breaking: rename s3_input to TrainingInput

bin/sagemaker-submit

@@ -56,4 +56,4 @@ if __name__ == '__main__':
                                            hyperparameters=hyperparameters,
                                            instance_count=args.instance_count,
                                            instance_type=args.instance_type)
-    estimator.fit(sagemaker.s3_input(args.data))
+    estimator.fit(sagemaker.TrainingInput(args.data))

doc/frameworks/tensorflow/using_tf.rst

@@ -351,9 +351,9 @@ If your TFRecords are compressed, you can train on Gzipped TF Records by passing
 
 .. code:: python
 
-    from sagemaker.session import s3_input
+    from sagemaker.inputs import TrainingInput
 
-    train_s3_input = s3_input('s3://bucket/path/to/training/data', compression='Gzip')
+    train_s3_input = TrainingInput('s3://bucket/path/to/training/data', compression='Gzip')
     tf_estimator.fit(train_s3_input)
 
 

src/sagemaker/__init__.py

@@ -29,6 +29,7 @@
     FactorizationMachinesModel,
 )
 from sagemaker.amazon.factorization_machines import FactorizationMachinesPredictor  # noqa: F401
+from sagemaker.inputs import TrainingInput  # noqa: F401
 from sagemaker.amazon.ntm import NTM, NTMModel, NTMPredictor  # noqa: F401
 from sagemaker.amazon.randomcutforest import (  # noqa: F401
     RandomCutForest,
@@ -54,7 +55,6 @@
 from sagemaker.session import Session  # noqa: F401
 from sagemaker.session import container_def, pipeline_container_def  # noqa: F401
 from sagemaker.session import production_variant  # noqa: F401
-from sagemaker.session import s3_input  # noqa: F401
 from sagemaker.session import get_execution_role  # noqa: F401
 
 from sagemaker.automl.automl import AutoML, AutoMLJob, AutoMLInput  # noqa: F401

src/sagemaker/algorithm.py

@@ -87,7 +87,7 @@ def __init__(
                   the container via a Unix-named pipe.
 
                 This argument can be overriden on a per-channel basis using
-                ``sagemaker.session.s3_input.input_mode``.
+                ``sagemaker.inputs.TrainingInput.input_mode``.
 
             output_path (str): S3 location for saving the training result (model artifacts and
                 output files). If not specified, results are stored to a default bucket. If

src/sagemaker/amazon/amazon_estimator.py

@@ -23,9 +23,8 @@
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.common import write_numpy_to_dense_tensor
 from sagemaker.estimator import EstimatorBase, _TrainingJob
-from sagemaker.inputs import FileSystemInput
+from sagemaker.inputs import FileSystemInput, TrainingInput
 from sagemaker.model import NEO_IMAGE_ACCOUNT
-from sagemaker.session import s3_input
 from sagemaker.utils import sagemaker_timestamp, get_ecr_image_uri_prefix
 from sagemaker.xgboost.defaults import (
     XGBOOST_1P_VERSIONS,
@@ -341,8 +340,10 @@ def data_channel(self):
         return {self.channel: self.records_s3_input()}
 
     def records_s3_input(self):
-        """Return a s3_input to represent the training data"""
-        return s3_input(self.s3_data, distribution="ShardedByS3Key", s3_data_type=self.s3_data_type)
+        """Return a TrainingInput to represent the training data"""
+        return TrainingInput(
+            self.s3_data, distribution="ShardedByS3Key", s3_data_type=self.s3_data_type
+        )
 
 
 class FileSystemRecordSet(object):

src/sagemaker/automl/candidate_estimator.py

@@ -103,7 +103,7 @@ def fit(
         self.name = candidate_name or self.name
         running_jobs = {}
 
