11.23. Digital Pathology Nuclei Segmentation Pipeline

            

            
api-version: 0.4.0
name: dp-nuclei-segmentation-pipeline
parameters:
  NUM_WORKERS: -1              # -1: # of cpus
operators:
  - name: segmentation
    description: Do cell nuclei segmentation
    container:
      image: clara/dp-nuclei-seg
      tag: latest
      command: ["/bin/bash", "-c", "python-u/app/main.pysegmentation_skimage--num-workers=${{NUM_WORKERS}}"]
    requests:
      gpu: 1
    input:
    - path: /input
    - path: /config
    output:
    - path: /output
    services:
      - name: triton
        # TRITON inference server, required by this AI application.
        container:
          image: nvcr.io/nvidia/tritonserver
          tag: 20.07-v1-py3
          command: ["tritonserver", "--model-repository=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models"]
        # services::connections defines how the TRITON service is expected to
        # be accessed. Clara Platform supports network ("http") and
        # volume ("file") connections.
        connections:
          http:
          # The name of the connection is used to populate an environment
          # variable inside the operator's container during execution.
          # This AI application inside the container needs to read this variable to
          # know the IP and port of TRITON in order to connect to the service.
          - name: NVIDIA_CLARA_TRTISURI
            port: 8000
          # Some services need a specialized or minimal set of hardware. In this case
          # NVIDIA TRITON inference server requires at least one GPU to function.
  - name: register-images-for-rendering
    description: Register pyramid images in tiff format for rendering.
    container:
      image: clara/register-results
      tag: latest
      command: ["python", "register.py", "--agent", "renderserver"]
    input:
    - from: segmentation
      path: /input
        

            

            
mkdir -p ~/.clara/pipelines
cd ~/.clara/pipelines
# Download pipeline
clara pull pipeline clara_dp_nuclei_seg_pipeline
cd clara_dp_nuclei_seg_pipeline
# Unzip test input data
unzip app_dp_nuclei_seg-input_v1.zip -d input
# Unzip model data
sudo unzip app_dp_nuclei_seg-model_v1.zip -d /clara/common/models/
# Create a pipeline
clara create pipeline -p dp-nuclei-segmentation-pipeline.yaml

clara create jobs -p <PIPELINE ID> -n <JOB NAME> -f <INPUT PATH>

# Start a job (with parameters `NUM_WORKERS` if needed. e.g., '-a NUM_WORKERS=8')
clara start job -j <JOB ID>
        

            

            
mkdir -p ~/.clara/pipelines
cd ~/.clara/pipelines
# Download pipeline
clara pull pipeline clara_dp_nuclei_seg_pipeline
cd clara_dp_nuclei_seg_pipeline
# Unzip test input data
unzip app_dp_nuclei_seg-input_v1.zip -d input
# Unzip model data
sudo unzip app_dp_nuclei_seg-model_v1.zip -d /clara/common/models/
# Run script
./run_pipeline.sh