update docstring

Author: Chuyang Deng

doc/frameworks/tensorflow/using_tf.rst

@@ -329,7 +329,7 @@ If your TFRecords are compressed, you can train on Gzipped TF Records by passing
 
 .. code:: python
 
-    from sagemaker.session import TrainingInput
+    from sagemaker.inputs import TrainingInput
 
     train_s3_input = TrainingInput('s3://bucket/path/to/training/data', compression='Gzip')
     tf_estimator.fit(train_s3_input)

src/sagemaker/algorithm.py

@@ -85,7 +85,7 @@ def __init__(
                   the container via a Unix-named pipe.
 
                 This argument can be overriden on a per-channel basis using
-                ``sagemaker.session.TrainingInput.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/estimator.py

@@ -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.TrainingInput.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
@@ -472,18 +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.TrainingInput): 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.TrainingInput]) 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.TrainingInput` objects.
-                * (sagemaker.session.TrainingInput) - channel configuration for S3 data sources
+                    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.session.TrainingInput` for full details.
+                    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.
@@ -1192,7 +1192,7 @@ def __init__(
                   container via a Unix-named pipe.
 
                 This argument can be overriden on a per-channel basis using
-                ``sagemaker.session.TrainingInput.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

src/sagemaker/tuner.py

@@ -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.TrainingInput]) -
+                * (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.TrainingInput` objects.
-                * (sagemaker.session.TrainingInput) - Channel configuration for S3 data sources
+                    :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.session.TrainingInput` for full details.
+                    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.

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.TrainingInput]) - 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.TrainingInput` objects.
+                  strings or :func:`~sagemaker.inputs.TrainingInput` objects.
 
-            * (sagemaker.session.TrainingInput) - 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.TrainingInput` 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.TrainingInput]) - 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.TrainingInput` objects.
+                  strings or :func:`~sagemaker.inputs.TrainingInput` objects.
 
-            * (sagemaker.session.TrainingInput) - 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.TrainingInput` 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.TrainingInput]) - 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.TrainingInput` objects.
+                  strings or :func:`~sagemaker.inputs.TrainingInput` objects.
 
-            * (sagemaker.session.TrainingInput) - 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.TrainingInput` 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.