AWS sagemaker documentation change
Summary
Removed legacy SageMaker Python SDK v2 code examples and implementation instructions
Security assessment
Changes delete deprecated SDK usage examples without modifying security controls, addressing vulnerabilities, or adding security documentation.
Diff
diff --git a/sagemaker/latest/dg/automatic-model-tuning-warm-start.md b/sagemaker/latest/dg/automatic-model-tuning-warm-start.md index 9eb3527ca..8be0ad9ee 100644 --- a//sagemaker/latest/dg/automatic-model-tuning-warm-start.md +++ b//sagemaker/latest/dg/automatic-model-tuning-warm-start.md @@ -130,3 +129,0 @@ To use the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/sta -SageMaker Python SDK v3 - - @@ -142,12 +138,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - * Specify the parent jobs and the warm start type by using a `WarmStartConfig` object. - - * Pass the `WarmStartConfig` object as the value of the `warm_start_config` argument of a [HyperparameterTuner](https://sagemaker.readthedocs.io/en/stable/tuner.html) object. - - * Call the `fit` method of the `HyperparameterTuner` object. - - - - @@ -164,3 +148,0 @@ The following code configures the warm start tuning job by creating a `WarmStart -SageMaker Python SDK v3 - - @@ -174,9 +155,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - from sagemaker.tuner import WarmStartConfig,WarmStartTypes - - parent_tuning_job_name = "MyParentTuningJob" - warm_start_config = WarmStartConfig(warm_start_type=WarmStartTypes.IDENTICAL_DATA_AND_ALGORITHM, parents={parent_tuning_job_name}) - @@ -185,3 +157,0 @@ Now set the values for static hyperparameters, which are hyperparameters that ke -SageMaker Python SDK v3 - - @@ -210,15 +179,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - imageclassification.set_hyperparameters(num_layers=18, - image_shape='3,224,224', - num_classes=257, - num_training_samples=15420, - mini_batch_size=128, - epochs=30, - optimizer='sgd', - top_k='2', - precision_dtype='float32', - augmentation_type='crop') - @@ -227,3 +181,0 @@ Now create the `HyperparameterTuner` object and pass the `WarmStartConfig` objec -SageMaker Python SDK v3 - - @@ -240,13 +191,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - tuner_warm_start = HyperparameterTuner(imageclassification, - 'validation:accuracy', - hyperparameter_ranges, - objective_type='Maximize', - max_jobs=10, - max_parallel_jobs=2, - base_tuning_job_name='warmstart', - warm_start_config=warm_start_config) - @@ -255,3 +193,0 @@ Finally, call the `tune` or `fit` method of the `HyperparameterTuner` object to -SageMaker Python SDK v3 - - @@ -262,8 +197,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - tuner_warm_start.fit( - {'train': s3_input_train, 'validation': s3_input_validation}, - include_cls_metadata=False) -