Merge branch 'master' into neo-input-shape-derivation-comment

Author: HappyAmazonian

doc/api/inference/deserializers.rst

@@ -1,8 +1,12 @@
 Deserializers
 -------------
 
-.. automodule:: sagemaker.deserializers
+.. automodule:: sagemaker.base_deserializers
     :members:
     :undoc-members:
     :show-inheritance:
 
+.. automodule:: sagemaker.deserializers
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/api/inference/serializers.rst

@@ -1,8 +1,12 @@
 Serializers
 -----------
 
-.. automodule:: sagemaker.serializers
+.. automodule:: sagemaker.base_serializers
     :members:
     :undoc-members:
     :show-inheritance:
 
+.. automodule:: sagemaker.serializers
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/api/prep_data/feature_store.rst

@@ -41,6 +41,25 @@ Feature Definition
     :members:
     :show-inheritance:
 
+.. autoclass:: sagemaker.feature_store.feature_definition.CollectionTypeEnum
+    :members:
+    :show-inheritance:
+
+.. autoclass:: sagemaker.feature_store.feature_definition.CollectionType
+    :members:
+    :show-inheritance:
+
+.. autoclass:: sagemaker.feature_store.feature_definition.ListCollectionType
+    :members:
+    :show-inheritance:
+
+.. autoclass:: sagemaker.feature_store.feature_definition.SetCollectionType
+    :members:
+    :show-inheritance:
+
+.. autoclass:: sagemaker.feature_store.feature_definition.VectorCollectionType
+    :members:
+    :show-inheritance:
 
 Inputs
 ******
@@ -77,6 +96,10 @@ Inputs
     :members:
     :show-inheritance:
 
+.. autoclass:: sagemaker.feature_store.inputs.OnlineStoreStorageTypeEnum
+    :members:
+    :show-inheritance:
+
 .. autoclass:: sagemaker.feature_store.inputs.ResourceEnum
     :members:
     :show-inheritance:

doc/workflows/airflow/using_workflow.rst

@@ -22,6 +22,16 @@ the configuration dictionary in the SageMaker Python SDK. Currently, the followi
 * ``SageMakerTransformOperator``
 * ``SageMakerEndpointConfigOperator``
 * ``SageMakerEndpointOperator``
+* ``SageMakerProcessingOperator``
+* ``SageMakerDeleteModelOperator``
+* ``SageMakerStartPipelineOperator``
+* ``SageMakerStopPipelineOperator``
+* ``SageMakerRegisterModelVersionOperator``
+* ``SageMakerAutoMLOperator``
+* ``SageMakerCreateExperimentOperator``
+
+You can look into `the Apache Airflow documentation <https://airflow.apache.org/docs/apache-airflow-providers-amazon/stable/operators/sagemaker.html>`_ for the full
+list of operators with examples.
 
 2. PythonOperator: Airflow built-in operator that executes Python callables. You can use the PythonOperator to execute
 operations in the SageMaker Python SDK to create a SageMaker workflow.

src/sagemaker/feature_store/feature_definition.py

@@ -39,6 +39,87 @@ class FeatureTypeEnum(Enum):
     STRING = "String"
 
