Using AWS SageMaker Studio - Revision history

PSuthar: /* Accessing DeepSense AWS SageMaker Studio */

2025-09-10T13:24:20Z

‎Accessing DeepSense AWS SageMaker Studio

PSuthar: Changes according to new setup

2025-09-10T13:18:38Z

Changes according to new setup

PSuthar at 15:08, 28 January 2025

2025-01-28T15:08:57Z

PSuthar: /* Accessing DeepSense AWS SageMaker Studio */

2025-01-28T15:07:33Z

‎Accessing DeepSense AWS SageMaker Studio

PSuthar at 15:06, 28 January 2025

2025-01-28T15:06:46Z

PSuthar: /* New edit Accessing DeepSense AWS SageMaker Studio Domain */

2025-01-28T15:04:54Z

‎New edit Accessing DeepSense AWS SageMaker Studio Domain

PSuthar: /* Updated Accessing DeepSense AWS Management Console */

2025-01-28T14:50:08Z

‎Updated Accessing DeepSense AWS Management Console

PSuthar: /* Accessing DeepSense AWS SageMaker Studio Domain */

2024-10-24T13:51:25Z

‎Accessing DeepSense AWS SageMaker Studio Domain

PSuthar: /* Accessing DeepSense AWS SageMaker Studio Domain */

2024-09-04T17:26:55Z

‎Accessing DeepSense AWS SageMaker Studio Domain

PSuthar at 19:22, 22 May 2024

2024-05-22T19:22:14Z

@@ Line 15: / Line 15: @@
 == Accessing DeepSense AWS SageMaker Studio ==
-# Log in to the AWS Access Portal (see [[AWS Account Access]] for setup instructions).
+# Log in to the AWS Access Portal (see [[Onboarding on AWS]] for setup instructions).
 # Click on the '''Applications''' tab.
 # Select your assigned '''SageMaker Studio''' to be redirected to the Studio IDE.
 == Working on DeepSense AWS SageMaker Studio ==

@@ Line 1: / Line 1: @@
-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.
+Amazon SageMaker Studio is a web-based integrated development environment (IDE) for machine learning (ML). It allows data scientists and developers to perform all ML development steps—data preparation, 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 the Studio IDE.
-'''AWS administrator will create DeepSense AWS IAM user account.'''
+You can find the Studio domain from the SageMaker service dashboard's left-side panel. These domains provide [[Resources]] (instances) that can be used for project work.
-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 SageMaker Studio ==
-* Accept the invitation sent in the welcome email and navigate to the AWS Management Console login page.
+# Log in to the AWS Access Portal (see [[AWS Account Access]] for setup instructions).
-* Alternatively, use the URL provided in the AWS correspondence email to sign in.
+# Click on the '''Applications''' tab.
-* Enter the username as specified in the email.
+# Select your assigned '''SageMaker Studio''' to be redirected to the Studio IDE.
 == Working on DeepSense AWS SageMaker Studio ==
-* From your Studio UI, start by creating a JupyterLab space. Click on JupyterLab and use the Create a new space button located in the top-right corner.
+* From the Studio UI, create a new JupyterLab space:
-* If you have a supervisor, select Shared space; otherwise, create a Private space.
+** Click on '''JupyterLab''' and select '''Create a new space''' (top-right corner).
-* After creating the space, select it and review the details.
+** If you have a supervisor, select a '''Shared space'''; otherwise, create a '''Private space'''.
-* To open the Jupyter application, select the JupyterServer application and launch the specific instance type you are instructed to use.
+** After creation, select the space and review its details.
-* (Optional) In the Change environment dialog box, you can select a start-up script from the dropdown menu if needed.
-* Use the ml.t3.medium instance type if you are editing code without executing it.
+* Launch a Jupyter application:
-* After editing your code, save your changes. Stop the current instance, select the required instance type from the dropdown menu, and restart it.
+** Select the '''JupyterServer''' application.
-* To create a new notebook in JupyterLab: go to File, select New, and then Notebook. A new notebook tab will open in a new window.
+** Start the specific instance type you are instructed to use.
-* To stop an instance after completing your work, navigate to the Running instances section in the left pane of the SageMaker console.
+** (Optional) In the ''Change environment'' dialog box, select a startup script if required.
-* Click the Stop button under the Actions column for the notebook instance you wish to stop.
-* Once the notebook instance is stopped, you can restart it anytime by clicking the Start button.
+* Recommended instance usage:
+== Best Practices ==
-It is necessary to stop the notebook instance in order to avoid being charged for not utilized hours.
+* Always stop unused notebook instances to avoid unnecessary charges.
-So, when you're finished, please stop the instance.
+* Use Shared spaces when collaborating with supervisors or teammates.

