Merge branch 'main' into support-placeholders-for-map-state

Author: ca-nguyen

src/stepfunctions/steps/sagemaker.py

@@ -74,11 +74,13 @@ def __init__(self, state_id, estimator, job_name, data=None, hyperparameters=Non
                     If there are duplicate entries, the value provided through this property will be used. (Default: Hyperparameters specified in the estimator.)
                 * (Placeholder, optional) - The TrainingStep will use the hyperparameters specified by the Placeholder's value instead of the hyperparameters specified in the estimator.
             mini_batch_size (int): Specify this argument only when estimator is a built-in estimator of an Amazon algorithm. For other estimators, batch size should be specified in the estimator.
-            experiment_config (dict, optional): Specify the experiment config for the training. (Default: None)
+            experiment_config (dict or Placeholder, optional): Specify the experiment config for the training. (Default: None)
             wait_for_completion (bool, optional): Boolean value set to `True` if the Task state should wait for the training job to complete before proceeding to the next step in the workflow. Set to `False` if the Task state should submit the training job and proceed to the next step. (default: True)
-            tags (list[dict], optional): `List to tags <https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html>`_ to associate with the resource.
+            tags (list[dict] or Placeholder, optional): `List of tags <https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html>`_ to associate with the resource.
             output_data_config_path (str or Placeholder, optional): S3 location for saving the training result (model
                 artifacts and output files). If specified, it overrides the `output_path` property of `estimator`.
+            parameters(dict, optional): The value of this field is merged with other arguments to become the request payload for SageMaker `CreateTrainingJob<https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html>`_. (Default: None)
+                You can use `parameters` to override the value provided by other arguments and specify any field's value dynamically using `Placeholders<https://aws-step-functions-data-science-sdk.readthedocs.io/en/stable/placeholders.html?highlight=placeholder#stepfunctions.inputs.Placeholder>`_.
         """
         self.estimator = estimator
         self.job_name = job_name
@@ -105,44 +107,48 @@ def __init__(self, state_id, estimator, job_name, data=None, hyperparameters=Non
             data = data.to_jsonpath()
 
         if isinstance(job_name, str):
-            parameters = training_config(estimator=estimator, inputs=data, job_name=job_name, mini_batch_size=mini_batch_size)
+            training_parameters = training_config(estimator=estimator, inputs=data, job_name=job_name, mini_batch_size=mini_batch_size)
         else:
-            parameters = training_config(estimator=estimator, inputs=data, mini_batch_size=mini_batch_size)
+            training_parameters = training_config(estimator=estimator, inputs=data, mini_batch_size=mini_batch_size)
 
         if estimator.debugger_hook_config != None and estimator.debugger_hook_config is not False:
-            parameters['DebugHookConfig'] = estimator.debugger_hook_config._to_request_dict()
+            training_parameters['DebugHookConfig'] = estimator.debugger_hook_config._to_request_dict()
 
         if estimator.rules != None:
-            parameters['DebugRuleConfigurations'] = [rule.to_debugger_rule_config_dict() for rule in estimator.rules]
+            training_parameters['DebugRuleConfigurations'] = [rule.to_debugger_rule_config_dict() for rule in estimator.rules]
 
         if isinstance(job_name, Placeholder):
-            parameters['TrainingJobName'] = job_name
+            training_parameters['TrainingJobName'] = job_name
 
         if output_data_config_path is not None:
-            parameters['OutputDataConfig']['S3OutputPath'] = output_data_config_path
+            training_parameters['OutputDataConfig']['S3OutputPath'] = output_data_config_path
 
         if data is not None and is_data_placeholder:
             # Replace the 'S3Uri' key with one that supports JSONpath value.
             # Support for uri str only: The list will only contain 1 element
-            data_uri = parameters['InputDataConfig'][0]['DataSource']['S3DataSource'].pop('S3Uri', None)
-            parameters['InputDataConfig'][0]['DataSource']['S3DataSource']['S3Uri.$'] = data_uri
+            data_uri = training_parameters['InputDataConfig'][0]['DataSource']['S3DataSource'].pop('S3Uri', None)
+            training_parameters['InputDataConfig'][0]['DataSource']['S3DataSource']['S3Uri.$'] = data_uri
 
         if hyperparameters is not None:
             if not isinstance(hyperparameters, Placeholder):
                 if estimator.hyperparameters() is not None:
                     hyperparameters = self.__merge_hyperparameters(hyperparameters, estimator.hyperparameters())
-            parameters['HyperParameters'] = hyperparameters
+            training_parameters['HyperParameters'] = hyperparameters
 
         if experiment_config is not None:
-            parameters['ExperimentConfig'] = experiment_config
+            training_parameters['ExperimentConfig'] = experiment_config
 
