DeepSense Docs - User contributions [en-ca]

MediaWiki:Sidebar

2025-10-22T18:31:22Z

PSuthar:

* navigation
** mainpage|mainpage-description
* DeepSense Cloud Computing Platform
** mainpage | Available Cloud Services
** Resources | Resources
* DeepSense AI Development Platform
** Access AI Development Platform | Access AI Development Platform
** Managing Notebooks on AI Development Platform | Managing Notebooks
** Sharing Notebooks on AI Development Platform | Sharing Notebooks
** Accessing CLI in Notebooks AI Development Platform | Accessing CLI
** Hosting TensorBoard in Notebooks AI Development Platform | Hosting TensorBoard

* Onboarding and using cloud services
** Onboarding on AWS | AWS Account Setup
** Using AWS EC2 | AWS EC2
** Using AWS Sagemaker | AWS SageMaker Notebook
** Using AWS SageMaker Studio | AWS SageMaker Studio
** Using AWS SageMaker Studio - Old IAM Method| AWS SageMaker Studio - Old
** Using Azure ML Workspace | Azure ML Workspace
** Using HPC on AWS and Azure | HPC on AWS and Azure
** Using AWS SageMaker Endpoint | AWS SageMaker Endpoint
** Using S3 Bucket | AWS S3 Bucket
* Getting Support
** Contact information | Contact - Support email
* Getting Started
** Introduction to Cloud Computing | Introduction to Cloud Computing
** User Guide | User Guide

* Storage on cloud
** Overview of your Storage on DeepSense | Storage Overview
* FAQ
** Restrictions | Restrictions
** Workarounds | Workarounds
** Best Practices | Best Practices
* Writing Tips
** Mitacs Accelerate Proposals | Mitacs Accelerate Proposals
** Paper Prep | Paper Prep
* DeepSense
** https://deepsense.ca | DeepSense home page
** Acknowledging DeepSense | Acknowledging DeepSense
** Terms of Use | Terms of use
* Self Directed Projects
** Training Projects | Training Projects
* Additional Resources
** Related Links | Related Links
** External Links | External Links
** External Data Sources | External Data Sources

Hosting TensorBoard in Notebooks AI Development Platform

2025-10-22T15:03:09Z

PSuthar:

== Hosting TensorBoard from JupyterLab Notebook Instance ==
TensorBoard is a visualization tool for TensorFlow and PyTorch experiments.
When using AWS SageMaker or the AI Development Platform, TensorBoard can be hosted directly from your JupyterLab notebook instance and accessed securely via the built-in proxy server.

=== Prerequisites ===
* Access to a running JupyterLab environment in SageMaker Studio or the AI Development Platform.
* TensorFlow or PyTorch training logs saved to a local directory (e.g., <code>./logs/</code>).
* AWS CLI setup completed (see [[Accessing CLI in Notebooks AI Development Platform]]).

=== Step 1: Start TensorBoard Server ===
In a notebook cell or terminal, navigate to your log directory and run:
<pre>
tensorboard --logdir=./logs/ --port=6006 --host=0.0.0.0
</pre>

This command starts TensorBoard on port 6006 and binds it to all network interfaces so it can be accessed via the Jupyter proxy.

=== Step 2: Access TensorBoard via Proxy ===
Once the TensorBoard server is running, open the following URL pattern in your browser (from within the same JupyterLab environment):

<pre>
https://<your-notebook-domain>/proxy/6006/
</pre>

Example:
<pre>
https://studiolab.sagemaker.aws/proxy/6006/
</pre>

You should now see the TensorBoard interface displaying your training metrics and graphs.

=== Step 3: Troubleshooting ===
* If TensorBoard doesn’t load, ensure:
** The port (6006) is available and not used by another process.
** The logs directory exists and contains valid TensorFlow/PyTorch event files.
** You’re using the correct proxy path (<code>/proxy/6006/</code>).

* To stop TensorBoard:
<pre>
pkill tensorboard
</pre>

=== Notes ===
* TensorBoard will only remain active while the notebook instance is running.
* Avoid exposing TensorBoard publicly; always access it via the secure notebook proxy.

Accessing CLI in Notebooks AI Development Platform

2025-10-08T19:04:25Z

PSuthar: /* Prerequisites */

== Accessing the Terminal from JupyterLab Notebook Instance ==
In the AI Development Platform (hosted on AWS SageMaker), you can access the terminal directly from your JupyterLab environment to use command-line tools such as the AWS CLI, Git, or Python scripts.

=== Prerequisites ===
* Access to your DeepSense AI Platform's Notebook project environment.
* JupyterLab interface opened under your assigned project.

=== Steps to Open the Terminal ===
# From the JupyterLab interface, click on the '''+''' icon to open the Launcher tab.
# Under the ''Other'' section, click on '''Terminal'''.
# A new terminal tab will open within your notebook environment.
# You can now run shell commands (e.g., to interact with AWS S3, manage files, or install dependencies).

=== Notes ===
* The terminal runs inside your notebook instance — all operations execute within the same compute environment.
* Avoid installing system-wide packages unless instructed by your project lead.
* For instructions on using the AWS CLI inside this terminal, see [[AWS S3 Bucket Access]].
* To close the terminal, simply click the '''X''' icon on the terminal tab.

Hosting TensorBoard in Notebooks AI Development Platform

2025-10-08T19:02:31Z

PSuthar: Create hosting TensorBoard in notebooks page

Accessing CLI in Notebooks AI Development Platform

2025-10-08T17:38:43Z

PSuthar: Create accessing CLI on AI platform page

== Accessing the Terminal from JupyterLab Notebook Instance ==
In the AI Development Platform (hosted on AWS SageMaker), you can access the terminal directly from your JupyterLab environment to use command-line tools such as the AWS CLI, Git, or Python scripts.

=== Prerequisites ===
* Access to your DeepSense AWS SageMaker Studio project environment.
* JupyterLab interface opened under your assigned project domain.

=== Steps to Open the Terminal ===
# From the JupyterLab interface, click on the '''+''' icon to open the Launcher tab.
# Under the ''Other'' section, click on '''Terminal'''.
# A new terminal tab will open within your notebook environment.
# You can now run shell commands (e.g., to interact with AWS S3, manage files, or install dependencies).

=== Notes ===
* The terminal runs inside your notebook instance — all operations execute within the same compute environment.
* Avoid installing system-wide packages unless instructed by your project lead.
* For instructions on using the AWS CLI inside this terminal, see [[AWS S3 Bucket Access]].
* To close the terminal, simply click the '''X''' icon on the terminal tab.

Sharing Notebooks on AI Development Platform

2025-10-08T17:36:33Z

PSuthar: Create sharing notebook page

== Sharing Notebook Instance with Supervisor on DeepSense AI Platform ==
You can share your notebook instance with your supervisor to collaborate or review work.

=== Steps to Share a Notebook ===
# Navigate to the '''My Notebooks''' page.
# Select the notebook you want to share to open its details page.
# Click the '''Share''' button in the upper-right corner.
# Enter your supervisor’s valid email address in the provided field.
# Click '''Share''' to send the invitation.

Once shared, your supervisor will gain access to the notebook instance.

=== Notes ===
* Ensure that your supervisor’s email is registered in the DeepSense domain.
* You can revoke access at any time from the same Share menu.

Managing Notebooks on AI Development Platform

2025-10-08T17:35:42Z

PSuthar: Create managing notebooks page

== Managing Notebooks on DeepSense AI Platform ==
This page describes how to edit, delete, and manage your notebooks in the DeepSense AI Platform.

=== Editing or Deleting a Notebook ===
# To make changes or delete a notebook, it must first be stopped.
# Navigate to the '''Home''' page and click on the notebook name.
# The notebook details page will open.
# To delete the notebook, click '''Delete'''. This will permanently terminate the notebook.
# To edit the notebook, click '''Edit'''. The same form used during notebook creation will appear.
# Make the necessary changes and save them.

=== Notes ===
* Always stop the notebook before editing or deleting.
* Deleting a notebook cannot be undone.

MediaWiki:Sidebar

2025-10-08T17:22:29Z

PSuthar:

* navigation
** mainpage|mainpage-description
* DeepSense Cloud Computing Platform
** mainpage | Available Cloud Services
** Resources | Resources
* DeepSense AI Development Platform
** Access AI Development Platform | Access AI Development Platform
** Managing Notebooks on AI Development Platform | Managing Notebooks
** Sharing Notebooks on AI Development Platform | Sharing Notebooks
** Accessing CLI in Notebooks AI Development Platform | Access CLI
** Hosting TensorBoard in Notebooks AI Development Platform | Hosting TensorBoard

* Onboarding and using cloud services
** Onboarding on AWS | AWS Account Setup
** Using AWS EC2 | AWS EC2
** Using AWS Sagemaker | AWS SageMaker Notebook
** Using AWS SageMaker Studio | AWS SageMaker Studio
** Using AWS SageMaker Studio - Old IAM Method| AWS SageMaker Studio - Old
** Using Azure ML Workspace | Azure ML Workspace
** Using HPC on AWS and Azure | HPC on AWS and Azure
** Using AWS SageMaker Endpoint | AWS SageMaker Endpoint
** Using S3 Bucket | AWS S3 Bucket
* Getting Support
** Contact information | Contact - Support email
* Getting Started
** Introduction to Cloud Computing | Introduction to Cloud Computing
** User Guide | User Guide

* Storage on cloud
** Overview of your Storage on DeepSense | Storage Overview
* FAQ
** Restrictions | Restrictions
** Workarounds | Workarounds
** Best Practices | Best Practices
* Writing Tips
** Mitacs Accelerate Proposals | Mitacs Accelerate Proposals
** Paper Prep | Paper Prep
* DeepSense
** https://deepsense.ca | DeepSense home page
** Acknowledging DeepSense | Acknowledging DeepSense
** Terms of Use | Terms of use
* Self Directed Projects
** Training Projects | Training Projects
* Additional Resources
** Related Links | Related Links
** External Links | External Links
** External Data Sources | External Data Sources

