Fix docs about s3_input usage (#258)

Author: laurenyu

src/sagemaker/estimator.py

@@ -165,8 +165,8 @@ def fit(self, inputs, wait=True, logs=True, job_name=None):
                     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.
+                    additional information as well as the path to the training dataset.
+                    See :func:`sagemaker.session.s3_input` for full details.
             wait (bool): Whether the call should wait until the job completes (default: True).
             logs (bool): Whether to show the logs produced by the job.
                 Only meaningful when wait is True (default: True).

src/sagemaker/session.py

@@ -890,7 +890,7 @@ def __init__(self, s3_data, distribution='FullyReplicated', compression=None,
             s3_data (str): Defines the location of s3 data to train on.
             distribution (str): Valid values: 'FullyReplicated', 'ShardedByS3Key'
                 (default: 'FullyReplicated').
-            compression (str): Valid values: 'Gzip', 'Bzip2', 'Lzop' (default: None).
+            compression (str): Valid values: 'Gzip', None (default: None). This is used only in Pipe input mode.
             content_type (str): MIME type of the input data (default: None).
             record_wrapping (str): Valid values: 'RecordIO' (default: None).
             s3_data_type (str): Value values: 'S3Prefix', 'ManifestFile'. If 'S3Prefix', ``s3_data`` defines

src/sagemaker/tensorflow/README.rst

@@ -482,6 +482,7 @@ Required argument
 
   - ``str``: An S3 URI, for example ``s3://my-bucket/my-training-data``, which indicates the dataset's location.
   - ``dict[str, str]``: A dictionary mapping channel names to S3 locations, for example ``{'train': 's3://my-bucket/my-training-data/train', 'test': 's3://my-bucket/my-training-data/test'}``
+  - ``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 `the API docs <https://sagemaker.readthedocs.io/en/latest/session.html#sagemaker.session.s3_input>`_ for full details.
 
 When the training job starts in SageMaker the container will download the dataset.
 Both ``train_input_fn`` and ``eval_input_fn`` functions have a parameter called ``training_dir`` which

src/sagemaker/tensorflow/estimator.py

@@ -216,8 +216,8 @@ def fit(self, inputs, wait=True, logs=True, job_name=None, run_tensorboard_local
                     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.
+                    additional information as well as the path to the training dataset.
+                    See :func:`sagemaker.session.s3_input` for full details.
             wait (bool): Whether the call should wait until the job completes (default: True).
             logs (bool): Whether to show the logs produced by the job.
                 Only meaningful when wait is True (default: True).