reachable by Clara at runtime.\n- `local` is a directory containing the model artifacts necessary to run `main.py` locally in the state it is distributed.\n - `run-triton.sh` will load the `local/models` folder into the Triton container to make it available for inference.\n - `run-local` will load the `local/input` folder into the operator container to allow the operator container to read `liver_14.nii.gz` and send it for inference.\n- `lib` contains the Clara operator Python client library. This library must __always__ be installed for a Clara operator to function correctly.\n\n## Run the Example Liver Segmentation Model Locally\n\nBefore running the inference model locally make sure to install Docker CE, and NVIDIA Docker 2 to be able to serve the model on a GPU. You may follow the steps [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker).\n\nTo run the packaged liver inference model:\n1. Build the Clara example inference operator using\n```\n./build.sh\n```\n2. Start the Triton inference server using\n```\n./run-triton.sh\n```\n3. Run the Clara operator locally using\n```\n./run-local.sh\n```\n4. Once the inference completes check the output of the inference in the `local/output` directory.\n\n## Deploy Single-Operator Pipeline to Existing Clara Cluster\n\nThis section assumes that you have installed a Clara cluster which has access to the container image built using `build.sh` (by default the image is tagged `clara/simple-operator:0.1.0`). To install or upgrade Clara Deploy SDK, follow the steps outlined on the [Installation](https://docs.nvidia.com/clara/deploy/ClaraInstallation.html) page of the Clara Deploy User Guide.\n\n### Deploy the Model\n\nBefore deploying the operator, one must ensure that the inference model is deployed in Clara (if an inference model is used). In the case of the example liver segmentation model, the user must copy the contents under `local/models` to the Clara model repository directory, by default `/clara/common/models`, resulting in a directory structure such as\n```\nclara\n └─ common\n └─ models\n └─ segmentation_liver_v1\n ├─ 1\n │ └─ model.graphdef\n └─ config.pbtxt\n```\n\n### Deploy the Operator\n\nTo deploy the singleton-operator example pipeline provided\n```\ncd def\nclara create pipelines -p one-operator-pipeline.yml\n```\n`clara create` will output a pipeline id, `PIPELINE_ID`, if deployed successfully.\n\nTo run the pipeline with the given data we must first create a job\n```\ncd ..\nclara create job -p PIPELINE_ID -n liver-job -f local/input\n```\nthe command will output a job id `JOB_ID`. \n\nTo run the created job use\n```\nclara start job -j JOB_ID\n```\n\n## Deploy an Existing Inference Container as a Clara Operator\n\nTo package a custom inference container as a Clara operator you may update the `Dockerfile` to do so. For instance, the `Dockerfile` to package an existing container may look something like\n```\nFROM my_inference_container:0.1.0\n\n...\n\n# install Clara-compatible Triton client\nARG TRITON_CLIENTS_URL=https://github.com/NVIDIA/tensorrt-inference-server/releases/download/v1.5.0/v1.5.0_ubuntu1804.clients.tar.gz\nRUN mkdir -p /opt/nvidia/trtis-clients \\\n && curl -L ${TRITON_CLIENTS_URL} | tar xvz -C /opt/nvidia/triton-clients\nRUN pip install --no-cache-dir future==0.18.2 grpcio==1.27.2 protobuf==3.11.3 /opt/nvidia/triton-clients/python/*.whl\n\n# install operator requirements\nRUN pip install --upgrade setuptools &&\\\n pip install -r requirements.txt\n\nENTRYPOINT [\"bash\", \"-c\", \"python -u -m nvidia_clara_pipeline_driver.app my_script.py\"]\n```\n\nNote that:\n1. You must include the Triton client installation steps in the script if you want to use Triton for inference. This step can be omitted if the inference model is packaged in your container.\n2. You must always install the `whl` file in the `lib/` directory as it contains the wrapper code necesary for the Clara operator to function correctly.