MediaWiki:Sidebar

2025-10-08T17:21:00Z

PSuthar:

* navigation
** mainpage|mainpage-description
* DeepSense Cloud Computing Platform
** mainpage | Available Cloud Services
** Resources | Resources
* DeepSense AI Development Platform
** Access AI Development Platform | Access AI Development Platform
** Managing Notebooks | Managing Notebooks
** Sharing Notebooks | Sharing Notebooks
** Accessing CLI | Access CLI in Notebooks
** Hosting TensorBoard | Hosting TensorBoard in Notebooks

* Onboarding and using cloud services
** Onboarding on AWS | AWS Account Setup
** Using AWS EC2 | AWS EC2
** Using AWS Sagemaker | AWS SageMaker Notebook
** Using AWS SageMaker Studio | AWS SageMaker Studio
** Using AWS SageMaker Studio - Old IAM Method| AWS SageMaker Studio - Old
** Using Azure ML Workspace | Azure ML Workspace
** Using HPC on AWS and Azure | HPC on AWS and Azure
** Using AWS SageMaker Endpoint | AWS SageMaker Endpoint
** Using S3 Bucket | AWS S3 Bucket
* Getting Support
** Contact information | Contact - Support email
* Getting Started
** Introduction to Cloud Computing | Introduction to Cloud Computing
** User Guide | User Guide

* Storage on cloud
** Overview of your Storage on DeepSense | Storage Overview
* FAQ
** Restrictions | Restrictions
** Workarounds | Workarounds
** Best Practices | Best Practices
* Writing Tips
** Mitacs Accelerate Proposals | Mitacs Accelerate Proposals
** Paper Prep | Paper Prep
* DeepSense
** https://deepsense.ca | DeepSense home page
** Acknowledging DeepSense | Acknowledging DeepSense
** Terms of Use | Terms of use
* Self Directed Projects
** Training Projects | Training Projects
* Additional Resources
** Related Links | Related Links
** External Links | External Links
** External Data Sources | External Data Sources

MediaWiki:Sidebar

2025-10-08T17:20:05Z

PSuthar: Add new pages for AI dev platform

* navigation
** mainpage|mainpage-description
* DeepSense Cloud Computing Platform
** mainpage | Available Cloud Services
** Resources | Resources
* DeepSense AI Development Platform
** Access AI Development Platform | Access AI Development Platform
** Managing Notebooks | Managing Notebooks
** Sharing Notebooks | Sharing Notebooks
** Accessing CLI in Notebooks | Access CLI in Notebooks
** Hosting TensorBoard in Notebooks | Hosting TensorBoard in Notebooks

* Onboarding and using cloud services
** Onboarding on AWS | AWS Account Setup
** Using AWS EC2 | AWS EC2
** Using AWS Sagemaker | AWS SageMaker Notebook
** Using AWS SageMaker Studio | AWS SageMaker Studio
** Using AWS SageMaker Studio - Old IAM Method| AWS SageMaker Studio - Old
** Using Azure ML Workspace | Azure ML Workspace
** Using HPC on AWS and Azure | HPC on AWS and Azure
** Using AWS SageMaker Endpoint | AWS SageMaker Endpoint
** Using S3 Bucket | AWS S3 Bucket
* Getting Support
** Contact information | Contact - Support email
* Getting Started
** Introduction to Cloud Computing | Introduction to Cloud Computing
** User Guide | User Guide

* Storage on cloud
** Overview of your Storage on DeepSense | Storage Overview
* FAQ
** Restrictions | Restrictions
** Workarounds | Workarounds
** Best Practices | Best Practices
* Writing Tips
** Mitacs Accelerate Proposals | Mitacs Accelerate Proposals
** Paper Prep | Paper Prep
* DeepSense
** https://deepsense.ca | DeepSense home page
** Acknowledging DeepSense | Acknowledging DeepSense
** Terms of Use | Terms of use
* Self Directed Projects
** Training Projects | Training Projects
* Additional Resources
** Related Links | Related Links
** External Links | External Links
** External Data Sources | External Data Sources

Onboarding on AWS

2025-09-11T18:57:55Z

PSuthar: /* Steps to Access Your AWS Account */

== DeepSense AWS Account Access ==
DeepSense provides each user with an AWS Identity and Access Management (IAM) account to access services such as SageMaker Studio and S3.

'''The AWS administrator will create your DeepSense AWS IAM user account.'''
You will receive a welcome email from AWS containing your login details. Follow the steps below to activate and secure your account.

=== Steps to Access Your AWS Account ===
# Accept the invitation sent in the welcome email and navigate to the AWS Management Console login page.
# Alternatively, use the direct URL provided in the AWS correspondence email to sign in. [[https://deepsense.awsapps.com/start DeepSense AWS]]
# Enter the '''username''' specified in the email.
# On your first login, you will be prompted to '''create a password'''.
# After creating the password, log in again using your username and new password.
# During the first login, you will be required to set up '''Multi-Factor Authentication (MFA)'''. Follow the on-screen instructions to complete the MFA setup.
# Once MFA is configured, you will be redirected to the '''AWS Access Portal'''.
# From the portal:
** To access your assigned SageMaker Studio environment, click the '''Applications''' tab and select SageMaker Studio. See [[Using AWS SageMaker Studio]] for details.
** To access S3 storage, follow the steps in [[Using S3 Bucket]].

=== Notes ===
* Bookmark the AWS Access Portal URL for quicker access.
* MFA setup is mandatory for account security. Ensure you use a reliable authenticator app (e.g., Microsoft Authenticator, Google Authenticator, Authy).
* If you face login or MFA issues, contact the DeepSense Cloud Administrator.

DeepSense Documentation

2025-09-11T15:18:37Z

PSuthar:

'''Welcome to the DeepSense technical cloud documentation wiki'''. This is the primary source for users with questions on the DeepSense cloud equipment and services. DeepSense uses cloud services from various cloud vendors for the development of AI projects. You'll now find all of our content on the sidebar. Just below you can see the available cloud services, and information about any planned outages we may have.
Due to the nature of "unlimited resources" of cloud computing, DeepSense doesn't limit any cloud services that the projects need. The cloud services listed in the following tables are our currently running resources. This doesn't necessarily indicate we cannot use other cloud resources. DeepSense users are encouraged to contact us to apply for required cloud computing services.
We continuously explore the best cloud solutions to the AI projects. We don't lock our solutions in any specific cloud vendors. The tables below show the cloud services we have tested and are developing projects on. More cloud services will be coming soon.

We routinely make changes and update the content. If you see anything missing, or have any suggestions for content, we would appreciate hearing from you. You can send us an email at ([mailto:info@deepsense.ca info@deepsense.ca]).
You can click on "Resources" on the navigation panel to find the technical details of the virtual machines and serverless computing services.

== DeepSense Cloud Computing Services ==

'''DeepSense AI Development Platform'''
{|class="wikitable" style="text-align: center; color: black; font-style:bold"
|'''Availability'''
|style="width:20% | '''Service'''
|style="width:70% | '''Usage'''
|-
|style="Color:green" | Available
| JupyterLab Notebook Instances
| JupyterLab notebook instance is a machine learning compute instance running the Jupyter Notebook application.
|-
|style="Color:green" | Available
| Instance attached Volume
| JupyterLab Notebook Instance attached with storage volume that hosts the directory on JupyterLab. The size of the storage volume is scalable.
|}

Follow the steps here [[Access AI Development Platform]] to access the platform.

'''Amazon Web Services (AWS)'''
{|class="wikitable" style="text-align: center; color: black; font-style:bold"
|'''Availability'''
|style="width:20% | '''Service'''
|style="width:70% | '''Usage'''
|-
|style="Color:green" | Available
| S3 (Simple Storage Service)
| Amazon S3 can be used to store and retrieve any amount of data. Mainly use it for long term data storage or backing up your data.
|-
|style="Color:green" | Available
| EC2 (Elastic Compute Cloud)
| Amazon EC2 can be used to create virtual machines for training models in a manually configured environment.
|-
|style="Color:green" | Available
| SageMaker Notebook
| Amazon SageMaker notebook instance is a machine learning (ML) compute instance that runs the Jupyter Notebook App.
|-
|style="Color:green" | Available
| SageMaker Studio - AutoML
| Amazon SageMaker Studio provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps.
|-
|style="Color:green" | Available
| SageMaker Endpoint
| Amazon SageMaker Inference Endpoints are a powerful tool to deploy your machine learning models in the cloud and make predictions on new data.
|}

'''Microsoft Azure'''
{|class="wikitable" style="text-align: center; color: black; font-style:bold"
|'''Availability'''
|style="width:20% | '''Service'''
|style="width:70% | '''Usage'''
|-
|style="Color:green" | Available
| Blob Storage
| Azure Blob Storage is a store for objects capable of storing large amounts of unstructured data. Can be used for long term storage or backing up your data.
|-
|style="Color:green" | Available
| Virtual Machine
| Azure Virtual Machines are image service instances that provide on-demand and scalable computing resources for training models in a manually configured environment.
|-
|style="Color:green" | Available
| Machine Learning Workspace
| Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project lifecycle.
|}

'''Google Cloud Platform (GCP)'''
{|class="wikitable" style="text-align: center; color: black; font-style:bold"
|'''Availability'''
|style="width:20% | '''Service'''
|style="width:70% | '''Usage'''
|-
|style="Color:green" | Available
| Cloud Storage
| Cloud Storage is a service for storing your objects in Google Cloud. Mainly use it for long term storage or backing up your data.
|-
|style="Color:green" | Available
| Compute Engine
|
Compute Engine is a customizable compute service that lets you create and run virtual machines for training models in a manually configured environment.
|-
|style="Color:orange" | Available Soon
| Vertex AI Notebooks
| Vertex AI Workbench managed notebooks instances are Google-managed end-to-end Jupyter notebook-based environment.
|}

