11.3. Multi AI Pipeline

11.3.1. Overview

Multi AI pipeline takes a single volume and splits them into multiple ROIs, these ROIs are then fed into respective AI operators. Results from AI operators are merged into a single volume.

Pipeline uses roi-generator operator for splitting data into rois and merging data back into a single volume. Liver tumor, Lung tumor, Colon tumor and Spleen AI operators are used in the ROI configuration.

Multi AI pipeline has 4 AI models, it needs 24 GB of GPU memory with the example test dataset.

11.3.2. Data

Data required for running this pipeline is stored in artifactory.

Get the data packaged with pipeline from NGC.
Unzip app_multiai-input_v1.zip file. There are 4 folders inside multiAIData folder namely ‘data’, ‘roiZ’, ‘roiXYZ’ and ‘render’
If roi in Z dimensions is desired then copy roiZ/roi.txt to data/roi.txt, if ROI in XYZ dimensions is desired then copy roiXYZ/roi.txt to data/roi.txt.
If results is desired in render server, copy render/config_render.json to data/config_render.json
Look at /clara/common/models folder. Spleen, LungTumor, ColonTumor, LiverTumor models must be present.
If any of the model is missing, get the models from NGC as zip folder.
Unzip app_multiai-model_v1.zip file. Copy missing models from zip file to /clara/common/models

11.3.3. Execution Steps on Clara Platform

The following are the execution steps for multi AI Pipeline:

Data preparation
- Store location of folder ‘data’ after updating configuration files as described in above section.
Platform Installation: Ensure that platform is installed successfully and Clara CLI is installed correctly.
Required Models: Ensure that spleen, colon tumor, lung tumor and liver tumor models are present in /clara/common/models folder.
Required Operators: Following operators must be installed. If these operators are not present in the system, either build them locally or pull them from https://ngc.nvidia.com/containers.
- roi-generator
- app_lungtumor
- app_livertumor
- app_spleen
- app_colontumor
- register-results
- pod-manager
Update the pipeline definition with appropriate container image and tag information. Ensure that container image and tag are present in the system before executing the pipeline.
Create roi-generator: This is not a mandatory step. Alternatively, ‘roi-generator’ can be build locally. Following are the steps:
- Execute ‘docker images’ and locate the ‘TAG’ of clara/python-base image.
- Execute following to tag python-base image, use value of ‘TAG’ as identified in above step:
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```
            
            docker tag clara/python-base:'TAG' clara/python-base:latest
        
```
- Navigate to Applications/Operators/image_processing/app_roi/ folder in sdk. Execute ‘make’.
- Execute ‘docker images’ and verify that roi-generator operator is created. Update the pipeline definition with appropriate container image ang tag information

Execute following in sequence using Clara cli:

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            clara create pipeline -p <path to multiAI pipeline>
clara create jobs -n <name> -p <pipeline ID> -f <path to folder 'data' as identified in step 1>
clara start job -j <JOB ID>

Check payloads for the mask or visualize the results in render server.