You must register on Eventbrite.
All instructions will come through Eventbrite. Please make sure your Eventbrite email address is up to date.
Register for early access directly on our website.
Request one of our talks for your conference or meetup.
This workshop is FREE, but would otherwise cost <25 USD.
Note: This workshop will create an ephemeral AWS acccount for each attendee. This ephemeral account is not accessible after the workshop. You can, of course, clone this GitHub repo and reproduce the entire workshop in your own AWS Account.
If you do not logout of existing AWS Consoles, things will not work properly.
Please logout of all AWS Console sessions in all browser tabs.
Take the defaults and click on Open AWS Console. This will open AWS Console in a new browser tab.
If you see this message, you need to logout from any previously used AWS accounts.
Please logout of all AWS Console sessions in all browser tabs.
Double-check that your account name is similar to TeamRole/MasterKey as follows:
If not, please logout of your AWS Console in all browser tabs and re-run the steps above!
Open the AWS Management Console
Note: This workshop has been tested on the US West (Oregon) (us-west-2) region. Make sure that you see Oregon on the top right hand corner of your AWS Management Console. If you see a different region, click the dropdown menu and select US West (Oregon).
In the AWS Console search bar, type SageMaker and select Amazon SageMaker to open the service console.
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Select Create notebook instance.
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In the Notebook instance name text box, enter workshop.
Choose ml.c5.2xlarge. We'll only be using this instance to launch jobs. The training job themselves will run either on a SageMaker managed cluster or an Amazon EKS cluster.
Volume size 250 - this is needed to explore datasets, build docker containers, and more. During training data is copied directly from Amazon S3 to the training cluster when using SageMaker. When using Amazon EKS, we'll setup a distributed file system that worker nodes will use to get access to training data.
In the IAM role box, select the default TeamRole.
Click Create notebook instance.
Note: Proceed when the status of the notebook instance changes from Pending to InService after a few minutes.
Click File > New > [...scroll down...] Terminal to launch a terminal in your Jupyter instance.
Within the Jupyter terminal, run the following:
cd ~/SageMaker && git clone https://github.com/data-science-on-aws/workshop
REPEATING AGAIN - THIS IS IMPORTANT - MAKE SURE YOU RUN THIS IN THE JUPYTER TERMINAL
cd ~/SageMaker && git clone https://github.com/data-science-on-aws/workshop
Navigate to 01_setup/ in your Jupyter notebook and start the workshop!
You may need to refresh your browser if you don't see the new workshop/ directory.