\n3. The `ENTRYPOINT` to your container __must use__ the `nvidia_clara_pipeline_driver.app` Python module for the operator to function correctly. Specifically, if your main script is `my_script.py` then your container entry-point should be\n```\npython -u -m nvidia_clara_pipeline_driver.app my_script.py\n```","hasQuickStartGuide","advanced","# Advanced\n## Customizing the Operator Development Kit Example Code\n\nThe Operator Development Kit (ODK) can be used as a starting point for the development of custom Clara pipeline operators or the migration of existing container images into Clara pipeline operators. This section explains the focal components of the ODK when building a custom Clara pipeline to perform inference.\n\n### Clara Operator-specific Variables in Python\n\n`main.py` is the functional part of the code the developer may customize for their own purposes. As provided, `main.py` is an example of decorating the code with the necessary components. Clara Deploy will expect certain components in the pipeline operator definition. \n\n- Enviornment variables\n- Inputs \n- Pre-transformations\n- Infererence \n- Post-transformations\n- Outputs \n\n---\nThe following describes the components used in the ODK sample application. \n1. The input component reads data from the input payload. The input directory path is defined by the `OPERATOR_INPUT_PATH` environment variable. When the environment variable is unavailable, the default path of /input is used.\n```\n input_path = os.getenv('OPERATOR_INPUT_PATH', '/input')\n```\n2. The pre-transformation component applies pre-inference transformations to the input data. This component is optional and model dependent.\n```\n pre_transforms = Compose([\n LoadImage(reader=\"NibabelReader\", image_only=True, dtype=np.float32),\n AddChannel(),\n ScaleIntensityRange(a_min=-1000, a_max=500, b_min=0.0, b_max=1.0, clip=True),\n CropForeground(margin=5),\n Resize([192, 192, 64], mode=\"area\"),\n ])\n```\n3. The inference uses the Triton Python client to perform inference via the model deployed to Triton Inference Server (see Section \"Deploy the Model\" below),\n```\n inference_ctx = initialize_inference_server()\n inference_response = inference_ctx.run({ model_input_label : (inference_image,) },\n { model_output_label: InferContext.ResultFormat.RAW },\n batch_size = 1)\n```\n4. Post-transformation component applies post-inference transformations to the inference result,\n```\n post_transforms = Compose([\n ToTensor(),\n Activations(sigmoid=True),\n AsDiscrete(threshold_values=True, logit_thresh=0.5),\n ToNumpy(),\n ])\n```\n5. Output component finally writes the output data to the path defined by `OPERATOR_OUTPUT_PATH`. When the environment variable is unavailable, the default value of /output is used.\n```\n output_path = os.getenv('OPERATOR_OUTPUT_PATH', '/output')\n```\nThe full code set can be found in [main.py](../main.py)\n---\n## Deploying a Single-Operator Pipeline to Existing Clara Deploy Cluster\n\nThis section assumes that you have installed a Clara Deploy cluster which has access to the container image built using `build.sh` (by default the image is tagged `clara/simple-operator:0.1.0`). To install or upgrade Clara Deploy SDK, follow the steps outlined on the [Installation](https://docs.nvidia.com/clara/deploy/ClaraInstallation.html) page of the Clara Deploy User Guide.\n\n### Deploy the Model\n\nBefore deploying the operator, one must ensure that the inference model is deployed in Clara (if an inference model is used). In the case of the example COVID-19 classification model, the user must copy the contents under `local/models` to the Clara model repository directory, by default `/clara/common/models`, resulting in a directory structure such as\n```\nclara\n └─ common\n └─ models\n └─ covid\n ├─ 1\n │ └─ model.pt\n └─ config.pbtxt\n```\nThe ODK contains a PyTorch model, however, the user may deploy ONNX, Tensorflow Graphdef, Tensoflow SavedMode, TensorRT, and other formats supported by the [Triton Inference Server](https://github.com/triton-inference-server/server/blob/r20.11/docs/model_repository.md), with an accompanying `config.pbtxt` for the model.\n\n### Deploy the Operator\n\nTo deploy the singleton-operator example pipeline provided start in the top level ODK folder.\n```\ncd pipelines\nclara create pipelines -p one-operator-pipeline.yml\n```\n`clara create` will output a pipeline id, ``, if deployed successfully.