-        # convert inputs to s3_input format
+        # convert inputs to TrainingInput format
         if isinstance(inputs, string_types):
             if not inputs.startswith("s3://"):
                 inputs = self.sagemaker_session.upload_data(inputs, key_prefix="auto-ml-input-data")

src/sagemaker/estimator.py

@@ -40,6 +40,7 @@
     validate_source_dir,
     _region_supports_debugger,
 )
+from sagemaker.inputs import TrainingInput
 from sagemaker.job import _Job
 from sagemaker.local import LocalSession
 from sagemaker.model import Model, NEO_ALLOWED_FRAMEWORKS
@@ -53,7 +54,6 @@
 )
 from sagemaker.predictor import Predictor
 from sagemaker.session import Session
-from sagemaker.session import s3_input
 from sagemaker.transformer import Transformer
 from sagemaker.utils import base_from_name, base_name_from_image, name_from_base, get_config_value
 from sagemaker import vpc_utils
@@ -127,7 +127,7 @@ def __init__(
                 'Pipe' - Amazon SageMaker streams data directly from S3 to the
                 container via a Unix-named pipe. This argument can be overriden
                 on a per-channel basis using
-                ``sagemaker.session.s3_input.input_mode``.
+                ``sagemaker.inputs.TrainingInput.input_mode``.
             output_path (str): S3 location for saving the training result (model
                 artifacts and output files). If not specified, results are
                 stored to a default bucket. If the bucket with the specific name
@@ -472,17 +472,18 @@ def fit(self, inputs=None, wait=True, logs="All", job_name=None, experiment_conf
         model using the Amazon SageMaker hosting services.
 
         Args:
-            inputs (str or dict or sagemaker.session.s3_input): Information
+            inputs (str or dict or sagemaker.inputs.TrainingInput): Information
                 about the training data. This can be one of three types:
 
                 * (str) the S3 location where training data is saved, or a file:// path in
                     local mode.
-                * (dict[str, str] or dict[str, sagemaker.session.s3_input]) If using multiple
+                * (dict[str, str] or dict[str, sagemaker.inputs.TrainingInput]) If using multiple
                     channels for training data, you can specify a dict mapping channel names to
-                    strings or :func:`~sagemaker.session.s3_input` objects.
-                * (sagemaker.session.s3_input) - channel configuration for S3 data sources that can
-                    provide additional information as well as the path to the training dataset.
-                    See :func:`sagemaker.session.s3_input` for full details.
+                    strings or :func:`~sagemaker.inputs.TrainingInput` objects.
+                * (sagemaker.inputs.TrainingInput) - channel configuration for S3 data sources
+                    that can provide additional information as well as the path to the training
+                    dataset.
+                    See :func:`sagemaker.inputs.TrainingInput` for full details.
                 * (sagemaker.session.FileSystemInput) - channel configuration for
                     a file system data source that can provide additional information as well as
                     the path to the training dataset.
@@ -1020,10 +1021,10 @@ def start_new(cls, estimator, inputs, experiment_config):
         train_args["metric_definitions"] = estimator.metric_definitions
         train_args["experiment_config"] = experiment_config
 
-        if isinstance(inputs, s3_input):
+        if isinstance(inputs, TrainingInput):
             if "InputMode" in inputs.config:
                 logging.debug(
-                    "Selecting s3_input's input_mode (%s) for TrainingInputMode.",
+                    "Selecting TrainingInput's input_mode (%s) for TrainingInputMode.",
                     inputs.config["InputMode"],
                 )
                 train_args["input_mode"] = inputs.config["InputMode"]
@@ -1191,7 +1192,7 @@ def __init__(
                   container via a Unix-named pipe.
 
