add new sklearn release 1.0-1

doc/amazon_sagemaker_processing.rst

@@ -36,7 +36,7 @@ You first create a ``SKLearnProcessor``
     from sagemaker.sklearn.processing import SKLearnProcessor
 
     sklearn_processor = SKLearnProcessor(
-        framework_version="0.20.0",
+        framework_version="1.0-1",
         role="[Your SageMaker-compatible IAM role]",
         instance_type="ml.m5.xlarge",
         instance_count=1,

doc/frameworks/sklearn/using_sklearn.rst

@@ -139,7 +139,7 @@ directories ('train' and 'test').
 
     sklearn_estimator = SKLearn('sklearn-train.py',
                                 instance_type='ml.m4.xlarge',
-                                framework_version='0.20.0',
+                                framework_version='1.0-1',
                                 hyperparameters = {'epochs': 20, 'batch-size': 64, 'learning-rate': 0.1})
     sklearn_estimator.fit({'train': 's3://my-data-bucket/path/to/my/training/data',
                             'test': 's3://my-data-bucket/path/to/my/test/data'})
@@ -203,7 +203,7 @@ operation.
     # Train my estimator
     sklearn_estimator = SKLearn(entry_point='train_and_deploy.py',
                                 instance_type='ml.m4.xlarge',
-                                framework_version='0.20.0')
+                                framework_version='1.0-1')
     sklearn_estimator.fit('s3://my_bucket/my_training_data/')
 
     # Deploy my estimator to a SageMaker Endpoint and get a Predictor
@@ -472,7 +472,7 @@ The following code sample shows how to do this, using the ``SKLearnModel`` class
     sklearn_model = SKLearnModel(model_data="s3://bucket/model.tar.gz",
                                  role="SageMakerRole",
                                  entry_point="transform_script.py",
-                                 framework_version="0.20.0")
+                                 framework_version="1.0-1")
 
     predictor = sklearn_model.deploy(instance_type="ml.c4.xlarge", initial_instance_count=1)
 

doc/overview.rst

@@ -1621,7 +1621,7 @@ To train a model in network isolation mode, set the optional parameter ``enable_
     # set the enable_network_isolation parameter to True
     sklearn_estimator = SKLearn('sklearn-train.py',
                                 instance_type='ml.m4.xlarge',
-                                framework_version='0.20.0',
+                                framework_version='1.0-1',
                                 hyperparameters = {'epochs': 20, 'batch-size': 64, 'learning-rate': 0.1},
                                 enable_network_isolation=True)
 

src/sagemaker/workflow/_utils.py

@@ -37,7 +37,7 @@
 if TYPE_CHECKING:
     from sagemaker.workflow.step_collections import StepCollection
 
-FRAMEWORK_VERSION = "0.23-1"
+FRAMEWORK_VERSION = "1.0-1"
 INSTANCE_TYPE = "ml.m5.large"
 REPACK_SCRIPT = "_repack_model.py"
 

tests/integ/sagemaker/workflow/test_workflow.py

@@ -677,7 +677,7 @@ def test_end_to_end_pipeline_successful_execution(
     )
 
     # define processing step
-    framework_version = "0.23-1"
+    framework_version = "1.0-1"
     sklearn_processor = SKLearnProcessor(
         framework_version=framework_version,
         instance_type=processing_instance_type,

tests/unit/sagemaker/image_uris/jumpstart/test_sklearn.py

@@ -55,7 +55,7 @@ def test_jumpstart_sklearn_image_uri(patched_get_model_specs, session):
 
     assert uri == framework_class_uri
     assert (
-        uri == "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:0.23-1-cpu-py3"
+        uri == "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:1.0-1-cpu-py3"
     )
 
     # training
@@ -81,7 +81,7 @@ def test_jumpstart_sklearn_image_uri(patched_get_model_specs, session):
 
     assert uri == framework_class_uri
     assert (
-        uri == "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:0.23-1-cpu-py3"
+        uri == "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:1.0-1-cpu-py3"
     )
 
     with pytest.raises(ValueError):

tests/unit/sagemaker/jumpstart/constants.py

@@ -959,7 +959,7 @@
         "incremental_training_supported": False,
         "hosting_ecr_specs": {
             "framework": "sklearn",
-            "framework_version": "0.23-1",
+            "framework_version": "1.0-1",
             "py_version": "py3",
         },
         "hosting_artifact_key": "sklearn-infer/infer-sklearn-classification-linear.tar.gz",
@@ -1025,7 +1025,7 @@
         "training_script_key": "source-directory-tarballs/sklearn/transfer_learning/classification/"
         "v1.0.0/sourcedir.tar.gz",
         "training_ecr_specs": {
-            "framework_version": "0.23-1",
+            "framework_version": "1.0-1",
             "framework": "sklearn",
             "py_version": "py3",
         },