\n\nBefore trying to run a job from the newly deployed pipeline, the user must ensure that the container image built using `build.sh` is reachable from the Kubernetes cluster on which Clara Deploy is running. For instance, if the container image is `my_inference_container:0.1` either ensure\n- that this image is present in the container repository local to Clara (i.e. in the same machine), or\n- available in a public container repository reachable by the Kubernetes cluster where Clara Deploy is deployed (e.g. DockerHub).\n\nTo run the pipeline with the given data we must first create a job\n```\ncd ..\nclara create job -p -n covid-class -f local/input\n```\nThe output will be a job id ``. \n\nIt is important to provide the input option with ` -f ` here as this takes the local input data and moves (uploads) it to the expected Clara operator location for input data.\n```\nJOB_ID: \nPAYLOAD_ID: \nPayload uploaded successfully.\n```\n\nTo run the created job use\n```\nclara start job -j \n```\nTo check for a successful job use\n```\nclara list jobs\n```\nLook for the `` and corresponding `` for the job. \n\nInspect the outputs using the `` and corresponding ``in the corresponding `` folder which by default Clara always stores in the defined payloads folder.\n```\ncd /clara/payloads/\n```\nThe output of the classification results are stored as text correxponding with each of the inputs as shown below.\n```\n├── input\n│ ├── volume-covid19-A-0000.nii.gz\n│ ├── volume-covid19-A-0001.nii.gz\n│ └── volume-covid19-A-0010.nii.gz\n├── NVIDIA\n│ └── Clara\n└── operators\n └── simple-operator\n └── classification-output\n ├── volume-covid19-A-0000.nii_result.txt\n ├── volume-covid19-A-0001.nii_result.txt\n └── volume-covid19-A-0010.nii_result.txt\n```\n\n## Package Existing Container\n\nTo package a custom inference container as a Clara operator pipeline you may update the `Dockerfile` and `build.sh` script. Packaging your own code to run in a Clara operator requires you to define and install environment requirements, an `ENTRYPOINT`, and any optional dependencies for your inference application. \n\nFor example, the `Dockerfile` to package an existing container may look something like\n```\nFROM my_inference_container:0.1.0\n\n...\n\n# install Clara-compatible Triton client\nARG TRITON_CLIENTS_URL=https://github.com/triton-inference-server/server/releases/download/v2.11.0/v2.11.0_ubuntu2004.clients.tar.gz\nRUN mkdir -p /opt/nvidia/triton-clients \\\n && curl -L ${TRITON_CLIENTS_URL} | tar xvz -C /opt/nvidia/triton-clients\nRUN pip install --no-cache-dir future==0.18.2 grpcio==1.27.2 protobuf==3.11.3 /opt/nvidia/triton-clients/python/*.whl\n\n# install operator requirements\nRUN pip install --upgrade setuptools &&\\\n pip install -r requirements.txt\n\nENTRYPOINT [\"bash\", \"-c\", \"python -u my_script.py\"]\n```\n\nNote that, you must include the Triton client installation steps in the script if you want to use Triton for inference. This step can be omitted if the inference model is packaged in your container.\n\nSimilary, use the `build.sh` script to package the container. Simply pass the fully qualified name and tag of the desired container to the script.\n``` ./build.sh custom_inference_container:1.0.0 \n ```\n note: By defulat the script will create an operator named `clara/simple-operator:1.0.0`if you do not specify the name in the command line.\n\n Once the container is successfuly created, then go back to [Deploy The Operator](#Deploy-the-operator) and follow the same steps using your custom container.","hasAdvanced","performance","hasPerformance","policies",[],{"_180":118,"_182":185,"_151":-5,"_159":26,"_162":26,"_175":184,"_163":65},{"_180":14,"_182":14,"_151":-5,"_159":26,"_162":26,"_175":184,"_163":65},{"_180":181,"_182":181,"_151":-5,"_159":26,"_162":26,"_175":183,"_163":65},"id","0.7.4-2103.2","name",[],[],"Latest (0.8.1-2108.1)","nvidia/clara/clara_operator_development_kit","key","nvidia/clara/resources/clara_operator_development_kit","resourceId","clara_operator_development_kit","shortDescription","A starter kit to develop Clara operators from scratch or package existing inference containers as Clara operators","# Clara Deploy SDK is being consolidated into Clara Holoscan SDK\n\nMore info https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/collections/claradeploy\n\n# Clara Deploy Operator Development Kit\n\nThe 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](https://ngc.nvidia.com/catalog/collections/nvidia:claracovid19) implemented from scratch using [MONAI](https://monai.io/).