                 This argument can be overriden on a per-channel basis using
-                ``sagemaker.session.s3_input.input_mode``.
+                ``sagemaker.inputs.TrainingInput.input_mode``.
             output_path (str): S3 location for saving the training result (model
                 artifacts and output files). If not specified, results are
                 stored to a default bucket. If the bucket with the specific name
@@ -2028,7 +2029,7 @@ def _s3_uri_prefix(channel_name, s3_data):
         channel_name:
         s3_data:
     """
-    if isinstance(s3_data, s3_input):
+    if isinstance(s3_data, TrainingInput):
         s3_uri = s3_data.config["DataSource"]["S3DataSource"]["S3Uri"]
     else:
         s3_uri = s3_data
@@ -2038,7 +2039,7 @@ def _s3_uri_prefix(channel_name, s3_data):
 
 
 # E.g. 's3://bucket/data' would return 'bucket/data'.
-# Also accepts other valid input types, e.g. dict and s3_input.
+# Also accepts other valid input types, e.g. dict and TrainingInput.
 def _s3_uri_without_prefix_from_input(input_data):
     # Unpack an input_config object from a dict if a dict was passed in.
     """
@@ -2052,8 +2053,10 @@ def _s3_uri_without_prefix_from_input(input_data):
         return response
     if isinstance(input_data, str):
         return _s3_uri_prefix("training", input_data)
-    if isinstance(input_data, s3_input):
+    if isinstance(input_data, TrainingInput):
         return _s3_uri_prefix("training", input_data)
     raise ValueError(
-        "Unrecognized type for S3 input data config - not str or s3_input: {}".format(input_data)
+        "Unrecognized type for S3 input data config - not str or TrainingInput: {}".format(
+            input_data
+        )
     )

src/sagemaker/inputs.py

@@ -13,15 +13,11 @@
 """Amazon SageMaker channel configurations for S3 data sources and file system data sources"""
 from __future__ import absolute_import, print_function
 
-import logging
-
 FILE_SYSTEM_TYPES = ["FSxLustre", "EFS"]
 FILE_SYSTEM_ACCESS_MODES = ["ro", "rw"]
 
-logger = logging.getLogger("sagemaker")
-
 
-class s3_input(object):
+class TrainingInput(object):
     """Amazon SageMaker channel configurations for S3 data sources.
 
     Attributes:
@@ -80,10 +76,6 @@ def __init__(
                 this channel. See the SageMaker API documentation for more info:
                 https://docs.aws.amazon.com/sagemaker/latest/dg/API_ShuffleConfig.html
         """
-        logger.warning(
-            "'s3_input' class will be renamed to 'TrainingInput' in SageMaker Python SDK v2."
-        )
-
         self.config = {
             "DataSource": {"S3DataSource": {"S3DataType": s3_data_type, "S3Uri": s3_data}}
         }

src/sagemaker/job.py

@@ -16,9 +16,8 @@
 from abc import abstractmethod
 from six import string_types
 
-from sagemaker.inputs import FileSystemInput
+from sagemaker.inputs import FileSystemInput, TrainingInput
 from sagemaker.local import file_input
-from sagemaker.session import s3_input
 
 
 class _Job(object):
@@ -142,7 +141,7 @@ def _format_inputs_to_input_config(inputs, validate_uri=True):
         input_dict = {}
         if isinstance(inputs, string_types):
             input_dict["training"] = _Job._format_string_uri_input(inputs, validate_uri)
-        elif isinstance(inputs, s3_input):
+        elif isinstance(inputs, TrainingInput):
             input_dict["training"] = inputs
         elif isinstance(inputs, file_input):
             input_dict["training"] = inputs
@@ -154,7 +153,10 @@ def _format_inputs_to_input_config(inputs, validate_uri=True):
         elif isinstance(inputs, FileSystemInput):
             input_dict["training"] = inputs
         else:
-            msg = "Cannot format input {}. Expecting one of str, dict, s3_input or FileSystemInput"
+            msg = (
+                "Cannot format input {}. Expecting one of str, dict, TrainingInput or "
+                "FileSystemInput"
+            )
             raise ValueError(msg.format(inputs))
 