'''HPC on AWS and Azure'''
{|class="wikitable" style="text-align: center; color: black; font-style:bold"
|'''Availability'''
|style="width:20% | '''Service'''
|style="width:70% | '''Usage'''
|-
|style="Color:green" | Available
| AWS ParallelCluster
| AWS ParallelCluster is an AWS supported open source cluster management tool that helps you to deploy and manage high performance computing (HPC) clusters in the AWS Cloud.
|-
|style="Color:green" | Available
| Azure CycleCloud
| Azure CycleCloud is designed to enable enterprise IT organizations to provide secure and flexible cloud HPC and Big Compute environments to their end users.
|}

DeepSense Documentation

2025-09-11T15:16:51Z

PSuthar: /* DeepSense Cloud Computing Services */

'''Welcome to the DeepSense technical cloud documentation wiki'''. This is the primary source for users with questions on the DeepSense cloud equipment and services. DeepSense uses cloud services from various cloud vendors for the development of AI projects. You'll now find all of our content on the sidebar. Just below you can see the available cloud services, and information about any planned outages we may have.
Due to the nature of "unlimited resources" of cloud computing, DeepSense doesn't limit any cloud services that the projects need. The cloud services listed in the following tables are our currently running resources. This doesn't necessarily indicate we cannot use other cloud resources. DeepSense users are encouraged to contact us to apply for required cloud computing services.
We continuously explore the best cloud solutions to the AI projects. We don't lock our solutions in any specific cloud vendors. The tables below show the cloud services we have tested and are developing projects on. More cloud services will be coming soon.

We routinely make changes and update the content. If you see anything missing, or have any suggestions for content, we would appreciate hearing from you. You can send us an email at ([mailto:info@deepsense.ca info@deepsense.ca]).
You can click on "Resources" on the navigation panel to find the technical details of the virtual machines and serverless computing services.

== DeepSense Cloud Computing Services ==

'''DeepSense AI Development Platform'''
{|class="wikitable" style="text-align: center; color: black; font-style:bold"
|'''Availability'''
|style="width:20% | '''Service'''
|style="width:70% | '''Usage'''
|-
|style="Color:green" | Available
| JupyterLab Notebook Instances
| JupyterLab notebook instance is a machine learning compute instance running the Jupyter Notebook application.
|-
|style="Color:green" | Available
| Instance attached Volume
| JupyterLab Notebook Instance attached with storage volume that hosts the directory on JupyterLab. The size of the storage volume is scalable.
|}

'''Amazon Web Services (AWS)'''
{|class="wikitable" style="text-align: center; color: black; font-style:bold"
|'''Availability'''
|style="width:20% | '''Service'''
|style="width:70% | '''Usage'''
|-
|style="Color:green" | Available
| S3 (Simple Storage Service)
| Amazon S3 can be used to store and retrieve any amount of data. Mainly use it for long term data storage or backing up your data.
|-
|style="Color:green" | Available
| EC2 (Elastic Compute Cloud)
| Amazon EC2 can be used to create virtual machines for training models in a manually configured environment.
|-
|style="Color:green" | Available
| SageMaker Notebook
| Amazon SageMaker notebook instance is a machine learning (ML) compute instance that runs the Jupyter Notebook App.
|-
|style="Color:green" | Available
| SageMaker Studio - AutoML
| Amazon SageMaker Studio provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps.
|-
|style="Color:green" | Available
| SageMaker Endpoint
| Amazon SageMaker Inference Endpoints are a powerful tool to deploy your machine learning models in the cloud and make predictions on new data.
|}

'''Microsoft Azure'''
{|class="wikitable" style="text-align: center; color: black; font-style:bold"
|'''Availability'''
|style="width:20% | '''Service'''
|style="width:70% | '''Usage'''
|-
|style="Color:green" | Available
| Blob Storage
| Azure Blob Storage is a store for objects capable of storing large amounts of unstructured data. Can be used for long term storage or backing up your data.
|-
|style="Color:green" | Available
| Virtual Machine
| Azure Virtual Machines are image service instances that provide on-demand and scalable computing resources for training models in a manually configured environment.
|-
|style="Color:green" | Available
| Machine Learning Workspace
| Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project lifecycle.
|}

'''Google Cloud Platform (GCP)'''
{|class="wikitable" style="text-align: center; color: black; font-style:bold"
|'''Availability'''
|style="width:20% | '''Service'''
|style="width:70% | '''Usage'''
|-
|style="Color:green" | Available
| Cloud Storage
| Cloud Storage is a service for storing your objects in Google Cloud. Mainly use it for long term storage or backing up your data.
|-
|style="Color:green" | Available
| Compute Engine
|
Compute Engine is a customizable compute service that lets you create and run virtual machines for training models in a manually configured environment.
|-
|style="Color:orange" | Available Soon
| Vertex AI Notebooks
| Vertex AI Workbench managed notebooks instances are Google-managed end-to-end Jupyter notebook-based environment.
|}

'''HPC on AWS and Azure'''
{|class="wikitable" style="text-align: center; color: black; font-style:bold"
|'''Availability'''
|style="width:20% | '''Service'''
|style="width:70% | '''Usage'''
|-
|style="Color:green" | Available
| AWS ParallelCluster
| AWS ParallelCluster is an AWS supported open source cluster management tool that helps you to deploy and manage high performance computing (HPC) clusters in the AWS Cloud.
|-
|style="Color:green" | Available
| Azure CycleCloud
| Azure CycleCloud is designed to enable enterprise IT organizations to provide secure and flexible cloud HPC and Big Compute environments to their end users.
|}

DeepSense Documentation

2025-09-11T15:16:03Z

PSuthar: Add AI Dev Platform

'''Welcome to the DeepSense technical cloud documentation wiki'''. This is the primary source for users with questions on the DeepSense cloud equipment and services. DeepSense uses cloud services from various cloud vendors for the development of AI projects. You'll now find all of our content on the sidebar. Just below you can see the available cloud services, and information about any planned outages we may have.
Due to the nature of "unlimited resources" of cloud computing, DeepSense doesn't limit any cloud services that the projects need. The cloud services listed in the following tables are our currently running resources. This doesn't necessarily indicate we cannot use other cloud resources. DeepSense users are encouraged to contact us to apply for required cloud computing services.
We continuously explore the best cloud solutions to the AI projects. We don't lock our solutions in any specific cloud vendors. The tables below show the cloud services we have tested and are developing projects on. More cloud services will be coming soon.

We routinely make changes and update the content. If you see anything missing, or have any suggestions for content, we would appreciate hearing from you. You can send us an email at ([mailto:info@deepsense.ca info@deepsense.ca]).
You can click on "Resources" on the navigation panel to find the technical details of the virtual machines and serverless computing services.

== DeepSense Cloud Computing Services ==

'''DeepSense AI Development Platform'''
{|class="wikitable" style="text-align: center; color: black; font-style:bold"
|'''Availability'''
|style="width:20% | '''Service'''
|style="width:70% | '''Usage'''
|-
|style="Color:green" | Available
| JupyterLab Notebook Instances
| JupyterLab notebook instance is a machine learning (ML) compute instance running the Jupyter Notebook application.
|-
|style="Color:green" | Available
| Instance attached Volume
| JupyterLab Notebook Instance attached with storage volume that hosts the directory on JupyterLab. The size of the storage volume is scalable.
|}

'''Amazon Web Services (AWS)'''
{|class="wikitable" style="text-align: center; color: black; font-style:bold"
|'''Availability'''
|style="width:20% | '''Service'''
|style="width:70% | '''Usage'''
|-
|style="Color:green" | Available
| S3 (Simple Storage Service)
| Amazon S3 can be used to store and retrieve any amount of data. Mainly use it for long term data storage or backing up your data.
|-
|style="Color:green" | Available
| EC2 (Elastic Compute Cloud)
| Amazon EC2 can be used to create virtual machines for training models in a manually configured environment.
|-
|style="Color:green" | Available
| SageMaker Notebook
| Amazon SageMaker notebook instance is a machine learning (ML) compute instance that runs the Jupyter Notebook App.
|-
|style="Color:green" | Available
| SageMaker Studio - AutoML
| Amazon SageMaker Studio provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps.
|-
|style="Color:green" | Available
| SageMaker Endpoint
| Amazon SageMaker Inference Endpoints are a powerful tool to deploy your machine learning models in the cloud and make predictions on new data.
|}

'''Microsoft Azure'''
{|class="wikitable" style="text-align: center; color: black; font-style:bold"
|'''Availability'''
|style="width:20% | '''Service'''
|style="width:70% | '''Usage'''
|-
|style="Color:green" | Available
| Blob Storage
| Azure Blob Storage is a store for objects capable of storing large amounts of unstructured data. Can be used for long term storage or backing up your data.
|-
|style="Color:green" | Available
| Virtual Machine
| Azure Virtual Machines are image service instances that provide on-demand and scalable computing resources for training models in a manually configured environment.
|-
|style="Color:green" | Available
| Machine Learning Workspace
| Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project lifecycle.
|}

'''Google Cloud Platform (GCP)'''
{|class="wikitable" style="text-align: center; color: black; font-style:bold"
|'''Availability'''
|style="width:20% | '''Service'''
|style="width:70% | '''Usage'''
|-
|style="Color:green" | Available
| Cloud Storage
| Cloud Storage is a service for storing your objects in Google Cloud. Mainly use it for long term storage or backing up your data.
|-
|style="Color:green" | Available
| Compute Engine
|
Compute Engine is a customizable compute service that lets you create and run virtual machines for training models in a manually configured environment.
|-
|style="Color:orange" | Available Soon
| Vertex AI Notebooks
| Vertex AI Workbench managed notebooks instances are Google-managed end-to-end Jupyter notebook-based environment.
|}

'''HPC on AWS and Azure'''
{|class="wikitable" style="text-align: center; color: black; font-style:bold"
|'''Availability'''
|style="width:20% | '''Service'''
|style="width:70% | '''Usage'''
|-
|style="Color:green" | Available
| AWS ParallelCluster
| AWS ParallelCluster is an AWS supported open source cluster management tool that helps you to deploy and manage high performance computing (HPC) clusters in the AWS Cloud.
|-
|style="Color:green" | Available
| Azure CycleCloud
| Azure CycleCloud is designed to enable enterprise IT organizations to provide secure and flexible cloud HPC and Big Compute environments to their end users.
|}

Onboarding on AWS

2025-09-10T13:30:31Z

PSuthar:

== DeepSense AWS Account Access ==
DeepSense provides each user with an AWS Identity and Access Management (IAM) account to access services such as SageMaker Studio and S3.

