pipeline.yml

$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json
type: pipeline

description: Pipeline using AutoML Image Instance Segmentation task

display_name: pipeline-with-image-instance-segmentation
experiment_name: pipeline-with-automl

settings:
  default_compute: azureml:gpu-cluster

inputs:
  image_instance_segmentation_training_data:
    type: mltable
    path: data/training-mltable-folder
  image_instance_segmentation_validation_data:
    type: mltable
    path: data/validation-mltable-folder

jobs:
  image_instance_segmentation_node:
    type: automl
    task: image_instance_segmentation
    log_verbosity: info
    primary_metric: mean_average_precision
    limits:
      timeout_minutes: 180
    target_column_name: label
    training_data: ${{parent.inputs.image_instance_segmentation_training_data}}
    validation_data: ${{parent.inputs.image_instance_segmentation_validation_data}}
    image_model:
      early_stopping: True
      evaluation_frequency: 1
    sweep:
      limits:
        max_trials: 10
        max_concurrent_trials: 2
      sampling_algorithm: random
      early_termination:
        type: bandit
        evaluation_interval: 2
        slack_factor: 0.2
        delay_evaluation: 6
    search_space:
      - model_name: "maskrcnn_resnet50_fpn"
        learning_rate: "uniform(0.0001, 0.001)"
        optimizer: "choice('sgd', 'adam', 'adamw')"
        min_size: "choice(600, 800)"
    # currently need to specify outputs "mlflow_model" explicitly to reference it in following nodes
    outputs:
      best_model:
        type: mlflow_model
  register_model_node:
    type: command
    component: file:./components/component_register_model.yaml
    inputs:
      model_input_path: ${{parent.jobs.image_instance_segmentation_node.outputs.best_model}}
      model_base_name: fridge_items_segmentation_model