-        if 'S3Operations' in parameters:
-            del parameters['S3Operations']
+        if 'S3Operations' in training_parameters:
+            del training_parameters['S3Operations']
 
         if tags:
-            parameters['Tags'] = tags_dict_to_kv_list(tags)
+            training_parameters['Tags'] = tags if isinstance(tags, Placeholder) else tags_dict_to_kv_list(tags)
 
-        kwargs[Field.Parameters.value] = parameters
+        if Field.Parameters.value in kwargs and isinstance(kwargs[Field.Parameters.value], dict):
+            # Update training parameters with input parameters
+            merge_dicts(training_parameters, kwargs[Field.Parameters.value])
+
+        kwargs[Field.Parameters.value] = training_parameters
         super(TrainingStep, self).__init__(state_id, **kwargs)
 
     def get_expected_model(self, model_name=None):
@@ -214,7 +220,7 @@ def __init__(self, state_id, transformer, job_name, model_name, data, data_type=
             split_type (str or Placeholder): The record delimiter for the input object (default: 'None'). Valid values: 'None', 'Line', 'RecordIO', and 'TFRecord'.
             experiment_config (dict or Placeholder, optional): Specify the experiment config for the transform. (Default: None)
             wait_for_completion(bool, optional): Boolean value set to `True` if the Task state should wait for the transform job to complete before proceeding to the next step in the workflow. Set to `False` if the Task state should submit the transform job and proceed to the next step. (default: True)
-            tags (list[dict] or Placeholder, optional): `List to tags <https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html>`_ to associate with the resource.
+            tags (list[dict] or Placeholder, optional): `List of tags <https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html>`_ to associate with the resource.
             input_filter (str or Placeholder): A JSONPath to select a portion of the input to pass to the algorithm container for inference. If you omit the field, it gets the value ‘$’, representing the entire input. For CSV data, each row is taken as a JSON array, so only index-based JSONPaths can be applied, e.g. $[0], $[1:]. CSV data should follow the RFC format. See Supported JSONPath Operators for a table of supported JSONPath operators. For more information, see the SageMaker API documentation for CreateTransformJob. Some examples: “$[1:]”, “$.features” (default: None).
             output_filter (str or Placeholder): A JSONPath to select a portion of the joined/original output to return as the output. For more information, see the SageMaker API documentation for CreateTransformJob. Some examples: “$[1:]”, “$.prediction” (default: None).
             join_source (str or Placeholder): The source of data to be joined to the transform output. It can be set to ‘Input’ meaning the entire input record will be joined to the inference result. You can use OutputFilter to select the useful portion before uploading to S3. (default: None). Valid values: Input, None.
@@ -296,14 +302,16 @@ def __init__(self, state_id, model, model_name=None, instance_type=None, tags=No
             model (sagemaker.model.Model): The SageMaker model to use in the ModelStep. If :py:class:`TrainingStep` was used to train the model and saving the model is the next step in the workflow, the output of :py:func:`TrainingStep.get_expected_model()` can be passed here.
             model_name (str or Placeholder, optional): Specify a model name, this is required for creating the model. We recommend to use :py:class:`~stepfunctions.inputs.ExecutionInput` placeholder collection to pass the value dynamically in each execution.
             instance_type (str, optional): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'.
-            tags (list[dict], optional): `List to tags <https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html>`_ to associate with the resource.
+            tags (list[dict] or Placeholders, optional): `List of tags <https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html>`_ to associate with the resource.
+            parameters(dict, optional): The value of this field is merged with other arguments to become the request payload for SageMaker `CreateModel<https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModel.html>`_. (Default: None)
+                You can use `parameters` to override the value provided by other arguments and specify any field's value dynamically using `Placeholders<https://aws-step-functions-data-science-sdk.readthedocs.io/en/stable/placeholders.html?highlight=placeholder#stepfunctions.inputs.Placeholder>`_.
         """
         if isinstance(model, FrameworkModel):
-            parameters = model_config(model=model, instance_type=instance_type, role=model.role, image_uri=model.image_uri)
+            model_parameters = model_config(model=model, instance_type=instance_type, role=model.role, image_uri=model.image_uri)
             if model_name:
-                parameters['ModelName'] = model_name
+                model_parameters['ModelName'] = model_name
         elif isinstance(model, Model):
-            parameters = {
+            model_parameters = {
                 'ExecutionRoleArn': model.role,
                 'ModelName': model_name or model.name,
                 'PrimaryContainer': {
@@ -315,13 +323,17 @@ def __init__(self, state_id, model, model_name=None, instance_type=None, tags=No
         else:
             raise ValueError("Expected 'model' parameter to be of type 'sagemaker.model.Model', but received type '{}'".format(type(model).__name__))
 
