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TruncationSelectionPolicy Class

  • Reference

Defines an early termination policy that cancels a given percentage of runs at each evaluation interval.

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

Constructor

TruncationSelectionPolicy(*, delay_evaluation: int = 0, evaluation_interval: int = 0, truncation_percentage: int = 0)

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 0.
truncation_percentage
int

The percentage of runs to cancel at each evaluation interval. Defaults to 0.

Examples

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


   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 QUniform, TruncationSelectionPolicy, Uniform

   job_for_sweep = job(
       kernel=Uniform(min_value=0.0005, max_value=0.005),
       penalty=QUniform(min_value=0.05, max_value=0.75, q=1),
   )

   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=TruncationSelectionPolicy(delay_evaluation=5, evaluation_interval=2),
   )