Terjemahan disediakan oleh mesin penerjemah. Jika konten terjemahan yang diberikan bertentangan dengan versi bahasa Inggris aslinya, utamakan versi bahasa Inggris.
{ "CacheConfig": { "Enabled": false, "ExpireAfter": "<time>" } }
from sagemaker.workflow.pipeline_context import PipelineSession from sagemaker.workflow.steps import CacheConfig from sagemaker.workflow.pipeline import Pipeline cache_config = CacheConfig(enable_caching=False, expire_after="PT1H") estimator = Estimator(..., sagemaker_session=PipelineSession()) step_train = TrainingStep( name="TrainAbaloneModel", step_args=estimator.fit(inputs=inputs), cache_config=cache_config ) # define pipeline pipeline = Pipeline( steps=[step_train] ) # update the pipeline pipeline.update() # or, call upsert() to update the pipeline # pipeline.upsert()
# turn off caching for the training step pipeline.steps[0].cache_config.enable_caching = False # update the pipeline pipeline.update() # or, call upsert() to update the pipeline # pipeline.upsert()