-        if 'S3Operations' in parameters:
-            del parameters['S3Operations']
+        if 'S3Operations' in model_parameters:
+            del model_parameters['S3Operations']
 
         if tags:
-            parameters['Tags'] = tags_dict_to_kv_list(tags)
+            model_parameters['Tags'] = tags if isinstance(tags, Placeholder) else tags_dict_to_kv_list(tags)
 
-        kwargs[Field.Parameters.value] = parameters
+        if Field.Parameters.value in kwargs and isinstance(kwargs[Field.Parameters.value], dict):
+            # Update model parameters with input parameters
+            merge_dicts(model_parameters, kwargs[Field.Parameters.value])
+
+        kwargs[Field.Parameters.value] = model_parameters
 
         """
         Example resource arn: arn:aws:states:::sagemaker:createModel
@@ -351,7 +363,7 @@ def __init__(self, state_id, endpoint_config_name, model_name, initial_instance_
             data_capture_config (sagemaker.model_monitor.DataCaptureConfig, optional): Specifies
                 configuration related to Endpoint data capture for use with
                 Amazon SageMaker Model Monitoring. Default: None.
-            tags (list[dict], optional): `List to tags <https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html>`_ to associate with the resource.
+            tags (list[dict], optional): `List of tags <https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html>`_ to associate with the resource.
         """
         parameters = {
             'EndpointConfigName': endpoint_config_name,
@@ -393,9 +405,8 @@ def __init__(self, state_id, endpoint_name, endpoint_config_name, tags=None, upd
             state_id (str): State name whose length **must be** less than or equal to 128 unicode characters. State names **must be** unique within the scope of the whole state machine.
             endpoint_name (str or Placeholder): The name of the endpoint to create. We recommend to use :py:class:`~stepfunctions.inputs.ExecutionInput` placeholder collection to pass the value dynamically in each execution.
             endpoint_config_name (str or Placeholder): The name of the endpoint configuration to use for the endpoint. We recommend to use :py:class:`~stepfunctions.inputs.ExecutionInput` placeholder collection to pass the value dynamically in each execution.
-            tags (list[dict], optional): `List to tags <https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html>`_ to associate with the resource.
             update (bool, optional): Boolean flag set to `True` if endpoint must to be updated. Set to `False` if new endpoint must be created. (default: False)
-            tags (list[dict], optional): `List to tags <https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html>`_ to associate with the resource.
+            tags (list[dict], optional): `List of tags <https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html>`_ to associate with the resource.
         """
 
         parameters = {
@@ -454,7 +465,7 @@ def __init__(self, state_id, tuner, job_name, data, wait_for_completion=True, ta
                     :class:`sagemaker.amazon.amazon_estimator.RecordSet` objects,
                     where each instance is a different channel of training data.
             wait_for_completion(bool, optional): Boolean value set to `True` if the Task state should wait for the tuning job to complete before proceeding to the next step in the workflow. Set to `False` if the Task state should submit the tuning job and proceed to the next step. (default: True)
-            tags (list[dict], optional): `List to tags <https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html>`_ to associate with the resource.
+            tags (list[dict], optional): `List of tags <https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html>`_ to associate with the resource.
         """
         if wait_for_completion:
             """
@@ -516,7 +527,7 @@ def __init__(self, state_id, processor, job_name, inputs=None, outputs=None, exp
                 ARN of a KMS key, alias of a KMS key, or alias of a KMS key.
                 The KmsKeyId is applied to all outputs.
             wait_for_completion (bool, optional): Boolean value set to `True` if the Task state should wait for the processing job to complete before proceeding to the next step in the workflow. Set to `False` if the Task state should submit the processing job and proceed to the next step. (default: True)
-            tags (list[dict] or Placeholder, optional): `List to tags <https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html>`_ to associate with the resource.
+            tags (list[dict] or Placeholder, optional): `List of tags <https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html>`_ to associate with the resource.
             parameters(dict, optional): The value of this field is merged with other arguments to become the request payload for SageMaker `CreateProcessingJob<https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateProcessingJob.html>`_. 
                 You can use `parameters` to override the value provided by other arguments and specify any field's value dynamically using `Placeholders<https://aws-step-functions-data-science-sdk.readthedocs.io/en/stable/placeholders.html?highlight=placeholder#stepfunctions.inputs.Placeholder>`_.