Notebooks

Data practitioners can experiment machine learning algorithms spinning up Jupyter notebook with access to all your datasets. data.all leverages Amazon SageMaker instance to access Jupyter notebooks.

📦 Create a Notebook

⚠️ Pre-requisites

To use Notebooks you need to introduce your own VPC ID or create a Sagemaker Studio domain inside a VPC (read the docs). Provisioning the notebook instances inside a VPC enables the notebook to access VPC-only resources such as EFS file systems.

To create a Notebook, go to Notebooks on the left pane and click on the Create button. Then fill in the following form:

Field	Description	Required	Editable	Example
Sagemaker instance name	Name of the notebook	Yes	No	Cannes Project
Short description	Short description about the notebook	No	No	Notebook for Cannes exploration
Tags	Tags	No	No	deleteme
Environment	Environment (and mapped AWS account)	Yes	No	Data Science
Region (auto-filled)	AWS region	Yes	No	Europe (Ireland)
Organization (auto-filled)	Organization of the environment	Yes	No	AnyCompany EMEA
Team	Team that owns the notebook	Yes	No	DataScienceTeam
VPC Identifier	VPC provided to host the notebook	No	No	vpc-……
Subnets	Subnets provided to host the notebook	No	No	subnet-….
Instance type	[ml.t3.medium, ml.t3.large, ml.m5.xlarge]	Yes	No	ml.t3.medium
Volume size	[32, 64, 128, 256]	Yes	No	32

If successfully created we can check its metadata in the Overview tab. Unlike other data.all resources, Notebooks are non-editable.

☁️ Check CloudFormation stack

In the Stack tab of the Notebook, is where we check the AWS resources provisioned by data.all as well as its status. As part of the Notebook CloudFormation stack deployed using CDK, data.all will deploy:

1. AWS EC2 Security Group
2. AWS SageMaker Notebook Instance
3. AWS KMS Key and Alias

🗑️ Delete a Notebook

To delete a Notebook, simply select it and click on the Delete button in the top right corner. It is possible to keep the CloudFormation stack associated with the Notebook by selecting this option in the confirmation delete window that appears after clicking on delete.

💻 Open JupyterLab

Click on the Open JupyterLab button of the Notebook window to start writing code on Jupyter Notebooks.

⏹️ Stop/Start instance

As we briefly commented, data.all uses AWS SageMaker instances to access Jupyter notebooks. Be frugal and stop your instances when you are not developing. To do that, close the Jupyter window and click on Stop Instance in the Notebook buttons. It takes a couple of minutes, just refresh and check the Notebook Status in the overview tab. It should end up in STOPPED.

✅ Save money, stop your instances

This feature allows users to easily manage their instances directly from data.all UI.

Same when you are coming back to work on your Notebook, click on Start instance to start the SageMaker instance. In this case the Status of the notebook should first be PENDING and once the instance is ready, INSERVICE.

🏷️ Create Key-value tags

In the Tags tab of the notebook window, we can create key-value tags. These tags are not data.all tags that are used to tag datasets and find them in the catalog. In this case we are creating AWS tags as part of the notebook CloudFormation stack. There are multiple tagging strategies as explained in the documentation.