Merge branch 'master' into automl_from_v1_to_v2_mapper

src/sagemaker/base_predictor.py

@@ -52,6 +52,7 @@
     JSONSerializer,
     NumpySerializer,
 )
+from sagemaker.iterators import ByteIterator
 from sagemaker.session import production_variant, Session
 from sagemaker.utils import name_from_base, stringify_object, format_tags
 
@@ -225,6 +226,7 @@ def _create_request_args(
         target_variant=None,
         inference_id=None,
         custom_attributes=None,
+        target_container_hostname=None,
     ):
         """Placeholder docstring"""
 
@@ -286,9 +288,89 @@ def _create_request_args(
         if self._get_component_name():
             args["InferenceComponentName"] = self.component_name
 
+        if target_container_hostname:
+            args["TargetContainerHostname"] = target_container_hostname
+
         args["Body"] = data
         return args
 
+    def predict_stream(
+        self,
+        data,
+        initial_args=None,
+        target_variant=None,
+        inference_id=None,
+        custom_attributes=None,
+        component_name: Optional[str] = None,
+        target_container_hostname=None,
+        iterator=ByteIterator,
+    ):
+        """Return the inference from the specified endpoint.
+
+        Args:
+            data (object): Input data for which you want the model to provide
+                inference. If a serializer was specified when creating the
+                Predictor, the result of the serializer is sent as input
+                data. Otherwise the data must be sequence of bytes, and the
+                predict method then sends the bytes in the request body as is.
+            initial_args (dict[str,str]): Optional. Default arguments for boto3
+                ``invoke_endpoint_with_response_stream`` call. Default is None (no default
+                arguments). (Default: None)
+            target_variant (str): Optional. The name of the production variant to run an inference
+                request on (Default: None). Note that the ProductionVariant identifies the
+                model you want to host and the resources you want to deploy for hosting it.
+            inference_id (str): Optional. If you provide a value, it is added to the captured data
+                when you enable data capture on the endpoint (Default: None).
+            custom_attributes (str): Optional. Provides additional information about a request for
+                an inference submitted to a model hosted at an Amazon SageMaker endpoint.
+                The information is an opaque value that is forwarded verbatim. You could use this
+                value, for example, to provide an ID that you can use to track a request or to
+                provide other metadata that a service endpoint was programmed to process. The value
+                must consist of no more than 1024 visible US-ASCII characters.
+
+                The code in your model is responsible for setting or updating any custom attributes
+                in the response. If your code does not set this value in the response, an empty
+                value is returned. For example, if a custom attribute represents the trace ID, your
+                model can prepend the custom attribute with Trace ID: in your post-processing
+                function (Default: None).
+            component_name (str): Optional. Name of the Amazon SageMaker inference component
+                corresponding the predictor. (Default: None)
+            target_container_hostname (str): Optional. If the endpoint hosts multiple containers
+                and is configured to use direct invocation, this parameter specifies the host name
+                of the container to invoke. (Default: None).
+            iterator (:class:`~sagemaker.iterators.BaseIterator`): An iterator class which provides
+                an iterable interface to iterate Event stream response from Inference Endpoint.
+                An object of the iterator class provided will be returned by the predict_stream
+                method (Default::class:`~sagemaker.iterators.ByteIterator`). Iterators defined in
+                :class:`~sagemaker.iterators` or custom iterators (needs to inherit
+                :class:`~sagemaker.iterators.BaseIterator`) can be specified as an input.
+
+        Returns:
+            object (:class:`~sagemaker.iterators.BaseIterator`): An iterator object which would
+            allow iteration on EventStream response will be returned. The object would be
+            instantiated from `predict_stream` method's `iterator` parameter.
+        """
+        # [TODO]: clean up component_name in _create_request_args
+        request_args = self._create_request_args(
+            data=data,
+            initial_args=initial_args,
+            target_variant=target_variant,
+            inference_id=inference_id,
+            custom_attributes=custom_attributes,
+            target_container_hostname=target_container_hostname,
+        )
+
+        inference_component_name = component_name or self._get_component_name()
+        if inference_component_name:
+            request_args["InferenceComponentName"] = inference_component_name
+
+        response = (
+            self.sagemaker_session.sagemaker_runtime_client.invoke_endpoint_with_response_stream(
+                **request_args
+            )
+        )
+        return iterator(response["Body"])
+
     def update_endpoint(
         self,
         initial_instance_count=None,

