Update 03_manual_sagemaker_process_train.ipynb

Author: eitansela

labs/03_manual_sagemaker_process_train/03_manual_sagemaker_process_train.ipynb

@@ -110,7 +110,7 @@
     "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",
     "\n",
-    "You can browse [this GitHub repository](https://github.com/aws-samples/amazon-sagemaker-local-mode) which contains examples and related resources showing you how to preprocess, train, debug your training script with breakpoints, and serve on your local machine using Amazon SageMaker Local mode for processing jobs, training and serving."
+    "In this workshop we will not use SageMaker Local mode, as we have processing, training and evaluation scripts ready. You can browse [this GitHub repository](https://github.com/aws-samples/amazon-sagemaker-local-mode) which contains examples and related resources showing you how to preprocess, train, debug your training script with breakpoints, and serve on your local machine using Amazon SageMaker Local mode for processing jobs, training and serving."
    ]
   },
   {