From a4fc20c117e3f3ec818e0f535822c986d75920ac Mon Sep 17 00:00:00 2001
From: Ben Johnston <6936084+doc-E-brown@users.noreply.github.com>
Date: Thu, 28 Mar 2019 11:46:27 +1100
Subject: [PATCH] documentation: spelling error correction

Corrected doc string spelling errors: trainig not training
---
 src/sagemaker/estimator.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/sagemaker/estimator.py b/src/sagemaker/estimator.py
index 116beac965..67df789936 100644
--- a/src/sagemaker/estimator.py
+++ b/src/sagemaker/estimator.py
@@ -73,7 +73,7 @@ def __init__(self, role, train_instance_count, train_instance_type,
                 'File' - Amazon SageMaker copies the training dataset from the S3 location to a local directory.
                 '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``.
-            output_path (str): S3 location for saving the trainig result (model artifacts and output files).
+            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
                 does not exist, the estimator creates the bucket during the
                 :meth:`~sagemaker.estimator.EstimatorBase.fit` method execution.
@@ -633,7 +633,7 @@ def __init__(self, image_name, role, train_instance_count, train_instance_type,
                 * '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``.
-            output_path (str): S3 location for saving the trainig result (model artifacts and output files).
+            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
                 does not exist, the estimator creates the bucket during the
                 :meth:`~sagemaker.estimator.EstimatorBase.fit` method execution.