\n\nFor more details follow the detailed steps in the [Quickstart](./quickstart.md) tab.\n## Overview of the Operator Development Kit\n\nThe Operator Development Kit (ODK) contains the minimal set of assets for a user to:\n- be able to develop a Clara operator from scratch, or\n- to migrate an existing container image into a Clara operator.\n\n\n## Package Contents\n\nDownload the ODK from [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:clara:clara_operator_development_kit) and unzip the package locally using\nNote: You can download the file with Guest account or using your NGC account.\n\nOnce you have downloaded the ODK asset, then unzip the file into a directory.\n```\nunzip app_operator.zip -d app_operator\n```\n\nThe operator development package should contain the following.\n```\n└─ app_operator\n ├─ Dockerfile\n ├─ main.py\n ├─ build.sh\n ├─ run-local.sh\n ├─ run-triton.sh\n ├─ requirements.txt\n ├─ def\n | └─ one-operator-pipeline.yml\n └─ local\n ├─ input\n | ├─ volume-covid19-A-0000.nii.gz\n | ├─ volume-covid19-A-0001.nii.gz\n | └─ volume-covid19-A-0010.nii.gz\n ├─ output\n └─ models\n └─ covid\n ├─ 1\n │ └─ model.pt\n └─ config.pbtxt\n```\n\n- `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](#package-existing-container) below).\n- `main.py` 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.\n- `build.sh` is a Bash script that initiates packaging the application into a container.\n- `run-local` is a Bash script that initiates a local (stand-alone) containerized operator. For `run-local.sh` to run successfully the user must first build the operator's container image using `build.sh` and run `run-triton.sh` to make the model available to the operator for inference.\n- `run-triton.sh` is a Bash script that starts a local containerized Triton Inference Server.\n- `requirements.txt` lists all the libraries needed for `main.py` to run.\n- `pipelines` contains the operator and pipeline (`one-operator-pipeline.yaml`) definitions necessary to deploy the pipeline and operator in Clara Deploy. \n 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.\n- `local` is a directory containing the model artifacts necessary to run `main.py` locally in the state it is distributed.\n- `run-triton.sh` will mount the `local/models` folder to the Triton Inference Server container, making the models available for inference.\n- `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.\n\n## Recommended Usage of the ODK\n- Start with the [Quickstart](./quickstart.md) instructions after completing [setup](./setup.md) 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. \n\n- Next, using the example after completing the [setup](./setup.md) instructions for Clara Deploy, follow the instructions on the [Advanced](./advanced.md) tab and incorporate the operator into a Clara pipeline creating a Clara Deploy operator pipeline and deploy.\n\n----\n- 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 \n - `Dockerfile` and `build.sh` script with modifications to package your application in a container\n - `one-operator-pipeline.yml` file with modifications as a templete to include your container in pipeline.\n- See the [Advanced](./advanced.md) tab for more details on how to make modifications to provided components for implementing a custom application into a Clara Deploy operator pipeline.\n\n## Suggested Reading\n\nRelease Notes, the Getting Started Guide, and the SDK itself are available at the NVIDIA Developer forum: (https://developer.nvidia.com/clara).\n\nFor answers to any questions you may have about this release, visit the NVIDIA Devtalk forum:\n(https://devtalk.nvidia.com/default/board/362/clara-sdk/).\n\n## License\n\nAn [End User License Agreement](https://developer.nvidia.com/nvidia-clara-sdk-license)\nis included with the product. 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