'''The AWS administrator will create your DeepSense AWS IAM user account.'''
You will receive a welcome email from AWS containing your login details. Follow the steps below to activate and secure your account.

=== Steps to Access Your AWS Account ===
# Accept the invitation sent in the welcome email and navigate to the AWS Management Console login page.
# Alternatively, use the direct URL provided in the AWS correspondence email to sign in.
# Enter the '''username''' specified in the email.
# On your first login, you will be prompted to '''create a password'''.
# After creating the password, log in again using your username and new password.
# During the first login, you will be required to set up '''Multi-Factor Authentication (MFA)'''. Follow the on-screen instructions to complete the MFA setup.
# Once MFA is configured, you will be redirected to the '''AWS Access Portal'''.
# From the portal:
** To access your assigned SageMaker Studio environment, click the '''Applications''' tab and select SageMaker Studio. See [[Using AWS SageMaker Studio]] for details.
** To access S3 storage, follow the steps in [[Using S3 Bucket]].

=== Notes ===
* Bookmark the AWS Access Portal URL for quicker access.
* MFA setup is mandatory for account security. Ensure you use a reliable authenticator app (e.g., Microsoft Authenticator, Google Authenticator, Authy).
* If you face login or MFA issues, contact the DeepSense Cloud Administrator.

Using AWS SageMaker Studio

2025-09-10T13:24:20Z

PSuthar: /* Accessing DeepSense AWS SageMaker Studio */

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 ==
# 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 ==
* 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.

Onboarding on AWS

2025-09-10T13:23:35Z

PSuthar:

== DeepSense AWS Account Access ==
DeepSense provides each user with an AWS Identity and Access Management (IAM) account to access services such as SageMaker Studio and S3.

'''The AWS administrator will create your DeepSense AWS IAM user account.'''
You will receive a welcome email from AWS containing your login details. Follow the steps below to activate and secure your account.

=== Steps to Access Your AWS Account ===
# Accept the invitation sent in the welcome email and navigate to the AWS Management Console login page.
# Alternatively, use the direct URL provided in the AWS correspondence email to sign in.
# Enter the '''username''' specified in the email.
# On your first login, you will be prompted to '''create a password'''.
# After creating the password, log in again using your username and new password.
# During the first login, you will be required to set up '''Multi-Factor Authentication (MFA)'''. Follow the on-screen instructions to complete the MFA setup.
# Once MFA is configured, you will be redirected to the '''AWS Access Portal'''.
# From the portal:
** To access your assigned SageMaker Studio environment, click the '''Applications''' tab and select SageMaker Studio. See [[Using AWS SageMaker Studio]] for details.
** To access S3 storage, follow the steps in [[Using S3 Bucket]].

=== Notes ===
* Bookmark the AWS Access Portal URL for quicker access.
* MFA setup is mandatory for account security. Ensure you use a reliable authenticator app (e.g., Google Authenticator, Authy).
* If you face login or MFA issues, contact the DeepSense Cloud Administrator.

Onboarding on AWS

2025-09-10T13:22:43Z

PSuthar: Add links

== DeepSense AWS Account Access ==
DeepSense provides each user with an AWS Identity and Access Management (IAM) account to access services such as SageMaker Studio and S3.

'''The AWS administrator will create your DeepSense AWS IAM user account.'''
You will receive a welcome email from AWS containing your login details. Follow the steps below to activate and secure your account.

=== Steps to Access Your AWS Account ===
# Accept the invitation sent in the welcome email and navigate to the AWS Management Console login page.
# Alternatively, use the direct URL provided in the AWS correspondence email to sign in.
# Enter the '''username''' specified in the email.
# On your first login, you will be prompted to '''create a password'''.
# After creating the password, log in again using your username and new password.
# During the first login, you will be required to set up '''Multi-Factor Authentication (MFA)'''. Follow the on-screen instructions to complete the MFA setup.
# Once MFA is configured, you will be redirected to the '''AWS Access Portal'''.
# From the portal:
** To access your assigned SageMaker Studio environment, click the '''Applications''' tab and select SageMaker Studio. See [[AWS SageMaker Studio]] for details.
** To access S3 storage, follow the steps in [[AWS S3 Bucket]].

=== Notes ===
* Bookmark the AWS Access Portal URL for quicker access.
* MFA setup is mandatory for account security. Ensure you use a reliable authenticator app (e.g., Google Authenticator, Authy).
* If you face login or MFA issues, contact the DeepSense Cloud Administrator.

Using AWS SageMaker Studio

2025-09-10T13:18:38Z

PSuthar: Changes according to new setup

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 ==
# Log in to the AWS Access Portal (see [[AWS Account Access]] 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 ==
* 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.

Onboarding on AWS

2025-09-10T13:15:59Z

PSuthar: First draft of account access

== DeepSense AWS Account Access ==
DeepSense provides each user with an AWS Identity and Access Management (IAM) account to access services such as SageMaker Studio and S3.

'''The AWS administrator will create your DeepSense AWS IAM user account.'''
You will receive a welcome email from AWS containing your login details. Follow the steps below to activate and secure your account.

=== Steps to Access Your AWS Account ===
# Accept the invitation sent in the welcome email and navigate to the AWS Management Console login page.
# Alternatively, use the direct URL provided in the AWS correspondence email to sign in.
# Enter the '''username''' specified in the email.
# On your first login, you will be prompted to '''create a password'''.
# After creating the password, log in again using your username and new password.
# During the first login, you will be required to set up '''Multi-Factor Authentication (MFA)'''. Follow the on-screen instructions to complete the MFA setup.
# Once MFA is configured, you will be redirected to the '''AWS Access Portal'''.
# From the portal:
** To access your assigned SageMaker Studio environment, click the '''Applications''' tab and select SageMaker Studio. See [[AWS SageMaker Studio Access]] for details.
** To access S3 storage, follow the steps in [[AWS S3 Bucket Access]].

=== Notes ===
* Bookmark the AWS Access Portal URL for quicker access.
* MFA setup is mandatory for account security. Ensure you use a reliable authenticator app (e.g., Google Authenticator, Authy).
* If you face login or MFA issues, contact the DeepSense Cloud Administrator.

Using S3 Bucket

2025-09-10T13:11:39Z

PSuthar:

Amazon S3, or Amazon Simple Storage Service, is an object storage service offered by Amazon Web Services (AWS). It provides a highly scalable, durable, and available storage solution for any type of data, accessible from anywhere on the web.

== DeepSense AWS S3 ==
At DeepSense, you will be using S3 buckets to store large datasets. These datasets can then be accessed in AWS SageMaker for use in projects.

'''The AWS administrator will create your DeepSense AWS IAM user account.'''
AWS will send you an email containing your account details. Users will then have access to AWS.

== Accessing the S3 Bucket on DeepSense AWS ==
# Sign in to the AWS access portal once your account is active.
# From the account dropdown, select '''DeepSense''' and choose the respective bucket you want to access.
# The AWS console will open after selection.
# Use the direct link provided by your Cloud Administrator (''tip: bookmark the link for easier access'').
# The S3 bucket should now be accessible in your browser.

== Using the S3 Bucket on DeepSense AWS ==
* Once inside the S3 bucket, you can:
** Download files
** Upload files
** Delete files or directories

* To access the bucket in your terminal, use the '''Access Key''' from the AWS access portal.

* To add bucket files to your AI Development Platform notebook, you have two approaches:
** '''Option 1 – Easy method (for small files):'''
::: Download the files from the S3 console to your local machine, then upload them to your JupyterLab directory for use in your project.

** '''Option 2 – Recommended for large files:'''
::: 1. Create your access keys from the AWS access portal.
::: 2. Open the terminal in your notebook instance.
::: 3. Follow the steps in the [AWS CLI Authentication Guide](https://docs.aws.amazon.com/cli/v1/userguide/cli-authentication-user.html#cli-authentication-user-configure.title).
::: 4. Once configured, run S3 CLI commands (e.g., <code>aws s3 cp</code>, <code>aws s3 ls</code>) to upload or download files directly between your bucket and notebook.

Using S3 Bucket

2025-09-10T13:10:15Z

PSuthar: Final edit for initial S3 bucket page

Amazon S3, or Amazon Simple Storage Service, is an object storage service offered by Amazon Web Services (AWS). It provides a highly scalable, durable, and available storage solution for any type of data, accessible from anywhere on the web.

== DeepSense AWS S3 ==
At DeepSense, you will be using S3 buckets to store large datasets. These datasets can then be accessed in AWS SageMaker for use in projects.

'''The AWS administrator will create your DeepSense AWS IAM user account.'''
AWS will send you an email containing your account details. Users will then have access to AWS SageMaker Studio, specific to their project, under the new domain.

== Accessing the S3 Bucket on DeepSense AWS ==
# Sign in to the AWS access portal once your account is active.
# From the account dropdown, select '''DeepSense''' and choose the respective bucket you want to access.
# The AWS console will open after selection.
# Use the direct link provided by your Cloud Administrator (''tip: bookmark the link for easier access'').
# The S3 bucket should now be accessible in your browser.

== Using the S3 Bucket on DeepSense AWS ==
* Once inside the S3 bucket, you can:
** Download files
** Upload files
** Delete files or directories

* To access the bucket in your terminal, use the '''Access Key''' from the AWS access portal.

