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TruncationSelectionPolicy 類別

  • 參考

定義提早終止原則，以取消每個評估間隔的指定執行百分比。

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

建構函式

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

僅限關鍵字的參數

名稱 Description
delay_evaluation
int

延遲第一次評估的間隔數目。 預設為 0。
evaluation_interval
int

原則評估之間) 執行的間隔 (數目。 預設為 0。
truncation_percentage
int

要在每個評估間隔取消的執行百分比。 預設為 0。

範例

使用 TruncationStoppingPolicy 設定超參數掃掠作業的早期終止原則


   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),
   )