src/sagemaker/exceptions.py

@@ -86,3 +86,23 @@ class AsyncInferenceModelError(AsyncInferenceError):
 
     def __init__(self, message):
         super().__init__(message=message)
+
+
+class ModelStreamError(Exception):
+    """Raised when invoke_endpoint_with_response_stream Response returns ModelStreamError"""
+
+    def __init__(self, message="An error occurred", code=None):
+        self.message = message
+        self.code = code
+        if code is not None:
+            super().__init__(f"{message} (Code: {code})")
+        else:
+            super().__init__(message)
+
+
+class InternalStreamFailure(Exception):
+    """Raised when invoke_endpoint_with_response_stream Response returns InternalStreamFailure"""
+
+    def __init__(self, message="An error occurred"):
+        self.message = message
+        super().__init__(self.message)

src/sagemaker/iterators.py

@@ -0,0 +1,186 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Implements iterators for deserializing data returned from an inference streaming endpoint."""
+from __future__ import absolute_import
+
+from abc import ABC, abstractmethod
+import io
+
+from sagemaker.exceptions import ModelStreamError, InternalStreamFailure
+
+
+def handle_stream_errors(chunk):
+    """Handle API Response errors within `invoke_endpoint_with_response_stream` API if any.
+
+    Args:
+        chunk (dict): A chunk of response received as part of `botocore.eventstream.EventStream`
+            response object.
+
+    Raises:
+        ModelStreamError: If `ModelStreamError` error is detected in a chunk of
+            `botocore.eventstream.EventStream` response object.
+        InternalStreamFailure: If `InternalStreamFailure` error is detected in a chunk of
+            `botocore.eventstream.EventStream` response object.
+    """
+    if "ModelStreamError" in chunk:
+        raise ModelStreamError(
+            chunk["ModelStreamError"]["Message"], code=chunk["ModelStreamError"]["ErrorCode"]
+        )
+    if "InternalStreamFailure" in chunk:
+        raise InternalStreamFailure(chunk["InternalStreamFailure"]["Message"])
+
+
+class BaseIterator(ABC):
+    """Abstract base class for Inference Streaming iterators.
+
+    Provides a skeleton for customization requiring the overriding of iterator methods
+    __iter__ and __next__.
+
+    Tenets of iterator class for Streaming Inference API Response
+    (https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/
+    sagemaker-runtime/client/invoke_endpoint_with_response_stream.html):
+    1. Needs to accept an botocore.eventstream.EventStream response.
+    2. Needs to implement logic in __next__ to:
+        2.1. Concatenate and provide next chunk of response from botocore.eventstream.EventStream.
+            While doing so parse the response_chunk["PayloadPart"]["Bytes"].
+        2.2. If PayloadPart not in EventStream response, handle Errors
+            [Recommended to use `iterators.handle_stream_errors` method].
+    """
+
+    def __init__(self, event_stream):
+        """Initialises a Iterator object to help parse the byte event stream input.
+
+        Args:
+            event_stream: (botocore.eventstream.EventStream): Event Stream object to be iterated.
+        """
+        self.event_stream = event_stream
+
+    @abstractmethod
+    def __iter__(self):
+        """Abstract method, returns an iterator object itself"""
+        return self
+
+    @abstractmethod
+    def __next__(self):
+        """Abstract method, is responsible for returning the next element in the iteration"""
+
+
+class ByteIterator(BaseIterator):
+    """A helper class for parsing the byte Event Stream input to provide Byte iteration."""
+
+    def __init__(self, event_stream):
+        """Initialises a BytesIterator Iterator object
+
+        Args:
+            event_stream: (botocore.eventstream.EventStream): Event Stream object to be iterated.
+        """
+        super().__init__(event_stream)
+        self.byte_iterator = iter(event_stream)
+
+    def __iter__(self):
+        """Returns an iterator object itself, which allows the object to be iterated.
+
+        Returns:
+            iter : object
+                    An iterator object representing the iterable.
+        """
