From 9b4d32faa0a383cccfc6270bfcc7f42f75920776 Mon Sep 17 00:00:00 2001 From: Chris Endemann Date: Wed, 6 Nov 2024 18:54:45 -0600 Subject: [PATCH] Update Training-models-in-SageMaker-notebooks.md --- episodes/Training-models-in-SageMaker-notebooks.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/episodes/Training-models-in-SageMaker-notebooks.md b/episodes/Training-models-in-SageMaker-notebooks.md index 225e653..3d4e0f1 100644 --- a/episodes/Training-models-in-SageMaker-notebooks.md +++ b/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.