         channels = [
@@ -193,7 +195,7 @@ def _format_string_uri_input(
             target_attribute_name:
         """
         if isinstance(uri_input, str) and validate_uri and uri_input.startswith("s3://"):
-            s3_input_result = s3_input(
+            s3_input_result = TrainingInput(
                 uri_input,
                 content_type=content_type,
                 input_mode=input_mode,
@@ -209,19 +211,19 @@ def _format_string_uri_input(
                 '"file://"'.format(uri_input)
             )
         if isinstance(uri_input, str):
-            s3_input_result = s3_input(
+            s3_input_result = TrainingInput(
                 uri_input,
                 content_type=content_type,
                 input_mode=input_mode,
                 compression=compression,
                 target_attribute_name=target_attribute_name,
             )
             return s3_input_result
-        if isinstance(uri_input, (s3_input, file_input, FileSystemInput)):
+        if isinstance(uri_input, (TrainingInput, file_input, FileSystemInput)):
             return uri_input
 
         raise ValueError(
-            "Cannot format input {}. Expecting one of str, s3_input, file_input or "
+            "Cannot format input {}. Expecting one of str, TrainingInput, file_input or "
             "FileSystemInput".format(uri_input)
         )
 
@@ -270,7 +272,7 @@ def _format_model_uri_input(model_uri, validate_uri=True):
             validate_uri:
         """
         if isinstance(model_uri, string_types) and validate_uri and model_uri.startswith("s3://"):
-            return s3_input(
+            return TrainingInput(
                 model_uri,
                 input_mode="File",
                 distribution="FullyReplicated",
@@ -283,7 +285,7 @@ def _format_model_uri_input(model_uri, validate_uri=True):
                 'Model URI must be a valid S3 or FILE URI: must start with "s3://" or ' '"file://'
             )
         if isinstance(model_uri, string_types):
-            return s3_input(
+            return TrainingInput(
                 model_uri,
                 input_mode="File",
                 distribution="FullyReplicated",

src/sagemaker/session.py

@@ -29,8 +29,6 @@
 import sagemaker.logs
 from sagemaker import vpc_utils
 
-# import s3_input for backward compatibility
-from sagemaker.inputs import s3_input  # noqa # pylint: disable=unused-import
 from sagemaker.user_agent import prepend_user_agent
 from sagemaker.utils import (
     name_from_image,

src/sagemaker/tuner.py

@@ -28,6 +28,7 @@
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.analytics import HyperparameterTuningJobAnalytics
 from sagemaker.estimator import Framework
+from sagemaker.inputs import TrainingInput
 from sagemaker.job import _Job
 from sagemaker.parameter import (
     CategoricalParameter,
@@ -36,7 +37,6 @@
     ParameterRange,
 )
 from sagemaker.session import Session
-from sagemaker.session import s3_input
 from sagemaker.utils import base_from_name, base_name_from_image, name_from_base
 
 AMAZON_ESTIMATOR_MODULE = "sagemaker"
@@ -377,13 +377,13 @@ def fit(
                 any of the following forms:
 
                 * (str) - The S3 location where training data is saved.
-                * (dict[str, str] or dict[str, sagemaker.session.s3_input]) -
+                * (dict[str, str] or dict[str, sagemaker.inputs.TrainingInput]) -
                     If using multiple channels for training data, you can specify
                     a dict mapping channel names to strings or
-                    :func:`~sagemaker.session.s3_input` objects.
-                * (sagemaker.session.s3_input) - Channel configuration for S3 data sources that can
-                    provide additional information about the training dataset.
-                    See :func:`sagemaker.session.s3_input` for full details.
+                    :func:`~sagemaker.inputs.TrainingInput` objects.
+                * (sagemaker.inputs.TrainingInput) - Channel configuration for S3 data sources
+                    that can provide additional information about the training dataset.
+                    See :func:`sagemaker.inputs.TrainingInput` for full details.
                 * (sagemaker.session.FileSystemInput) - channel configuration for
                     a file system data source that can provide additional information as well as
                     the path to the training dataset.
@@ -1500,10 +1500,10 @@ def _prepare_training_config(
         training_config["input_mode"] = estimator.input_mode
         training_config["metric_definitions"] = metric_definitions
 