* To add bucket files to your AI Development Platform notebook, you have two approaches:
** '''Option 1 – Easy method (for small files):'''
::: Download the files from the S3 console to your local machine, then upload them to your JupyterLab directory for use in your project.

** '''Option 2 – Recommended for large files:'''
::: 1. Create your access keys from the AWS access portal.
::: 2. Open the terminal in your notebook instance.
::: 3. Follow the steps in the [AWS CLI Authentication Guide](https://docs.aws.amazon.com/cli/v1/userguide/cli-authentication-user.html#cli-authentication-user-configure.title).
::: 4. Once configured, run S3 CLI commands (e.g., <code>aws s3 cp</code>, <code>aws s3 ls</code>) to upload or download files directly between your bucket and notebook.

Using S3 Bucket

2025-09-10T13:09:38Z

PSuthar: First Draft

Amazon S3, or Amazon Simple Storage Service, is an object storage service offered by Amazon Web Services (AWS). It provides a highly scalable, durable, and available storage solution for any type of data, accessible from anywhere on the web.

== DeepSense AWS S3 ==
At DeepSense, you will be using S3 buckets to store large amount of dataset. Storing all of them on S3 and then using the same in AWS SageMaker to use for projects.

'''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 the S3 Bucket on DeepSense AWS ==
* Once your account is accessible and you are sign on to the AWS access portal.
* Select the account dropdown of DeepSense select the respective bucket access you want to use.
* After selection it will open AWS console.
* Click on the link directly from your Cloud administrator has provided you the link (Bookmark link for easier access)
* S3 bucket should be accessible now on your browser.

== Using the S3 Bucket on DeepSense AWS ==
* Once you are in the S3 bucket you can download, upload and delete files/ directories from the bucket.
* If you need to open the bucket in your terminal use the access key from the access portal.
* For adding your bucket files to AI Development Platform notebook, 1. Easy way - good for small files. 2. For big files.
* 1. Simply download the files from S3 console to your local and then upload it to your JupyterLab directory to use it for project.
* 2. Create your access keys from access portal then open terminal of your notebook instance and follow these steps https://docs.aws.amazon.com/cli/v1/userguide/cli-authentication-user.html#cli-authentication-user-configure.title after that just type in the S3 commands to get or put files in bucket.

MediaWiki:Sidebar

2025-09-10T12:32:02Z

PSuthar: Add AWS account setup

* navigation
** mainpage|mainpage-description
* DeepSense Cloud Computing Platform
** mainpage | Available Cloud Services
** Resources | Resources
* DeepSense AI Development Platform
** Access AI Development Platform | Access AI Development Platform
* Onboarding and using cloud services
** Onboarding on AWS | AWS Account Setup
** Using AWS EC2 | AWS EC2
** Using AWS Sagemaker | AWS SageMaker Notebook
** Using AWS SageMaker Studio | AWS SageMaker Studio
** Using AWS SageMaker Studio - Old IAM Method| AWS SageMaker Studio - Old
** Using Azure ML Workspace | Azure ML Workspace
** Using HPC on AWS and Azure | HPC on AWS and Azure
** Using AWS SageMaker Endpoint | AWS SageMaker Endpoint
** Using S3 Bucket | AWS S3 Bucket
* Getting Support
** Contact information | Contact - Support email
* Getting Started
** Introduction to Cloud Computing | Introduction to Cloud Computing
** User Guide | User Guide

* Storage on cloud
** Overview of your Storage on DeepSense | Storage Overview
* FAQ
** Restrictions | Restrictions
** Workarounds | Workarounds
** Best Practices | Best Practices
* Writing Tips
** Mitacs Accelerate Proposals | Mitacs Accelerate Proposals
** Paper Prep | Paper Prep
* DeepSense
** https://deepsense.ca | DeepSense home page
** Acknowledging DeepSense | Acknowledging DeepSense
** Terms of Use | Terms of use
* Self Directed Projects
** Training Projects | Training Projects
* Additional Resources
** Related Links | Related Links
** External Links | External Links
** External Data Sources | External Data Sources

MediaWiki:Sidebar

2025-09-10T12:18:13Z

PSuthar: Add S3 bucket doc

* navigation
** mainpage|mainpage-description
* DeepSense Cloud Computing Platform
** mainpage | Available Cloud Services
** Resources | Resources
* DeepSense AI Development Platform
** Access AI Development Platform | Access AI Development Platform
* Onboarding and using cloud services
** Using AWS EC2 | AWS EC2
** Using AWS Sagemaker | AWS SageMaker Notebook
** Using AWS SageMaker Studio | AWS SageMaker Studio
** Using AWS SageMaker Studio - Old IAM Method| AWS SageMaker Studio - Old
** Using Azure ML Workspace | Azure ML Workspace
** Using HPC on AWS and Azure | HPC on AWS and Azure
** Using AWS SageMaker Endpoint | AWS SageMaker Endpoint
** Using S3 Bucket | AWS S3 Bucket
* Getting Support
** Contact information | Contact - Support email
* Getting Started
** Introduction to Cloud Computing | Introduction to Cloud Computing
** User Guide | User Guide

* Storage on cloud
** Overview of your Storage on DeepSense | Storage Overview
* FAQ
** Restrictions | Restrictions
** Workarounds | Workarounds
** Best Practices | Best Practices
* Writing Tips
** Mitacs Accelerate Proposals | Mitacs Accelerate Proposals
** Paper Prep | Paper Prep
* DeepSense
** https://deepsense.ca | DeepSense home page
** Acknowledging DeepSense | Acknowledging DeepSense
** Terms of Use | Terms of use
* Self Directed Projects
** Training Projects | Training Projects
* Additional Resources
** Related Links | Related Links
** External Links | External Links
** External Data Sources | External Data Sources

Access AI Development Platform

2025-07-23T13:40:51Z

PSuthar: /* Sharing the notebook instance with supervisor on DeepSense AI Platform */

== What is DeepSense AI Development Platform? ==
The AI Development Platform by DeepSense reduces cloud complexity, enabling users to manage AI/ML workloads with a user-friendly UI. This platform is serverless, scalable, and focuses on simplified onboarding and resource management. To get started with the platform gather and understand project requirements from your organization then go to AI Dev platform: https://aidevplatform.deepsense.ca and follow the below steps to onboard.

== Accessing the notebook on DeepSense AI Platform ==
* Go to AI Dev platform’s signup page: https://aidevplatform.deepsense.ca/signup
* Fill all the details from the form and submit (don’t forget project details, we’ll need it to determine your resource type)
* Reach out to an admin for the account approval.
* Once approved, log in to your account with same credentials.
* Start your development by creating a new notebook.
* Fill out the details and submit.
* Notebook names must be unique and without any special characters (will fail if created notebook with same name or with underscore _ ).
* Select smallest required instance type (can be changed later).
* Give required storage size (can be increased later).
* Select if you need any libraries preinstalled in your notebook environment. (Only select the necessary ones as it will install every libraries on start)
* The notebook will automatically start, give it some time (5-10 mins max). If the notebook is stopped state, you can start again then continue.
* Open the notebook in a new tab by clicking the open button.

== Managing notebooks on DeepSense AI Platform ==
* If you need to edit/delete a notebook, it has to be stopped first.
* Click on notebook name on home page.
* You will see the notebook details.
* You can click on Delete to terminate the notebook.
* Click on Edit in case any changes are required to the notebook. You will be taken to edit the notebook. The form is same as when you created the notebook.

==Sharing the notebook instance with supervisor on DeepSense AI Platform==
* Navigate to My Notebooks page and select your notebook which will take you to notebook details page.
* There you can click on Share button upper right corner.
* Here you can add your supervisor’s valid email address and the notebook will be shared with your supervisor.

== Stopping the notebook instance on DeepSense AI Platform==
* To stop an instance after completing your work, navigate to the My notebooks page.
* Click the Stop button for the notebook instance you wish to stop.
* Once the notebook instance is stopped, you can restart it anytime by clicking the Start button.

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.

Access AI Development Platform

2025-07-23T13:33:29Z

PSuthar:

== What is DeepSense AI Development Platform? ==
The AI Development Platform by DeepSense reduces cloud complexity, enabling users to manage AI/ML workloads with a user-friendly UI. This platform is serverless, scalable, and focuses on simplified onboarding and resource management. To get started with the platform gather and understand project requirements from your organization then go to AI Dev platform: https://aidevplatform.deepsense.ca and follow the below steps to onboard.

== Accessing the notebook on DeepSense AI Platform ==
* Go to AI Dev platform’s signup page: https://aidevplatform.deepsense.ca/signup
* Fill all the details from the form and submit (don’t forget project details, we’ll need it to determine your resource type)
* Reach out to an admin for the account approval.
* Once approved, log in to your account with same credentials.
* Start your development by creating a new notebook.
* Fill out the details and submit.
* Notebook names must be unique and without any special characters (will fail if created notebook with same name or with underscore _ ).
* Select smallest required instance type (can be changed later).
* Give required storage size (can be increased later).
* Select if you need any libraries preinstalled in your notebook environment. (Only select the necessary ones as it will install every libraries on start)
* The notebook will automatically start, give it some time (5-10 mins max). If the notebook is stopped state, you can start again then continue.
* Open the notebook in a new tab by clicking the open button.

== Managing notebooks on DeepSense AI Platform ==
* If you need to edit/delete a notebook, it has to be stopped first.
* Click on notebook name on home page.
* You will see the notebook details.
* You can click on Delete to terminate the notebook.
* Click on Edit in case any changes are required to the notebook. You will be taken to edit the notebook. The form is same as when you created the notebook.

==Sharing the notebook instance with supervisor on DeepSense AI Platform==
* Navigate to My Notebooks page and select your notebook which will take you to notebook details page.
* There you can click on Share button.
* Here you can add your supervisor’s valid email address and the notebook will be shared with your supervisor (The other person should have an account on this platform).

== Stopping the notebook instance on DeepSense AI Platform==
* To stop an instance after completing your work, navigate to the My notebooks page.
* Click the Stop button for the notebook instance you wish to stop.
* Once the notebook instance is stopped, you can restart it anytime by clicking the Start button.

