Update 03_manual_sagemaker_process_train.ipynb

Author: eitansela

labs/03_manual_sagemaker_process_train/03_manual_sagemaker_process_train.ipynb

@@ -105,7 +105,7 @@
     "Next, simply supply an ordinary Python data preprocessing script as shown below.  For this example, we're using a SageMaker prebuilt Scikit-learn framework container, which includes many common functions for processing data.  There are few limitations on what kinds of code and operations you can run, and only a minimal API contract:  input and output data must be placed in specified directories.  If this is done, SageMaker Processing automatically loads the input data from S3 and uploads transformed data back to S3 when the job is complete.\n",
     "\n",
     "## SageMaker Local Mode\n",
-    "To help you perform the code changes required to take your script and adapt it to SageMaker, you can use the [SageMaker local mode](https://aws.amazon.com/blogs/machine-learning/use-the-amazon-sagemaker-local-mode-to-train-on-your-notebook-instance/) to process, train or inference on your local PC.\n",
+    "To help you perform the code changes required to take your script and adapt it to SageMaker, you can use the [SageMaker local mode](https://aws.amazon.com/blogs/machine-learning/use-the-amazon-sagemaker-local-mode-to-train-on-your-notebook-instance/) to process, train or inference on your local machine.\n",
     "\n",
     "The local mode in the Amazon SageMaker Python SDK can emulate CPU (single and multi-instance) and GPU (single instance) SageMaker training jobs by changing a single argument in the TensorFlow, PyTorch or MXNet estimators. To do this, it uses Docker compose and NVIDIA Docker. It will also pull the Amazon SageMaker TensorFlow, PyTorch or MXNet containers from Amazon ECS, so you’ll need to be able to access a public Amazon ECR repository from your local environment.\n",
     "\n",
@@ -731,4 +731,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}