Skip to content

Latest commit

 

History

History
64 lines (36 loc) · 2.49 KB

Machine_Learning_Instructions.md

File metadata and controls

64 lines (36 loc) · 2.49 KB
Raw

Instructions to create SageMaker Notebook and upload sample notebooks

Step 1: Create SageMaker Notebook:

To create an Amazon SageMaker notebook instance

  1. Open the Amazon SageMaker console at https://console.aws.amazon.com/sagemaker/home?region=us-east-1

  2. Choose Notebook instances, then choose Create notebook instance.

image 1

On the Create notebook instance page, provide the following information:

  • For Notebook instance name, type SageMakerWorkshop.
  • ❗❗❗ For Instance type, choose CPU instances such as ml.m4.xlarge or GPU based instances ml.p3.2xLarge. VERY IMPORTANT - To avoid additional uncessary charge on your AWS account Please remember to stop notebook as soon as training is finished.
  • For IAM role, create an IAM role.
  • Choose Create a new role.

image 2

To access more S3 buckets from your Amazon SageMaker notebook instance

  • choose Any S3 bucket

Choose Create role. Amazon SageMaker creates an IAM role named AmazonSageMaker-ExecutionRole-YYYYMMDDTHHmmSS. For example, AmazonSageMaker-ExecutionRole-20171125T090800.

  1. Choose Create notebook instance. In a few minutes, Amazon SageMaker launches an ML compute instance—in this case, a notebook instance—and attaches an ML storage volume to it. The notebook instance has a preconfigured Jupyter notebook server and a set of Anaconda libraries.

When the status of the notebook instance is InService, choose Open next to its name to open the Juypter dashboard.

image 3

The dashboard provides access to:

A new tab that contains sample notebooks. To use a sample notebook, on the "Sagemaker Examples" tab, choose the sample notebook you would like to explore. For information about the sample notebooks, see the Amazon SageMaker GitHub repository.

Step 2: To upload sample notebooks

Use the following steps to clone the following file in your already created notebook instance:

  • Go to Jupyter
  • New > Terminal
cd SageMaker/

git clone https://github.com/mahendrabairagi/ASU_Hackathon
  • Go to jupyter and open the newly downloaded ipynb notebook file by clicking

"ASU_hackathon_train_mascot_model_resnet18.ipynb"

"ASU_hackathon_train_roadfollowing_model.ipynb"

Step3: Using sample notebooks

To create new Road Following model, follow instruction on ASU_hackathon_train_roadfollowing_model notebook

To create new Mascot detection model, follow instruction on ASU_hackathon_train_mascot_model_resnet18 notebook