-        if isinstance(inputs, s3_input):
+        if isinstance(inputs, TrainingInput):
             if "InputMode" in inputs.config:
                 logging.debug(
-                    "Selecting s3_input's input_mode (%s) for TrainingInputMode.",
+                    "Selecting TrainingInput's input_mode (%s) for TrainingInputMode.",
                     inputs.config["InputMode"],
                 )
                 training_config["input_mode"] = inputs.config["InputMode"]

src/sagemaker/workflow/airflow.py

@@ -115,13 +115,13 @@ def training_base_config(estimator, inputs=None, job_name=None, mini_batch_size=
 
             * (str) - The S3 location where training data is saved.
 
-            * (dict[str, str] or dict[str, sagemaker.session.s3_input]) - If using multiple
+            * (dict[str, str] or dict[str, sagemaker.inputs.TrainingInput]) - If using multiple
                   channels for training data, you can specify a dict mapping channel names to
-                  strings or :func:`~sagemaker.session.s3_input` objects.
+                  strings or :func:`~sagemaker.inputs.TrainingInput` objects.
 
-            * (sagemaker.session.s3_input) - Channel configuration for S3 data sources that can
+            * (sagemaker.inputs.TrainingInput) - Channel configuration for S3 data sources that can
                   provide additional information about the training dataset. See
-                  :func:`sagemaker.session.s3_input` for full details.
+                  :func:`sagemaker.inputs.TrainingInput` for full details.
 
             * (sagemaker.amazon.amazon_estimator.RecordSet) - A collection of
                   Amazon :class:~`Record` objects serialized and stored in S3.
@@ -208,13 +208,13 @@ def training_config(estimator, inputs=None, job_name=None, mini_batch_size=None)
             method of the associated estimator, as this can take any of the following forms:
             * (str) - The S3 location where training data is saved.
 
-            * (dict[str, str] or dict[str, sagemaker.session.s3_input]) - If using multiple
+            * (dict[str, str] or dict[str, sagemaker.inputs.TrainingInput]) - If using multiple
                   channels for training data, you can specify a dict mapping channel names to
-                  strings or :func:`~sagemaker.session.s3_input` objects.
+                  strings or :func:`~sagemaker.inputs.TrainingInput` objects.
 
-            * (sagemaker.session.s3_input) - Channel configuration for S3 data sources that can
+            * (sagemaker.inputs.TrainingInput) - Channel configuration for S3 data sources that can
                   provide additional information about the training dataset. See
-                  :func:`sagemaker.session.s3_input` for full details.
+                  :func:`sagemaker.inputs.TrainingInput` for full details.
 
             * (sagemaker.amazon.amazon_estimator.RecordSet) - A collection of
                   Amazon :class:~`Record` objects serialized and stored in S3.
@@ -258,13 +258,13 @@ def tuning_config(tuner, inputs, job_name=None, include_cls_metadata=False, mini
 
             * (str) - The S3 location where training data is saved.
 
-            * (dict[str, str] or dict[str, sagemaker.session.s3_input]) - If using multiple
+            * (dict[str, str] or dict[str, sagemaker.inputs.TrainingInput]) - If using multiple
                   channels for training data, you can specify a dict mapping channel names to
-                  strings or :func:`~sagemaker.session.s3_input` objects.
+                  strings or :func:`~sagemaker.inputs.TrainingInput` objects.
 
-            * (sagemaker.session.s3_input) - Channel configuration for S3 data sources that can
+            * (sagemaker.inputs.TrainingInput) - Channel configuration for S3 data sources that can
                   provide additional information about the training dataset. See
-                  :func:`sagemaker.session.s3_input` for full details.
+                  :func:`sagemaker.inputs.TrainingInput` for full details.
 
             * (sagemaker.amazon.amazon_estimator.RecordSet) - A collection of
                   Amazon :class:~`Record` objects serialized and stored in S3.