TruncationSelectionPolicy Class
Defines an early termination policy that cancels a given percentage of runs at each evaluation interval.
Constructor
TruncationSelectionPolicy(*, delay_evaluation: int = 0, evaluation_interval: int = 0, truncation_percentage: int = 0)Keyword-Only Parameters
| Name | Description |
|---|---|
|
delay_evaluation
|
Number of intervals by which to delay the first evaluation. Defaults to 0. Default value: 0
|
|
evaluation_interval
|
Interval (number of runs) between policy evaluations. Defaults to 0. Default value: 0
|
|
truncation_percentage
|
The percentage of runs to cancel at each evaluation interval. Defaults to 0. Default value: 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),
)