@@ Line 9: / Line 9: @@
 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 ==
+== Accessing DeepSense AWS SageMaker Studio ==
 * Accept the invitation sent in the welcome email and navigate to the AWS Management Console login page.
 * Alternatively, use the URL provided in the AWS correspondence email to sign in.
@@ Line 19: / Line 19: @@
 * Click on the Applications tab and select the assigned SageMaker Studio. This will redirect you to your Studio IDE.
-== Accessing DeepSense AWS SageMaker Studio ==
+== Working on DeepSense AWS SageMaker Studio ==
 * From your Studio UI, start by creating a JupyterLab space. Click on JupyterLab and use the Create a new space button located in the top-right corner.
 * If you have a supervisor, select Shared space; otherwise, create a Private space.

@@ Line 20: / Line 20: @@
 == Accessing DeepSense AWS SageMaker Studio ==
-* Start by creating a JupyterLab space. Click on JupyterLab and use the Create a new space button located in the top-right corner.
+* From your Studio UI, start by creating a JupyterLab space. Click on JupyterLab and use the Create a new space button located in the top-right corner.
 * If you have a supervisor, select Shared space; otherwise, create a Private space.
 * After creating the space, select it and review the details.

@@ Line 10: / Line 10: @@
 == Accessing DeepSense AWS Management Console ==
-* Accept the invitation provided in the welcome email and navigate to the AWS Management Console login page.
+* Accept the invitation sent in the welcome email and navigate to the AWS Management Console login page.
-* Or enter the URL provided in AWS correspondence email and continue as Sign in .
+* Alternatively, use the URL provided in the AWS correspondence email to sign in.
-* Enter the username in the field as mentioned in the email.
+* Enter the username as specified in the email.
-* You will be redirected to create the password on first login.
+* On your first login, you will be prompted to create a password.
-* Your password will be created and you have to login again with username and created password.
+* After creating the password, log in again using your username and the newly created password.
-* First login redirects you to add MFA, follow the on-screen instructions to enable MFA.
+* During the first login, you will be redirected to enable Multi-Factor Authentication (MFA). Follow the on-screen instructions to complete the MFA setup.
-* After setting MFA you will be redirected to AWS access portal.
+* Once MFA is set up, you will be directed to the AWS Access Portal.
-* Select "Application" tab and choose given "SageMaker Studio" which will redirect to your Studio IDE.
+* Click on the Applications tab and select the assigned SageMaker Studio. This will redirect you to your Studio IDE.
-== Accessing DeepSense AWS SageMaker Studio Domain ==
+== Accessing DeepSense AWS SageMaker Studio ==
 * Start by creating a JupyterLab space. Click on JupyterLab and use the Create a new space button located in the top-right corner.
 * If you have a supervisor, select Shared space; otherwise, create a Private space.

@@ Line 21: / Line 21: @@
 == Accessing DeepSense AWS SageMaker Studio Domain ==
 *Select Admin configurations from the left navigation panel.
 *Select Domains from the Admin configurations menu.
 *Choose your project named domain to access from the Domains page.

@@ Line 25: / Line 25: @@
 *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).
-*Select Applications from the user profile's settings.
+*Dropdown "Launch" from the user profile's settings and then .
-*To open the Jupyter app, select the JupyterServer application.
+*To open the Jupyter app, select the JupyterServer application, then launch specific instance type you are told to use.
 *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.
@@ Line 33: / Line 35: @@
 *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.