 
+class CollectionTypeEnum(Enum):
+    """Enum of collection types.
+
+    The collection type of a feature can be List, Set or Vector.
+    """
+
+    LIST = "List"
+    SET = "Set"
+    VECTOR = "Vector"
+
+
+@attr.s
+class CollectionType(Config):
+    """Collection type and its configuration.
+
+    This initiates a collectiontype object where CollectionType is a subclass of Config.
+
+    Attributes:
+        collection_type (CollectionTypeEnum): The type of the collection
+        collection_config (Dict[str, Any]): The configuration for the collection.
+    """
+
+    collection_type: CollectionTypeEnum = attr.ib()
+    collection_config: Dict[str, Any] = attr.ib()
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Construct a dictionary based on each attribute."""
+        return Config.construct_dict(
+            CollectionType=self.collection_type.value, CollectionConfig=self.collection_config
+        )
+
+
+class ListCollectionType(CollectionType):
+    """List collection type
+
+    This class instantiates a ListCollectionType object, as subclass of CollectionType
+    where the collection type is defined as List.
+
+    """
+
+    def __init__(self):
+        """Construct an instance of ListCollectionType."""
+        super(ListCollectionType, self).__init__(CollectionTypeEnum.LIST, None)
+
+
+class SetCollectionType(CollectionType):
+    """Set collection type
+
+    This class instantiates a SetCollectionType object, as subclass of CollectionType
+    where the collection type is defined as Set.
+
+    """
+
+    def __init__(self):
+        """Construct an instance of SetCollectionType."""
+        super(SetCollectionType, self).__init__(CollectionTypeEnum.SET, None)
+
+
+class VectorCollectionType(CollectionType):
+    """Vector collection type
+
+    This class instantiates a VectorCollectionType object, as subclass of CollectionType
+    where the collection type is defined as Vector.
+
+    Attributes:
+        dimension (int): The dimension size for the Vector.
+    """
+
+    def __init__(self, dimension: int):
+        """Construct an instance of VectorCollectionType.
+
+        Attributes:
+        dimension (int): The dimension size for the Vector.
+        """
+        collection_config: Dict[str, Any] = {}
+        vector_config: Dict[str, Any] = {}
+        vector_config["Dimension"] = dimension
+        collection_config["VectorConfig"] = vector_config
+        super(VectorCollectionType, self).__init__(CollectionTypeEnum.VECTOR, collection_config)
+
+
 @attr.s
 class FeatureDefinition(Config):
     """Feature definition.
@@ -48,15 +129,25 @@ class FeatureDefinition(Config):
     Attributes:
         feature_name (str): The name of the feature
         feature_type (FeatureTypeEnum): The type of the feature
+        collection_type (CollectionType): The type of collection for the feature
     """
 
     feature_name: str = attr.ib()
     feature_type: FeatureTypeEnum = attr.ib()
+    collection_type: CollectionType = attr.ib(default=None)
 
     def to_dict(self) -> Dict[str, Any]:
         """Construct a dictionary based on each attribute."""
+
         return Config.construct_dict(
-            FeatureName=self.feature_name, FeatureType=self.feature_type.value
+            FeatureName=self.feature_name,
+            FeatureType=self.feature_type.value,
+            CollectionType=(
+                self.collection_type.collection_type.value if self.collection_type else None
+            ),
+            CollectionConfig=(
+                self.collection_type.collection_config if self.collection_type else None
+            ),
         )
 
 
@@ -69,15 +160,18 @@ class FractionalFeatureDefinition(FeatureDefinition):
     Attributes:
         feature_name (str): The name of the feature
         feature_type (FeatureTypeEnum): A `FeatureTypeEnum.FRACTIONAL` type
+        collection_type (CollectionType): The type of collection for the feature
     """
 
-    def __init__(self, feature_name: str):
+    def __init__(self, feature_name: str, collection_type: CollectionType = None):
         """Construct an instance of FractionalFeatureDefinition.
 
         Args:
             feature_name (str): the name of the feature.
         """
-        super(FractionalFeatureDefinition, self).__init__(feature_name, FeatureTypeEnum.FRACTIONAL)
+        super(FractionalFeatureDefinition, self).__init__(
+            feature_name, FeatureTypeEnum.FRACTIONAL, collection_type
+        )
 
 
 class IntegralFeatureDefinition(FeatureDefinition):
@@ -89,15 +183,18 @@ class IntegralFeatureDefinition(FeatureDefinition):
     Attributes:
         feature_name (str): the name of the feature.
         feature_type (FeatureTypeEnum): a `FeatureTypeEnum.INTEGRAL` type.
+        collection_type (CollectionType): The type of collection for the feature.
     """
 
-    def __init__(self, feature_name: str):
+    def __init__(self, feature_name: str, collection_type: CollectionType = None):
         """Construct an instance of IntegralFeatureDefinition.
 
         Args:
             feature_name (str): the name of the feature.
         """
-        super(IntegralFeatureDefinition, self).__init__(feature_name, FeatureTypeEnum.INTEGRAL)
+        super(IntegralFeatureDefinition, self).__init__(
+            feature_name, FeatureTypeEnum.INTEGRAL, collection_type
+        )
 
 
 class StringFeatureDefinition(FeatureDefinition):
@@ -109,12 +206,15 @@ class StringFeatureDefinition(FeatureDefinition):
     Attributes:
         feature_name (str): the name of the feature.
         feature_type (FeatureTypeEnum): a `FeatureTypeEnum.STRING` type.
+        collection_type (CollectionType): The type of collection for the feature.
     """
 
-    def __init__(self, feature_name: str):
+    def __init__(self, feature_name: str, collection_type: CollectionType = None):
         """Construct an instance of StringFeatureDefinition.
 