+        return self
+
+    def __next__(self):
+        """Returns the next chunk of Byte directly."""
+        # Even with "while True" loop the function still behaves like a generator
+        # and sends the next new byte chunk.
+        while True:
+            chunk = next(self.byte_iterator)
+            if "PayloadPart" not in chunk:
+                # handle API response errors and force terminate.
+                handle_stream_errors(chunk)
+                # print and move on to next response byte
+                print("Unknown event type:" + chunk)
+                continue
+            return chunk["PayloadPart"]["Bytes"]
+
+
+class LineIterator(BaseIterator):
+    """A helper class for parsing the byte Event Stream input to provide Line iteration."""
+
+    def __init__(self, event_stream):
+        """Initialises a LineIterator Iterator object
+
+        Args:
+            event_stream: (botocore.eventstream.EventStream): Event Stream object to be iterated.
+        """
+        super().__init__(event_stream)
+        self.byte_iterator = iter(self.event_stream)
+        self.buffer = io.BytesIO()
+        self.read_pos = 0
+
+    def __iter__(self):
+        """Returns an iterator object itself, which allows the object to be iterated.
+
+        Returns:
+            iter : object
+                    An iterator object representing the iterable.
+        """
+        return self
+
+    def __next__(self):
+        r"""Returns the next Line for an Line iterable.
+
+        The output of the event stream will be in the following format:
+
+        ```
+        b'{"outputs": [" a"]}\n'
+        b'{"outputs": [" challenging"]}\n'
+        b'{"outputs": [" problem"]}\n'
+        ...
+        ```
+
+        While usually each PayloadPart event from the event stream will contain a byte array
+        with a full json, this is not guaranteed and some of the json objects may be split across
+        PayloadPart events. For example:
+        ```
+        {'PayloadPart': {'Bytes': b'{"outputs": '}}
+        {'PayloadPart': {'Bytes': b'[" problem"]}\n'}}
+        ```
+
+        This class accounts for this by concatenating bytes written via the 'write' function
+        and then exposing a method which will return lines (ending with a '\n' character) within
+        the buffer via the 'scan_lines' function. It maintains the position of the last read
+        position to ensure that previous bytes are not exposed again.
+
+        Returns:
+            str: Read and return one line from the event stream.
+        """
+        # Even with "while True" loop the function still behaves like a generator
+        # and sends the next new concatenated line
+        while True:
+            self.buffer.seek(self.read_pos)
+            line = self.buffer.readline()
+            if line and line[-1] == ord("\n"):
+                self.read_pos += len(line)
+                return line[:-1]
+            try:
+                chunk = next(self.byte_iterator)
+            except StopIteration:
+                if self.read_pos < self.buffer.getbuffer().nbytes:
+                    continue
+                raise
+            if "PayloadPart" not in chunk:
+                # handle API response errors and force terminate.
+                handle_stream_errors(chunk)
+                # print and move on to next response byte
+                print("Unknown event type:" + chunk)
+                continue
+            self.buffer.seek(0, io.SEEK_END)
+            self.buffer.write(chunk["PayloadPart"]["Bytes"])

src/sagemaker/jumpstart/cache.py

@@ -65,7 +65,9 @@ def __init__(
         self,
         region: Optional[str] = None,
         max_s3_cache_items: int = JUMPSTART_DEFAULT_MAX_S3_CACHE_ITEMS,
-        s3_cache_expiration_horizon: datetime.timedelta = JUMPSTART_DEFAULT_S3_CACHE_EXPIRATION_HORIZON,
+        s3_cache_expiration_horizon: datetime.timedelta = (
+            JUMPSTART_DEFAULT_S3_CACHE_EXPIRATION_HORIZON
+        ),
         max_semantic_version_cache_items: int = JUMPSTART_DEFAULT_MAX_SEMANTIC_VERSION_CACHE_ITEMS,
         semantic_version_cache_expiration_horizon: datetime.timedelta = (
             JUMPSTART_DEFAULT_SEMANTIC_VERSION_CACHE_EXPIRATION_HORIZON