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.

Access AI Development Platform

2025-07-15T18:48:19Z

PSuthar: /* What is DeepSense AI Development Platform? */

== What is DeepSense AI Development Platform? ==
The AI Development Platform by DeepSense reduces cloud complexity, enabling users to manage AI/ML workloads with a user-friendly UI. This platform is serverless, scalable, and focuses on simplified onboarding and resource management. To get started with the platform gather and understand project requirements from your organization then go to AI Dev platform: https://aidevplatform.deepsense.ca and follow the below steps to onboard.

== Accessing the notebook on DeepSense AI Platform ==
* Go to AI Dev platform’s signup page: https://aidevplatform.deepsense.ca/signup
* Fill all the details from the form and submit (don’t forget project details, we’ll need it to determine your resource type)
* Reach out to an admin for the account approval.
* Once approved, log in to your account with same credentials.
* Start your development by creating a new notebook.
* Fill out the details and submit.
* Notebook names must be unique (will fail if notebook with same name exists in someone else’s account).
* Select smallest required instance type (can be changed later).
* Give required storage size (can be increased later).
* Select if you need any libraries preinstalled in your notebook environment.
* The notebook will automatically start, give it some time (5-10 mins max). If the notebook is stopped, you can start it.
* Open the notebook in a new tab by clicking the open button.
== Managing notebooks on DeepSense AI Platform ==
* If you need to edit/delete a notebook, it has to be stopped first.
* Click on notebook name on home page.
* You will see the notebook details.
* You can click on Delete to terminate the notebook.
* Click on Edit in case any changes are required to the notebook. You will be taken to edit the notebook. The form is same as when you created the notebook.
* Back to the notebook details page. If you need to share the notebook with your supervisor, click on Share button. Here you can add your supervisor’s valid email address and the notebook will be shared with your supervisor (The other person should have an account on this platform).
== Stopping the notebook instance on DeepSense AI Platform==
* To stop an instance after completing your work, navigate to the My notebooks page.
* Click the Stop button for the notebook instance you wish to stop.
* Once the notebook instance is stopped, you can restart it anytime by clicking the Start button.

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.

Access AI Development Platform

2025-05-05T18:22:08Z

PSuthar: Add Access Ai Dev Platform

== What is DeepSense AI Development Platform? ==
The AI Development Platform by DeepSense abstracts cloud complexity, enabling users to manage AI/ML workloads with a user-friendly UI. This platform is serverless, scalable, and focuses on simplified onboarding and resource management. Gather and understand project requirements from your organization. Go to AI Dev platform’s: https://aidevplatform.deepsense.ca
== Accessing the notebook on DeepSense AI Platform ==
* Go to AI Dev platform’s signup page: https://aidevplatform.deepsense.ca/signup
* Fill all the details from the form and submit (don’t forget project details, we’ll need it to determine your resource type)
* Reach out to an admin for the account approval.
* Once approved, log in to your account with same credentials.
* Start your development by creating a new notebook.
* Fill out the details and submit.
* Notebook names must be unique (will fail if notebook with same name exists in someone else’s account).
* Select smallest required instance type (can be changed later).
* Give required storage size (can be increased later).
* Select if you need any libraries preinstalled in your notebook environment.
* The notebook will automatically start, give it some time (5-10 mins max). If the notebook is stopped, you can start it.
* Open the notebook in a new tab by clicking the open button.
== Managing notebooks on DeepSense AI Platform ==
* If you need to edit/delete a notebook, it has to be stopped first.
* Click on notebook name on home page.
* You will see the notebook details.
* You can click on Delete to terminate the notebook.
* Click on Edit in case any changes are required to the notebook. You will be taken to edit the notebook. The form is same as when you created the notebook.
* Back to the notebook details page. If you need to share the notebook with your supervisor, click on Share button. Here you can add your supervisor’s valid email address and the notebook will be shared with your supervisor (The other person should have an account on this platform).
== Stopping the notebook instance on DeepSense AI Platform==
* To stop an instance after completing your work, navigate to the My notebooks page.
* Click the Stop button for the notebook instance you wish to stop.
* Once the notebook instance is stopped, you can restart it anytime by clicking the Start button.

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.

Access AI Development Platform

2025-05-05T16:58:04Z

PSuthar: Create initial AI Dev Platform

== What is DeepSense AI Development Platform ==
The AI Development Platform by DeepSense abstracts cloud complexity, enabling users to manage AI/ML workloads with a user-friendly UI. This platform is serverless, scalable, and focuses on simplified onboarding and resource management. Gather and understand project requirements from your organization. Go to AI Dev platform’s: https://aidevplatform.deepsense.ca
== Signup for DeepSense AI Platform ==
* Go to AI Dev platform’s signup page: https://aidevplatform.deepsense.ca/signup
* Fill all the details from the form and submit (don’t forget project details, we’ll need it to determine your resource type)
* Reach out to an admin for the account approval.
* Once approved, log in to your account with same credentials.
* Start your development by creating a new notebook.
* Fill out the details and submit.
* Notebook names must be unique (will fail if notebook with same name exists in someone else’s account).
* Select smallest required instance type (can be changed later).
* Give required storage size (can be increased later).
* Select if you need any libraries preinstalled in your notebook environment.
* The notebook will automatically start, give it some time (5-10 mins max). If the notebook is stopped, you can start it.
* Open the notebook in a new tab by clicking the open button.

MediaWiki:Sidebar

2025-05-05T16:46:28Z

PSuthar:

* navigation
** mainpage|mainpage-description
* DeepSense Cloud Computing Platform
** mainpage | Available Cloud Services
** Resources | Resources
* DeepSense AI Development Platform
** Access AI Development Platform | Access AI Development Platform
* Getting Support
** Contact information | Contact - Support email
* Getting Started
** Introduction to Cloud Computing | Introduction to Cloud Computing
** User Guide | User Guide
* Onboarding and using cloud services
** Using AWS EC2 | AWS EC2
** Using AWS Sagemaker | AWS SageMaker Notebook
** Using AWS SageMaker Studio | AWS SageMaker Studio
** Using AWS SageMaker Studio - Old IAM Method| AWS SageMaker Studio - Old
** Using Azure ML Workspace | Azure ML Workspace
** Using HPC on AWS and Azure | HPC on AWS and Azure
** Using AWS SageMaker Endpoint | AWS SageMaker Endpoint
**
* Storage on cloud
** Overview of your Storage on DeepSense | Storage Overview
* FAQ
** Restrictions | Restrictions
** Workarounds | Workarounds
** Best Practices | Best Practices
* Writing Tips
** Mitacs Accelerate Proposals | Mitacs Accelerate Proposals
** Paper Prep | Paper Prep
* DeepSense
** https://deepsense.ca | DeepSense home page
** Acknowledging DeepSense | Acknowledging DeepSense
** Terms of Use | Terms of use
* Self Directed Projects
** Training Projects | Training Projects
* Additional Resources
** Related Links | Related Links
** External Links | External Links
** External Data Sources | External Data Sources

MediaWiki:Sidebar

2025-05-05T16:45:27Z

PSuthar:

* navigation
** mainpage|mainpage-description
* DeepSense Cloud Computing Platform
** mainpage | Available Cloud Services
** Resources | Resources
* DeepSense AI Development Platform
** Access AI Development Platform | AI Platform
* Getting Support
** Contact information | Contact - Support email
* Getting Started
** Introduction to Cloud Computing | Introduction to Cloud Computing
** User Guide | User Guide
* Onboarding and using cloud services
** Using AWS EC2 | AWS EC2
** Using AWS Sagemaker | AWS SageMaker Notebook
** Using AWS SageMaker Studio | AWS SageMaker Studio
** Using AWS SageMaker Studio - Old IAM Method| AWS SageMaker Studio - Old
** Using Azure ML Workspace | Azure ML Workspace
** Using HPC on AWS and Azure | HPC on AWS and Azure
** Using AWS SageMaker Endpoint | AWS SageMaker Endpoint
**
* Storage on cloud
** Overview of your Storage on DeepSense | Storage Overview
* FAQ
** Restrictions | Restrictions
** Workarounds | Workarounds
** Best Practices | Best Practices
* Writing Tips
** Mitacs Accelerate Proposals | Mitacs Accelerate Proposals
** Paper Prep | Paper Prep
* DeepSense
** https://deepsense.ca | DeepSense home page
** Acknowledging DeepSense | Acknowledging DeepSense
** Terms of Use | Terms of use
* Self Directed Projects
** Training Projects | Training Projects
* Additional Resources
** Related Links | Related Links
** External Links | External Links
** External Data Sources | External Data Sources

MediaWiki:Sidebar

2025-05-05T16:44:22Z

PSuthar: Add AI Development Platform page

* navigation
** mainpage|mainpage-description
* DeepSense Cloud Computing Platform
** mainpage | Available Cloud Services
** Resources | Resources
* DeepSense AI Development Platform
** Access AI Development Platform
* Getting Support
** Contact information | Contact - Support email
* Getting Started
** Introduction to Cloud Computing | Introduction to Cloud Computing
** User Guide | User Guide
* Onboarding and using cloud services
** Using AWS EC2 | AWS EC2
** Using AWS Sagemaker | AWS SageMaker Notebook
** Using AWS SageMaker Studio | AWS SageMaker Studio
** Using AWS SageMaker Studio - Old IAM Method| AWS SageMaker Studio - Old
** Using Azure ML Workspace | Azure ML Workspace
** Using HPC on AWS and Azure | HPC on AWS and Azure
** Using AWS SageMaker Endpoint | AWS SageMaker Endpoint
**
* Storage on cloud
** Overview of your Storage on DeepSense | Storage Overview
* FAQ
** Restrictions | Restrictions
** Workarounds | Workarounds
** Best Practices | Best Practices
* Writing Tips
** Mitacs Accelerate Proposals | Mitacs Accelerate Proposals
** Paper Prep | Paper Prep
* DeepSense
** https://deepsense.ca | DeepSense home page
** Acknowledging DeepSense | Acknowledging DeepSense
** Terms of Use | Terms of use
* Self Directed Projects
** Training Projects | Training Projects
* Additional Resources
** Related Links | Related Links
** External Links | External Links
** External Data Sources | External Data Sources

Using AWS SageMaker Studio

2025-01-28T15:08:57Z

PSuthar:

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 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.
* Enter the username as specified in the email.
* 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 ==
* 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.
* To open the Jupyter application, select the JupyterServer application and launch the specific instance type you are instructed to use.
* (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.
* After editing your code, save your changes. Stop the current instance, select the required instance type from the dropdown menu, and restart it.
* 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.
* To stop an instance after completing your work, navigate to the Running instances section in the left pane of the SageMaker console.
* 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.

