Merge branch 'master' into training-step-anno-fix

Author: icywang86rui

src/sagemaker/inputs.py

@@ -70,6 +70,8 @@ def __init__(
                         a local directory.
                     * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via
                         a Unix-named pipe.
+                    * 'FastFile' - Amazon SageMaker streams data from S3 on demand instead of
+                        downloading the entire dataset before training begins.
 
             attribute_names (list[str]): A list of one or more attribute names to use that are
                 found in a specified AugmentedManifestFile.

src/sagemaker/session.py

@@ -467,6 +467,8 @@ def train(  # noqa: C901
                 a directory in the Docker container.
                 * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a
                 Unix-named pipe.
+                * 'FastFile' - Amazon SageMaker streams data from S3 on demand instead of
+                downloading the entire dataset before training begins.
             input_config (list): A list of Channel objects. Each channel is a named input source.
                 Please refer to the format details described:
                 https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_training_job
@@ -609,6 +611,8 @@ def _get_train_request(  # noqa: C901
                 a directory in the Docker container.
                 * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a
                 Unix-named pipe.
+                * 'FastFile' - Amazon SageMaker streams data from S3 on demand instead of
+                downloading the entire dataset before training begins.
             input_config (list): A list of Channel objects. Each channel is a named input source.
                 Please refer to the format details described:
                 https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_training_job
@@ -1897,6 +1901,8 @@ def tune(  # noqa: C901
                 a directory in the Docker container.
                 * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a
                 Unix-named pipe.
+                * 'FastFile' - Amazon SageMaker streams data from S3 on demand instead of
+                downloading the entire dataset before training begins.
             metric_definitions (list[dict]): A list of dictionaries that defines the metric(s)
                 used to evaluate the training jobs. Each dictionary contains two keys: 'Name' for
                 the name of the metric, and 'Regex' for the regular expression used to extract the
@@ -2180,6 +2186,8 @@ def _map_training_config(
                     a directory in the Docker container.
                 * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a
                     Unix-named pipe.
+                * 'FastFile' - Amazon SageMaker streams data from S3 on demand instead of
+                    downloading the entire dataset before training begins.
             role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training
                 jobs and APIs that create Amazon SageMaker endpoints use this role to access
                 training data and model artifacts. You must grant sufficient permissions to