tests/unit/sagemaker/lineage/test_query.py

@@ -404,7 +404,7 @@ def test_vertex_to_dataset_artifact(sagemaker_session):
     sagemaker_session.sagemaker_client.describe_artifact.return_value = {
         "ArtifactArn": "arn:aws:sagemaker:us-west-2:0123456789012:artifact/e66eef7f19c05e75284089183491bd4f",
         "Source": {
-            "SourceUri": "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:0.23-1-cpu-py3",
+            "SourceUri": "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:1.0-1-cpu-py3",
             "SourceTypes": [],
         },
         "ArtifactType": "Image",
@@ -435,7 +435,7 @@ def test_vertex_to_model_artifact(sagemaker_session):
     sagemaker_session.sagemaker_client.describe_artifact.return_value = {
         "ArtifactArn": "arn:aws:sagemaker:us-west-2:0123456789012:artifact/e66eef7f19c05e75284089183491bd4f",
         "Source": {
-            "SourceUri": "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:0.23-1-cpu-py3",
+            "SourceUri": "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:1.0-1-cpu-py3",
             "SourceTypes": [],
         },
         "ArtifactType": "Image",
@@ -466,7 +466,7 @@ def test_vertex_to_object_image_artifact(sagemaker_session):
     sagemaker_session.sagemaker_client.describe_artifact.return_value = {
         "ArtifactArn": "arn:aws:sagemaker:us-west-2:0123456789012:artifact/e66eef7f19c05e75284089183491bd4f",
         "Source": {
-            "SourceUri": "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:0.23-1-cpu-py3",
+            "SourceUri": "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:1.0-1-cpu-py3",
             "SourceTypes": [],
         },
         "ArtifactType": "Image",
@@ -497,7 +497,7 @@ def test_vertex_to_object_action(sagemaker_session):
     sagemaker_session.sagemaker_client.describe_action.return_value = {
         "ActionName": "cp-m5-20210424t041405868z-1619237657-1-aws-endpoint",
         "Source": {
-            "SourceUri": "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:0.23-1-cpu-py3",
+            "SourceUri": "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:1.0-1-cpu-py3",
             "SourceTypes": [],
         },
         "ActionType": "A",

tests/unit/sagemaker/model/test_model.py

@@ -598,7 +598,7 @@ def test_all_framework_models_add_jumpstart_tags(
         },
         MXNetModel: {"framework_version": "1.7.0", "py_version": "py3"},
         SKLearnModel: {
-            "framework_version": "0.23-1",
+            "framework_version": "1.0-1",
         },
         XGBoostModel: {
             "framework_version": "1.3-1",
@@ -690,7 +690,7 @@ def test_all_framework_models_add_jumpstart_base_name(
         },
         MXNetModel: {"framework_version": "1.7.0", "py_version": "py3"},
         SKLearnModel: {
-            "framework_version": "0.23-1",
+            "framework_version": "1.0-1",
         },
         XGBoostModel: {
             "framework_version": "1.3-1",

tests/unit/sagemaker/workflow/test_processing_step.py

@@ -233,7 +233,7 @@ def test_processing_step_with_script_processor(pipeline_session, processing_inpu
         ),
         (
             SKLearnProcessor(
-                framework_version="0.23-1",
+                framework_version="1.0-1",
                 instance_type=INSTANCE_TYPE,
                 instance_count=1,
                 role=sagemaker.get_execution_role(),

tests/unit/sagemaker/workflow/test_step_collections.py

@@ -63,7 +63,7 @@
 ROLE = "DummyRole"
 MODEL_NAME = "gisele"
 MODEL_REPACKING_IMAGE_URI = (
-    "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:0.23-1-cpu-py3"
+    "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:1.0-1-cpu-py3"
 )
 
 
@@ -1044,7 +1044,7 @@ def test_estimator_transformer_with_model_repack_with_estimator(estimator):
                 "AlgorithmSpecification": {
                     "TrainingInputMode": "File",
                     "TrainingImage": "246618743249.dkr.ecr.us-west-2.amazonaws.com/"
-                    + "sagemaker-scikit-learn:0.23-1-cpu-py3",
+                    + "sagemaker-scikit-learn:1.0-1-cpu-py3",
                 },
                 "OutputDataConfig": {"S3OutputPath": "s3://my-bucket/"},
                 "StoppingCondition": {"MaxRuntimeInSeconds": 86400},

tests/unit/sagemaker/workflow/test_training_step.py

@@ -160,7 +160,7 @@ def test_estimator_with_parameterized_output(pipeline_session, training_input):
     "estimator",
     [
         SKLearn(
-            framework_version="0.23-1",
+            framework_version="1.0-1",
             py_version="py3",
             instance_type=INSTANCE_TYPE,
             instance_count=1,

tests/unit/test_estimator.py

@@ -3808,7 +3808,7 @@ def test_all_framework_estimators_add_jumpstart_tags(
             "instance_type": "ml.p2.xlarge",
         },
         MXNet: {"framework_version": "1.7.0", "py_version": "py3", "instance_type": "ml.p2.xlarge"},
-        SKLearn: {"framework_version": "0.23-1", "instance_type": "ml.m2.xlarge"},
+        SKLearn: {"framework_version": "1.0-1", "instance_type": "ml.m2.xlarge"},
         XGBoost: {"framework_version": "1.3-1", "instance_type": "ml.m2.xlarge"},
     }
     jumpstart_model_uri = f"s3://{list(JUMPSTART_BUCKET_NAME_SET)[0]}/model_dirs/model.tar.gz"
@@ -3959,7 +3959,7 @@ def test_all_framework_estimators_add_jumpstart_base_name(
             "instance_type": "ml.p2.xlarge",
         },
         MXNet: {"framework_version": "1.7.0", "py_version": "py3", "instance_type": "ml.p2.xlarge"},
-        SKLearn: {"framework_version": "0.23-1", "instance_type": "ml.m2.xlarge"},
+        SKLearn: {"framework_version": "1.0-1", "instance_type": "ml.m2.xlarge"},
         XGBoost: {"framework_version": "1.3-1", "instance_type": "ml.m2.xlarge"},
     }
     jumpstart_model_uri = f"s3://{list(JUMPSTART_BUCKET_NAME_SET)[0]}/model_dirs/model.tar.gz"