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.

Using AWS SageMaker Studio

2025-01-28T15:07:33Z

PSuthar: /* Accessing DeepSense 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 ==
* 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.
* Enter the username as specified in the email.
* 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.

== Accessing 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.
* After creating the space, select it and review the details.
* To open the Jupyter application, select the JupyterServer application and launch the specific instance type you are instructed to use.
* (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.
* After editing your code, save your changes. Stop the current instance, select the required instance type from the dropdown menu, and restart it.
* 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.
* To stop an instance after completing your work, navigate to the Running instances section in the left pane of the SageMaker console.
* 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.

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.

Using AWS SageMaker Studio

2025-01-28T15:06:46Z

PSuthar:

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 ==
* 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.
* Enter the username as specified in the email.
* 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.

== 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.
* After creating the space, select it and review the details.
* To open the Jupyter application, select the JupyterServer application and launch the specific instance type you are instructed to use.
* (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.
* After editing your code, save your changes. Stop the current instance, select the required instance type from the dropdown menu, and restart it.
* 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.
* To stop an instance after completing your work, navigate to the Running instances section in the left pane of the SageMaker console.
* 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.

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.

Using AWS SageMaker Studio

2025-01-28T15:04:54Z

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

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 ==
* Accept the invitation provided 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 .
* Enter the username in the field as mentioned in the email.
* You will be redirected to create the password on first login.
* Your password will be created and you have to login again with username and created password.
* First login redirects you to add MFA, follow the on-screen instructions to enable MFA.
* After setting MFA you will be redirected to AWS access portal.
* Select "Application" tab and choose given "SageMaker Studio" which will redirect to your Studio IDE.

== Accessing DeepSense AWS SageMaker Studio Domain ==
* 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.
* To open the Jupyter application, select the JupyterServer application and launch the specific instance type you are instructed to use.
* (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.
* After editing your code, save your changes. Stop the current instance, select the required instance type from the dropdown menu, and restart it.
* 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.
* To stop an instance after completing your work, navigate to the Running instances section in the left pane of the SageMaker console.
* 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.

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.

Using AWS SageMaker Studio

2025-01-28T14:50:08Z

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

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 ==
* Accept the invitation provided 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 .
* Enter the username in the field as mentioned in the email.
* You will be redirected to create the password on first login.
* Your password will be created and you have to login again with username and created password.
* First login redirects you to add MFA, follow the on-screen instructions to enable MFA.
* After setting MFA you will be redirected to AWS access portal.
* Select "Application" tab and choose given "SageMaker Studio" which will redirect to your Studio IDE.

== 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.

Using AWS SageMaker Studio - Old IAM Method

2025-01-28T14:32:28Z

PSuthar: Add new title - old description

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.

MediaWiki:Sidebar

2025-01-28T14:31:19Z

PSuthar: Initial SageMaker Studio new sign in method

* navigation
** mainpage|mainpage-description
* DeepSense Cloud Computing Platform
** mainpage | Available Cloud Services
** Resources | Resources
* Getting Support
** Contact information | Contact - Support email
* Getting Started
** Introduction to Cloud Computing | Introduction to Cloud Computing
** User Guide | User Guide
* Onboarding and using cloud services
** Using AWS EC2 | AWS EC2
** Using AWS Sagemaker | AWS SageMaker Notebook
** Using AWS SageMaker Studio | AWS SageMaker Studio
** Using AWS SageMaker Studio - Old IAM Method| AWS SageMaker Studio - Old
** Using Azure ML Workspace | Azure ML Workspace
** Using HPC on AWS and Azure | HPC on AWS and Azure
** Using AWS SageMaker Endpoint | AWS SageMaker Endpoint
**
* Storage on cloud
** Overview of your Storage on DeepSense | Storage Overview
* FAQ
** Restrictions | Restrictions
** Workarounds | Workarounds
** Best Practices | Best Practices
* Writing Tips
** Mitacs Accelerate Proposals | Mitacs Accelerate Proposals
** Paper Prep | Paper Prep
* DeepSense
** https://deepsense.ca | DeepSense home page
** Acknowledging DeepSense | Acknowledging DeepSense
** Terms of Use | Terms of use
* Self Directed Projects
** Training Projects | Training Projects
* Additional Resources
** Related Links | Related Links
** External Links | External Links
** External Data Sources | External Data Sources

Using AWS SageMaker Studio

2024-10-24T13:51:25Z

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

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.

Resources

2024-09-05T19:07:31Z

PSuthar: /* AWS SageMaker Notebook Instance Resources */

== AWS EC2 Cloud Compute Resources ==
{|class="wikitable" style="text-align: left; color: black; font-style:bold"
|+''' Available accelerated computing instances'''
|-
|'''Instance'''
|'''GPUs'''
|'''vCPU'''
|'''Memory (GiB)'''
|'''GPU Memory (GiB)'''
|'''Instance Storage (GB)'''
|'''Network Performance'''
|'''EBS Bandwidth (Gbps)'''
|-
|g4dn.xlarge
|1
|4
|16
|16
|1 x 125 NVMe SSD
|Up to 25
|Up to 3.5
|-
|g4dn.2xlarge
|1
|8
|32
|16
|1 x 225 NVMe SSD
|Up to 25
|Up to 3.5
|-
|g5.xlarge
|1
|4
|16
|24
|1 x 250 NVMe SSD
|Up to 10
|Up to 3.5
|}

{|class="wikitable" style="text-align: left; color: black; font-style:bold"
|+''' Available compute optimized instances'''
|-
|'''Instance'''
|'''vCPU'''
|'''Memory (GiB)'''
|'''Instance Storage (GB)'''
|'''Network Performance'''
|-
| c5.xlarge
| 4
| 8
| EBS-Only
| Up to 10
|-
| c5.2xlarge
|8
|16
|EBS-Only
|Up to 10
|}

{|class="wikitable" style="text-align: left; color: black; font-style:bold"
|+''' Available general purpose computing instances'''
|-
|'''Instance'''
|'''vCPU'''
|'''Memory (GiB)'''
|'''Instance Storage (GB)'''
|'''Network Performance'''
|-
| m5.xlarge
| 4
| 16
| EBS-Only
| Up to 10
|-
| m5.2xlarge
|8
|32
|EBS-Only
|Up to 10
|}

== AWS SageMaker Notebook Instance Resources ==
{|class="wikitable" style="text-align: left; color: black; font-style:bold"
|+''' Available accelerated computing instances'''
|-
|'''Instance'''
|'''GPUs'''
|'''vCPU'''
|'''Memory (GiB)'''
|'''GPU Memory (GiB)'''
|'''Instance Storage (GB)'''
|'''Network Performance'''
|'''EBS Bandwidth (Gbps)'''
|-
|ml.g4dn.xlarge (fast launch)
|1
|4
|16
|16
|Elastic EBS
|Up to 25
|Up to 3.5
|-
|ml.g4dn.2xlarge
|1
|8
|32
|16
|Elastic EBS
|Up to 25
|Up to 3.5
|-
| m5.xlarge (fast launch)
|No GPU
| 4
| 16
|No GPU
| EBS-Only
| Up to 10
| Up to 3.5
|}

Contact support for more options.

Resources

2024-09-05T19:06:20Z

PSuthar: /* AWS EC2 Cloud Compute Resources */

Resources

2024-09-05T19:01:38Z

PSuthar: /* AWS EC2 Cloud Compute Resources */

== AWS EC2 Cloud Compute Resources ==
{|class="wikitable" style="text-align: left; color: black; font-style:bold"
|+''' Available accelerated computing instances'''
|-
|'''Instance'''
|'''GPUs'''
|'''vCPU'''
|'''Memory (GiB)'''
|'''GPU Memory (GiB)'''
|'''Instance Storage (GB)'''
|'''Network Performance'''
|'''EBS Bandwidth (Gbps)'''
|-
|g4dn.xlarge
|1
|4
|16
|16
|1 x 125 NVMe SSD
|Up to 25
|Up to 3.5
|-
|g4dn.2xlarge
|1
|8
|32
|16
|1 x 225 NVMe SSD
|Up to 25
|Up to 3.5
|-
|g5.xlarge
|1
|4
|16
|24
|1 x 250 NVMe SSD
|Up to 10
|Up to 3.5
|}

{|class="wikitable" style="text-align: left; color: black; font-style:bold"
|+''' Available general purpose computing instances'''
|-
|'''Instance'''
|'''vCPU'''
|'''Memory (GiB)'''
|'''Instance Storage (GB)'''
|'''Network Performance'''
|-
| m5.xlarge
| 4
| 16
| EBS-Only
| Up to 10
|-
| m5.2xlarge
|8
|32
|EBS-Only
|Up to 10
|}

{|class="wikitable" style="text-align: left; color: black; font-style:bold"
|+''' Available compute optimized instances'''
|-
|'''Instance'''
|'''vCPU'''
|'''Memory (GiB)'''
|'''Instance Storage (GB)'''
|'''Network Performance'''
|-
| c5.xlarge
| 4
| 8
| EBS-Only
| Up to 10
|-
| c5.2xlarge
|8
|16
|EBS-Only
|Up to 10
|}

== AWS SageMaker Notebook Instance Resources ==
{|class="wikitable" style="text-align: left; color: black; font-style:bold"
|+''' Available accelerated computing instances'''
|-
|'''Instance'''
|'''GPUs'''
|'''vCPU'''
|'''Memory (GiB)'''
|'''GPU Memory (GiB)'''
|'''Instance Storage (GB)'''
|'''Network Performance'''
|'''EBS Bandwidth (Gbps)'''
|-
|ml.g4dn.xlarge (fast launch)
|1
|4
|16
|16
|Elastic EBS
|Up to 25
|Up to 3.5
|-
|ml.g4dn.2xlarge
|1
|8
|32
|16
|Elastic EBS
|Up to 25
|Up to 3.5
|-
| m5.xlarge (fast launch)
|No GPU
| 4
| 16
|No GPU
| EBS-Only
| Up to 10
| Up to 4,750
|}

Contact support for more options.

