Difference between revisions of "Using AWS SageMaker Studio"

From DeepSense Docs
Jump to: navigation, search
m
m (→‎Accessing DeepSense AWS SageMaker Studio)
 
(One intermediate revision by the same user not shown)
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 ==
 
== 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.
−
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.'''
+
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.  
+
 
 +
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 ==
 
== 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 [[Onboarding on AWS]] 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.
−
* On your first login, you will be prompted to create a password.
 
−
* After creating the password, log in again using your username and the newly created password.
 
−
* During the first login, you will be redirected to enable Multi-Factor Authentication (MFA). Follow the on-screen instructions to complete the MFA setup.
 
−
* Once MFA is set up, you will be directed to the AWS Access Portal.
 
−
* Click on the Applications tab and select the assigned SageMaker Studio. This will redirect you to your Studio IDE.
 
  
 
== Working on 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.
+
* 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: 
 +
** 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 ==
−
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. 
 +
* 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.
Retrieved from "https://docs.deepsense.ca/index.php?title=Using_AWS_SageMaker_Studio&oldid=732"