Thank you for attending our session at KDD.
If you are interested in our Research reward program, please see the link below. Or contact me directly. https://aws.amazon.com/aws-ml-research-awards/
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon EC2 P3 instances deliver the highest performance compute in the cloud, are cost-effective, support all major machine learning frameworks, and are available globally. In this workshop, you'll create a SageMaker notebook instance and work through sample Jupyter notebooks that demonstrate some of the many features of SageMaker and how Amazon EC2 P3 is used to accelerate machine learning model training.
Slides https://github.com/awshlabs/kdd2019/tree/master/slides This now includes GTC DC 2019 Slides.
In order to complete this workshop you'll need an AWS Account with access to create AWS IAM, S3, and SageMaker resources. If you do not have an AWS Account, please follow the instructions here to create an AWS Account.
The code and instructions in this workshop assume only one student is using a given AWS account at a time. If you try sharing an account with another student, you'll run into naming conflicts for certain resources. You can work around these by appending a unique suffix to the resources that fail to create due to conflicts, but the instructions do not provide details on the changes required to make this work.
If you are provided with AWS credit for this workshop, use this link to apply the credit to your AWS Account.
SageMaker is not available in all AWS Regions at this time. Accordingly, we recommend running this workshop in one of the supported AWS Regions such as N. Virginia, Oregon, Ohio. For this Lab, please use Oregon.
Once you've chosen a region, you should create all of the resources for this workshop there, including a new Amazon S3 bucket and a new SageMaker notebook instance. Make sure you select your region from the dropdown in the upper right corner of the AWS Console before getting started.
We recommend you use the latest version of Chrome or Firefox to complete this workshop.
This workshop is divided into multiple modules. Module 1 must be completed first. You can complete the other modules (Modules 2 and 3) in any order.
- Creating a Notebook Instance (in Oregon)
- Object Detection Using P3
- Running GluonNLP BERT Model
Be patient as you work your way through the notebook-based modules. After you run a cell in a notebook, it may take several seconds for the code to show results. For the cells that start training jobs, it may take 10 to 30 minutes.
After you have completed the workshop, you can delete all of the resources that were created by following the Cleanup Guide provided with this lab guide.
In this module, we'll start by creating an Amazon S3 bucket that will be used throughout the workshop. We'll then create a SageMaker notebook instance, which we will use to run the other workshop modules.
SageMaker typically uses S3 as storage for data and model artifacts. In this step you'll create a S3 bucket for this purpose. To