         Args:
             feature_name (str): the name of the feature.
         """
-        super(StringFeatureDefinition, self).__init__(feature_name, FeatureTypeEnum.STRING)
+        super(StringFeatureDefinition, self).__init__(
+            feature_name, FeatureTypeEnum.STRING, collection_type
+        )

src/sagemaker/feature_store/feature_group.py

@@ -63,6 +63,7 @@
     DeletionModeEnum,
     TtlDuration,
     OnlineStoreConfigUpdate,
+    OnlineStoreStorageTypeEnum,
 )
 from sagemaker.utils import resolve_value_from_config
 
@@ -539,6 +540,7 @@ def create(
         description: str = None,
         tags: List[Dict[str, str]] = None,
         table_format: TableFormatEnum = None,
+        online_store_storage_type: OnlineStoreStorageTypeEnum = None,
     ) -> Dict[str, Any]:
         """Create a SageMaker FeatureStore FeatureGroup.
 
@@ -566,6 +568,8 @@ def create(
             description (str): description of the FeatureGroup (default: None).
             tags (List[Dict[str, str]]): list of tags for labeling a FeatureGroup (default: None).
             table_format (TableFormatEnum): format of the offline store table (default: None).
+            online_store_storage_type (OnlineStoreStorageTypeEnum): storage type for the
+                online store (default: None).
 
         Returns:
             Response dict from service.
@@ -606,6 +610,7 @@ def create(
             online_store_config = OnlineStoreConfig(
                 enable_online_store=enable_online_store,
                 ttl_duration=ttl_duration,
+                storage_type=online_store_storage_type,
             )
             if online_store_kms_key_id is not None:
                 online_store_config.online_store_security_config = OnlineStoreSecurityConfig(

src/sagemaker/feature_store/inputs.py

@@ -108,6 +108,16 @@ def to_dict(self) -> Dict[str, Any]:
         )
 
 
+class OnlineStoreStorageTypeEnum(Enum):
+    """Enum of storage types for online store.
+
+    The online store storage types can be Standard or InMemory.
+    """
+
+    STANDARD = "Standard"
+    IN_MEMORY = "InMemory"
+
+
 @attr.s
 class OnlineStoreConfig(Config):
     """OnlineStoreConfig for FeatureStore.
@@ -121,6 +131,7 @@ class OnlineStoreConfig(Config):
     enable_online_store: bool = attr.ib(default=True)
     online_store_security_config: OnlineStoreSecurityConfig = attr.ib(default=None)
     ttl_duration: TtlDuration = attr.ib(default=None)
+    storage_type: OnlineStoreStorageTypeEnum = attr.ib(default=None)
 
     def to_dict(self) -> Dict[str, Any]:
         """Construct a dictionary based on the attributes.
@@ -132,6 +143,7 @@ def to_dict(self) -> Dict[str, Any]:
             EnableOnlineStore=self.enable_online_store,
             SecurityConfig=self.online_store_security_config,
             TtlDuration=self.ttl_duration,
+            StorageType=self.storage_type.value if self.storage_type else None,
         )
 
 
@@ -254,10 +266,13 @@ class FeatureValue(Config):
     Attributes:
         feature_name (str): name of the Feature.
         value_as_string (str): value of the Feature in string form.
+        value_as_string_list (List[str]): value of the Feature in string list
+        form used for collection type.
     """
 
     feature_name: str = attr.ib(default=None)
     value_as_string: str = attr.ib(default=None)
+    value_as_string_list: List[str] = attr.ib(default=None)
 
     def to_dict(self) -> Dict[str, Any]:
         """Construct a dictionary based on the attributes provided.
@@ -268,6 +283,7 @@ def to_dict(self) -> Dict[str, Any]:
         return Config.construct_dict(
             FeatureName=self.feature_name,
             ValueAsString=self.value_as_string,
+            ValueAsStringList=self.value_as_string_list,
         )
 
 

src/sagemaker/model.py

@@ -1101,7 +1101,7 @@ def compile(
             input_shape (dict): Specifies the name and shape of the expected
                 inputs for your trained model in json dictionary form, for
                 example: {'data': [1,3,1024,1024]}, or {'var1': [1,1,28,28],
-                'var2': [1,1,28,28]}. Input shape is optional for those models traced with torch 2.0, 
+                'var2': [1,1,28,28]}. Input shape is optional for those models traced with torch 2.0,
                 see pytorch section of this document for detail:
                 https://docs.aws.amazon.com/sagemaker/latest/dg/neo-compilation-preparing-model.html
             output_path (str): Specifies where to store the compiled model