MedianStoppingPolicy Class

Defines an early termination policy based on a running average of the primary metric of all runs.

Inheritance

azure.ai.ml.entities._job.sweep.early_termination_policy.EarlyTerminationPolicy

MedianStoppingPolicy

Constructor

MedianStoppingPolicy(*, delay_evaluation: int = 0, evaluation_interval: int = 1)

Keyword-Only Parameters

Name	Description
delay_evaluation	int Number of intervals by which to delay the first evaluation. Defaults to 0.
evaluation_interval	int Interval (number of runs) between policy evaluations. Defaults to 1. Default value: 1

Examples

Configuring an early termination policy for a hyperparameter sweep job using MedianStoppingPolicy

   from azure.ai.ml import command

   job = command(
       inputs=dict(kernel="linear", penalty=1.0),
       compute=cpu_cluster,
       environment=f"{job_env.name}:{job_env.version}",
       code="./scripts",
       command="python scripts/train.py --kernel $kernel --penalty $penalty",
       experiment_name="sklearn-iris-flowers",
   )

   # we can reuse an existing Command Job as a function that we can apply inputs to for the sweep configurations
   from azure.ai.ml.sweep import MedianStoppingPolicy, Uniform

   job_for_sweep = job(
       kernel=Uniform(min_value=0.0005, max_value=0.005),
       penalty=Uniform(min_value=0.9, max_value=0.99),
   )

   sweep_job = job_for_sweep.sweep(
       sampling_algorithm="random",
       primary_metric="best_val_acc",
       goal="Maximize",
       max_total_trials=8,
       max_concurrent_trials=4,
       early_termination_policy=MedianStoppingPolicy(delay_evaluation=5, evaluation_interval=2),
   )