Difference between revisions of "Using AWS SageMaker Studio"

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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.
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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 ==
 
== DeepSense AWS SageMaker Studio ==
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You can find the Studio domain from SageMaker service dashboard's left side panel. These are the instances [[Resources]] which can be used.
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DeepSense has acquired Amazon SageMaker Studio Domains per project to give users access to the Studio IDE.
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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.
 
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. 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.
 
  
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'''AWS administrator will create DeepSense AWS IAM user account.'''
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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.
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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.  
 
  
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== Accessing DeepSense AWS Management Console ==
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SageMaker Studio includes a comprehensive set of tools for every stage of ML development, enabling you to: 
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* Accept the invitation provided in the welcome email and navigate to the AWS Management Console login page.
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* Prepare and process data 
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* Or enter the URL provided in AWS correspondence email and continue as Sign in .
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* Build, train, and deploy models 
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* Enter the username in the field as mentioned in the email.
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* Manage experiments and training jobs 
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* You will be redirected to create the password on first login.
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* Switch quickly between workflows (JupyterLab, Code Editor, or RStudio) 
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* Your password will be created and you have to login again with username and created password.
 
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* First login redirects you to add MFA, follow the on-screen instructions to enable MFA.
 
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* After setting MFA you will be redirected to AWS access portal.
 
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* Select "Application" tab and choose given "SageMaker Studio" which will redirect to your Studio IDE.
 
  
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== Accessing DeepSense AWS SageMaker Studio Domain ==
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Use Studio notebooks in your project domain to prepare data, write training code, deploy ML models, and validate results.
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*Select Admin configurations from the left navigation panel.
 
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*Check for current AWS region on top right corner beside your account name. Set it to Canada (Central).
 
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*Select Domains from the Admin configurations menu.
 
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*Choose your project named domain to access from the Domains page.
 
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*Select User Profiles from the domain settings.
 
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*Choose the user profile you want to view (if there are more than 1 person working on same project).
 
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*Dropdown "Launch" from the user profile's settings and then .
 
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*To open the Jupyter app, select the JupyterServer application, then launch specific instance type you are told to use.
 
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*Please use t3.medium instance if you are only editing the code and not executing it.
 
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*After editing your code save it and then stop the instance and launch the required instance after that.
 
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*To open Studio Notebook: Select File, New, and Notebook from the SageMaker Studio menu.
 
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*Choose your Image, Kernel, instance type, and start-up script from the dropdown menus in the Change environment dialogue box, then click Select.
 
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*A new Studio tab will appear once your new notebook launches.
 
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*For stopping the instance once your work is done, click the Notebook instances in the left pane of the SageMaker console.
 
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*Then click the Stop link under the Actions column to the left of the notebook instance's name.
 
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*Once the notebook instance is stopped, you can start it again by clicking the Start link
 
  
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== Accessing DeepSense AWS SageMaker Studio ==
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It is necessary to stop the notebook instance in order to avoid being charged for not utilized hours.  
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# Log in to the AWS Access Portal (see [[Onboarding on AWS]] for setup instructions). 
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So, when you're finished, please stop the instance.
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# Click on the '''Applications''' tab.  
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# Select your assigned '''SageMaker Studio''' to be redirected to the Studio IDE.
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== Working on DeepSense AWS SageMaker Studio ==
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* From the Studio UI, create a new JupyterLab space: 
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** Click on '''JupyterLab''' and select '''Create a new space''' (top-right corner). 
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** If you have a supervisor, select a '''Shared space'''; otherwise, create a '''Private space'''. 
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** After creation, select the space and review its details. 
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* Launch a Jupyter application: 
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** Select the '''JupyterServer''' application. 
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** Start the specific instance type you are instructed to use.
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** (Optional) In the ''Change environment'' dialog box, select a startup script if required. 
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* Recommended instance usage: 
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** Use '''ml.t3.medium''' if editing code without execution. 
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** For training or running experiments, switch to the instance type specified by your project lead. 
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* Notebook management: 
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** To create a new notebook: go to '''File → New → Notebook'''. 
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** Save your work frequently. 
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** To stop an instance: 
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::: 1. Navigate to the '''Running instances''' section in the SageMaker console. 
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::: 2. Click '''Stop''' under the Actions column. 
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** To restart: click '''Start''' for the stopped instance. 
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== Best Practices ==
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* Always stop unused notebook instances to avoid unnecessary charges. 
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* Use Shared spaces when collaborating with supervisors or teammates. 
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* Keep experiments organized by naming notebooks and spaces clearly.

Latest revision as of 13:24, 10 September 2025

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

DeepSense has acquired Amazon SageMaker Studio Domains per project to give users access to the Studio IDE.

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.

SageMaker Studio includes a comprehensive set of tools for every stage of ML development, enabling you to:

  • Prepare and process data
  • Build, train, and deploy models
  • Manage experiments and training jobs
  • Switch quickly between workflows (JupyterLab, Code Editor, or RStudio)

Use Studio notebooks in your project domain to prepare data, write training code, deploy ML models, and validate results.

Accessing DeepSense AWS SageMaker Studio

  1. Log in to the AWS Access Portal (see Onboarding on AWS for setup instructions).
  2. Click on the Applications tab.
  3. Select your assigned SageMaker Studio to be redirected to the Studio IDE.

Working on DeepSense AWS SageMaker Studio

  • From the Studio UI, create a new JupyterLab space:
    • Click on JupyterLab and select Create a new space (top-right corner).
    • If you have a supervisor, select a Shared space; otherwise, create a Private space.
    • After creation, select the space and review its details.
  • Launch a Jupyter application:
    • Select the JupyterServer application.
    • Start the specific instance type you are instructed to use.
    • (Optional) In the Change environment dialog box, select a startup script if required.
  • Recommended instance usage:
    • Use ml.t3.medium if editing code without execution.
    • For training or running experiments, switch to the instance type specified by your project lead.
  • Notebook management:
    • To create a new notebook: go to File → New → Notebook.
    • Save your work frequently.
    • To stop an instance:
1. Navigate to the Running instances section in the SageMaker console.
2. Click Stop under the Actions column.
    • To restart: click Start for the stopped instance.

Best Practices

  • Always stop unused notebook instances to avoid unnecessary charges.
  • Use Shared spaces when collaborating with supervisors or teammates.
  • Keep experiments organized by naming notebooks and spaces clearly.
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