Update Training-models-in-SageMaker-notebooks.md

episodes/Training-models-in-SageMaker-notebooks.md

@@ -111,7 +111,7 @@ If you didn't complete the earlier episodes, you'll need to clone our code repo
 !pwd
 ```
 
-    /home/ec2-user/SageMaker/test_AWS
+    /home/ec2-user/SageMaker/
 
 
 If not, change directory using `%cd `.
@@ -128,8 +128,6 @@ If not, change directory using `%cd `.
 !git clone https://github.com/username/AWS_helpers.git
 ```
 
-    fatal: destination path 'test_AWS' already exists and is not an empty directory.
-
 
 ## Testing train.py on this notebook's instance
 In this next section, we will learn how to take a model training script, and deploy it to more powerful instances (or many instances). This is helpful for machine learning jobs that require extra power, GPUs, or benefit from parallelization. Before we try exploiting this extra power, it is essential that we test our code thoroughly. We don't want to waste unnecessary compute cycles and resources on jobs that produce bugs instead of insights. If you need to, you can use a subset of your data to run quicker tests. You can also select a slightly better instance resource if your current instance insn't meeting your needs. See the [Instances for ML spreadsheet](https://docs.google.com/spreadsheets/d/1uPT4ZAYl_onIl7zIjv5oEAdwy4Hdn6eiA9wVfOBbHmY/edit?usp=sharing) for guidance.