Resources

2024-09-05T18:58:35Z

PSuthar: /* AWS EC2 Cloud Compute Resources */

== AWS EC2 Cloud Compute Resources ==
{|class="wikitable" style="text-align: left; color: black; font-style:bold"
|+''' Available accelerated computing instances'''
|-
|'''Instance'''
|'''GPUs'''
|'''vCPU'''
|'''Memory (GiB)'''
|'''GPU Memory (GiB)'''
|'''Instance Storage (GB)'''
|'''Network Performance'''
|'''EBS Bandwidth (Gbps)'''
|-
|g4dn.xlarge
|1
|4
|16
|16
|1 x 125 NVMe SSD
|Up to 25
|Up to 3.5
|-
|g4dn.2xlarge
|1
|8
|32
|16
|1 x 225 NVMe SSD
|Up to 25
|Up to 3.5
|-
|g5.xlarge
|1
|4
|16
|24
|1 x 250 NVMe SSD
|Up to 10
|Up to 3.5
|}

{|class="wikitable" style="text-align: left; color: black; font-style:bold"
|+''' Available general purpose computing instances'''
|-
|'''Instance'''
|'''vCPU'''
|'''Memory (GiB)'''
|'''Instance Storage (GB)'''
|'''Network Performance'''
|-
| m5.xlarge
| 4
| 16
| EBS-Only
| Up to 10
|-
| m5.2xlarge
|8
|32
|EBS-Only
|Up to 10
|}

== AWS SageMaker Notebook Instance Resources ==
{|class="wikitable" style="text-align: left; color: black; font-style:bold"
|+''' Available accelerated computing instances'''
|-
|'''Instance'''
|'''GPUs'''
|'''vCPU'''
|'''Memory (GiB)'''
|'''GPU Memory (GiB)'''
|'''Instance Storage (GB)'''
|'''Network Performance'''
|'''EBS Bandwidth (Gbps)'''
|-
|ml.g4dn.xlarge (fast launch)
|1
|4
|16
|16
|Elastic EBS
|Up to 25
|Up to 3.5
|-
|ml.g4dn.2xlarge
|1
|8
|32
|16
|Elastic EBS
|Up to 25
|Up to 3.5
|-
| m5.xlarge (fast launch)
|No GPU
| 4
| 16
|No GPU
| EBS-Only
| Up to 10
| Up to 4,750
|}

Contact support for more options.

Using AWS SageMaker Studio

2024-09-04T17:26:55Z

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

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.
*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.

Using AWS SageMaker Endpoint

2024-08-14T18:51:38Z

PSuthar: Final edit

Amazon SageMaker is a managed ML service that helps you build and train models and then deploy them into a production-ready hosted environment.

== What is SageMaker Endpoint? ==
Amazon SageMaker is a fully managed machine learning service that enables data scientists and developers to build, train, and deploy machine learning models at scale. One of the key features of SageMaker is the ability to deploy machine learning models as endpoints, which can be invoked to make predictions on new data. Real-time inference is ideal for inference workloads where you have real-time, interactive, low latency requirements. You can deploy your model to SageMaker hosting services and get an endpoint that can be used for inference. These endpoints are fully managed and support autoscaling.

== Prerequisite for setting up SageMaker Endpoint (Model Artifact)==
* After you are done with the modeling phase you can store the trained model on your project allocated s3 bucket.
* Open the SageMaker console and open "Inference" on left panel and click on “Create model".
* Give your model a name and choose the appropriate container image for your model.
* Specify the location of your trained model artifacts (e.g., S3 bucket).
* Specify any additional configuration settings (e.g., instance type, number of instances).

== Setting up a SageMaker Endpoint ==
* Open the SageMaker console and open "Inference" on left panel and click on “Create endpoint”.
* Give your endpoint a name and choose the appropriate configuration settings (e.g., instance type, number of instances).
* Select the model you created in the previous step.
* Click on “Create endpoint”. Your SageMaker endpoint is now ready to be invoked.

== Invoking the endpoint ==
* For invoking the endpoint use your notebook to
* Use this code in your notebook to invoke an endpoint.
* Invoke the endpoint using the `invoke_endpoint` method of the SageMaker runtime client object.
<code> response = runtime.invoke_endpoint(EndpointName='<your-endpoint-name>', ContentType='application/json', Body=json.dumps(input_data)) </code>
* You can parse the input and output as per your convenience.

Remember: It's crucial to delete or scale down your endpoint when not actively in use to avoid unnecessary charges.

Using AWS SageMaker Endpoint

2024-08-13T19:13:47Z

PSuthar:

Amazon SageMaker is a managed ML service that helps you build and train models and then deploy them into a production-ready hosted environment.

== What is SageMaker Endpoint? ==
Amazon SageMaker is a fully managed machine learning service that enables data scientists and developers to build, train, and deploy machine learning models at scale. One of the key features of SageMaker is the ability to deploy machine learning models as endpoints, which can be invoked to make predictions on new data. Real-time inference is ideal for inference workloads where you have real-time, interactive, low latency requirements. You can deploy your model to SageMaker hosting services and get an endpoint that can be used for inference. These endpoints are fully managed and support autoscaling.

== Prerequisite for setting up SageMaker Endpoint (Model Artifact)==
* After you are done with the modeling phase you can store the trained model on your project allocated s3 bucket.
* Open the SageMaker console and open "Inference" on left panel and click on “Create model".
* Give your model a name and choose the appropriate container image for your model.
* Specify the location of your trained model artifacts (e.g., S3 bucket).
* Specify any additional configuration settings (e.g., instance type, number of instances).

== Setting up a SageMaker Endpoint ==
* Open the SageMaker console and open "Inference" on left panel and click on “Create endpoint”.
* Give your endpoint a name and choose the appropriate configuration settings (e.g., instance type, number of instances).
* Select the model you created in the previous step.
* Click on “Create endpoint”. Your SageMaker endpoint is now ready to be invoked.

Remember: It's crucial to delete or scale down your endpoint when not actively in use to avoid unnecessary charges.

Using AWS SageMaker Endpoint

2024-08-13T19:11:58Z

PSuthar:

Amazon SageMaker is a managed ML service that helps you build and train models and then deploy them into a production-ready hosted environment.

== What is SageMaker Endpoint? ==
Amazon SageMaker is a fully managed machine learning service that enables data scientists and developers to build, train, and deploy machine learning models at scale. One of the key features of SageMaker is the ability to deploy machine learning models as endpoints, which can be invoked to make predictions on new data. Real-time inference is ideal for inference workloads where you have real-time, interactive, low latency requirements. You can deploy your model to SageMaker hosting services and get an endpoint that can be used for inference. These endpoints are fully managed and support autoscaling.

=== Prerequisite for setting up SageMaker Endpoint (Model Artifact)===
* After you are done with the modeling phase you can store the trained model on your project allocated s3 bucket.
* Open the SageMaker console and open "Inference" on left panel and click on “Create model".
* Give your model a name and choose the appropriate container image for your model.
* Specify the location of your trained model artifacts (e.g., S3 bucket).
* Specify any additional configuration settings (e.g., instance type, number of instances).

=== Setting up a SageMaker Endpoint ===
* Open the SageMaker console and open "Inference" on left panel and click on “Create endpoint”.
* Give your endpoint a name and choose the appropriate configuration settings (e.g., instance type, number of instances).
* Select the model you created in the previous step.
* Click on “Create endpoint”. Your SageMaker endpoint is now ready to be invoked.

Remember: It's crucial to delete or scale down your endpoint when not actively in use to avoid unnecessary charges.

Using AWS SageMaker Endpoint

2024-08-13T19:04:02Z

PSuthar: Add sagemaker endpoint initial content

Amazon SageMaker is a managed ML service that helps you build and train models and then deploy them into a production-ready hosted environment.

== What is SageMaker Endpoint? ==
Amazon SageMaker is a fully managed machine learning service that enables data scientists and developers to build, train, and deploy machine learning models at scale. One of the key features of SageMaker is the ability to deploy machine learning models as endpoints, which can be invoked to make predictions on new data. Real-time inference is ideal for inference workloads where you have real-time, interactive, low latency requirements. You can deploy your model to SageMaker hosting services and get an endpoint that can be used for inference. These endpoints are fully managed and support autoscaling.

=== Prerequisite for setting up SageMaker Endpoint (Model Artifact)===
* After you are done with the modeling phase you can store the trained model on your project allocated s3 bucket.
* Open the SageMaker console and open "Inference" on left panel and click on “Create model".
* Give your model a name and choose the appropriate container image for your model.
* Specify the location of your trained model artifacts (e.g., S3 bucket).
* Specify any additional configuration settings (e.g., instance type, number of instances).

=== Setting up a SageMaker Endpoint ===
* Open the SageMaker console and open "Inference" on left panel and click on “Create endpoint”.
* Give your endpoint a name and choose the appropriate configuration settings (e.g., instance type, number of instances).
* Select the model you created in the previous step.
* Click on “Create endpoint”. Your SageMaker endpoint is now ready to be invoked.