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pipeline.yml
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$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json
type: pipeline
description: Pipeline using AutoML Image Multilabel Classification task
display_name: pipeline-with-image-classification-multilabel
experiment_name: pipeline-with-automl
settings:
default_compute: azureml:gpu-cluster
inputs:
image_multilabel_classification_training_data:
type: mltable
path: data/training-mltable-folder
image_multilabel_classification_validation_data:
type: mltable
path: data/validation-mltable-folder
jobs:
image_multilabel_classification_node:
type: automl
task: image_classification_multilabel
log_verbosity: info
primary_metric: iou
limits:
timeout_minutes: 180
target_column_name: label
training_data: ${{parent.inputs.image_multilabel_classification_training_data}}
validation_data: ${{parent.inputs.image_multilabel_classification_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: "vitb16r224"
learning_rate: "uniform(0.005, 0.05)"
number_of_epochs: "choice(15, 30)"
grad_accumulation_step: "choice(1, 2)"
- model_name: "seresnext"
learning_rate: "uniform(0.005, 0.05)"
validation_resize_size: "choice(288, 320, 352)"
validation_crop_size: "choice(224, 256)"
training_crop_size: "choice(224, 256)"
# 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_multilabel_classification_node.outputs.best_model}}
model_base_name: fridge_items_multilabel_classification_model