job-sweep.yml

$schema: https://azuremlschemas.azureedge.net/latest/sweepJob.schema.json
type: sweep
trial:
  code: src
  command: >-
    python main.py
    --iris-csv ${{inputs.iris_csv}}
    --learning-rate ${{search_space.learning_rate}}
    --boosting ${{search_space.boosting}}
  environment: azureml:AzureML-lightgbm-3.3@latest
inputs:
  iris_csv:
    type: uri_file
    path: https://azuremlexamples.blob.core.windows.net/datasets/iris.csv
compute: azureml:cpu-cluster
sampling_algorithm: random
search_space:
  learning_rate:
    type: uniform
    min_value: 0.01
    max_value: 0.9
  boosting:
    type: choice
    values: ["gbdt", "dart"]
objective:
  goal: minimize
  primary_metric: test-multi_logloss
limits:
  max_total_trials: 20
  max_concurrent_trials: 10
  timeout: 7200
display_name: lightgbm-iris-sweep-example
experiment_name: lightgbm-iris-sweep-example
description: Run a hyperparameter sweep job for LightGBM on Iris dataset.