Using AWS SageMaker Studio

Amazon SageMaker Studio is a web-based integrated development environment (IDE) for machine learning (ML) that allows data scientists and developers to perform all ML development steps, from data preparation to model building, training, deployment, and management, on a single, unified platform.

DeepSense AWS SageMaker Studio

You can find the Studio domain from SageMaker service dashboard's left side panel. These are the instances Resources which can be used. DeepSense has acquired Amazon SageMaker Studio Domains per project to give users access to use the Studio IDE. The Studio also includes a comprehensive set of tools for every stage of machine learning development, from data preparation to building, training, deploying, and managing ML models. It enables users to jump between these steps quickly to fine-tune their models, replay training experiments, and scale to distributed training directly from JupyterLab, Code Editor, or RStudio. . Use Studio notebooks in your project domain to prepare and process data, write code to train models, deploy models to SageMaker hosting, and test or validate your models.

AWS administrator will create DeepSense AWS IAM user account. AWS will send you an email containing information about your account. and users will have access to the AWS SageMaker Studio specific to thee project in new domain.

Accessing DeepSense AWS Management Console

• Open web browser and navigate to the AWS Management Console login page.
• Enter the URL provided in AWS correspondence email and continue as Sign in as IAM user.
• Enter the IAM username in the "IAM user name" field.
• Enter the IAM user's temporary first password in the "Password" field provided by DeepSense AWS Admin.
• You will be redirected to change the password on first login, create a new password.
• After signing in to the AWS Management Console, locate the security credentials in the right top corner, where the account number and user name is written.
• Click on add MFA and follow the on-screen instructions to enable MFA. (https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_mfa_enable_virtual.html#enable-virt-mfa-for-iam-user)
• After setting MFA kindly sign out and then sign in again with new password you have created and use MFA to access the cloud resources.

Accessing DeepSense AWS SageMaker Studio Domain

• Select Admin configurations from the left navigation panel.
• Check for current AWS region on top right corner beside your account name. Set it to Canada (Central).
• Select Domains from the Admin configurations menu.
• Choose your project named domain to access from the Domains page.
• Select User Profiles from the domain settings.
• Choose the user profile you want to view (if there are more than 1 person working on same project).
• Dropdown "Launch" from the user profile's settings and then .
• To open the Jupyter app, select the JupyterServer application, then launch specific instance type you are told to use.
• Please use t3.medium instance if you are only editing the code and not executing it.
• After editing your code save it and then stop the instance and launch the required instance after that.
• To open Studio Notebook: Select File, New, and Notebook from the SageMaker Studio menu.
• Choose your Image, Kernel, instance type, and start-up script from the dropdown menus in the Change environment dialogue box, then click Select.
• A new Studio tab will appear once your new notebook launches.
• For stopping the instance once your work is done, click the Notebook instances in the left pane of the SageMaker console.
• Then click the Stop link under the Actions column to the left of the notebook instance's name.
• Once the notebook instance is stopped, you can start it again by clicking the Start link

It is necessary to stop the notebook instance in order to avoid being charged for not utilized hours. So, when you're finished, please stop the instance.