User Guide - Revision history

PSuthar at 19:54, 24 April 2024

2024-04-24T19:54:27Z

PSuthar: /* Using HPC Platforms on AWS and Azure */

2024-04-24T19:34:29Z

‎Using HPC Platforms on AWS and Azure

PSuthar: Add HPC guide link

2024-04-24T19:32:39Z

Add HPC guide link

Lyang at 22:59, 12 March 2023

2023-03-12T22:59:52Z

Lyang at 22:59, 12 March 2023

2023-03-12T22:59:04Z

Lyang at 22:57, 12 March 2023

2023-03-12T22:57:19Z

Lyang at 19:44, 12 March 2023

2023-03-12T19:44:02Z

PSuthar: /* Amazon Web Services (AWS) */

2023-02-24T21:52:20Z

‎Amazon Web Services (AWS)

PSuthar: Update cloud resources and delete cluster information

2023-02-24T21:51:02Z

Update cloud resources and delete cluster information

New page

== Amazon Web Services (AWS) ==
* The cloud administrator will provide you with an IAM User account of AWS.
* Based on the project requirements, you can select available resources from the table.
* Access the account by following the instructions given here.
* For S3 storage, an S3 bucket will be provided if needed to store project data.

== Google Cloud Platform (GCP) ==
* Google HPC cloud toolkit still under development for DeepSense.

@@ Line 8: / Line 8: @@
 . DeepSense will set up the environment according to your requirement and create an IAM account for you in the cloud.<br/>
 . DeepSense will notify you when the set up is finished and provide detailed instructions how to develop your projects. Using cloud's Platform as a Service (PAAS), you only need to provide your code to train your models or process your data.<br/>
-. According to the specific environment set up for you, please select corresponding instructions below to start developing your projects. You can also find the instructions in the section "[[How to develop Machine Learning projects on cloud]]" in the navigation menu on the left side of this page.<br/>
+. According to the specific environment set up for you, please select corresponding instructions below to start developing your projects. You can also find the instructions in the section '''Onboarding and using cloud services''' in the navigation menu on the left side of this page.<br/>
-== Using Cloud Services (Sagemaker, ML Workspace, etc.)==
+== Using Cloud Services (SageMaker Studio, ML Workspace, etc.)==
 For streamlined development, deployment, and management of your machine learning models, we leverage cloud-based services that offer a comprehensive suite of tools and infrastructure.
-Please follow the instructions at "[[Using AWS Sagemaker]]", "[[Using AWS SageMaker Studio]]", "[[Using Azure ML Workspace]]".
+Please follow the instructions at "[[Using AWS SageMaker Studio]]", "[[Using Azure ML Workspace]]".
 == Using HPC Platforms on AWS and Azure ==

← Older revision		Revision as of 19:34, 24 April 2024
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	We're moving beyond traditional virtual machines to unlock the full potential of cloud-based High-Performance Computing (HPC). Enables you to working with demanding workloads that require significant computational power, such as simulations, complex modeling, large-scale data analysis, or AI/ML tasks, HPC platforms on AWS and Azure provide the resources you need.		We're moving beyond traditional virtual machines to unlock the full potential of cloud-based High-Performance Computing (HPC). Enables you to working with demanding workloads that require significant computational power, such as simulations, complex modeling, large-scale data analysis, or AI/ML tasks, HPC platforms on AWS and Azure provide the resources you need.

−	Please follow the instructions at "[[Using HPC]]".	+	Please follow the instructions at "[[Using HPC on AWS and Azure]]".

@@ Line 10: / Line 10: @@
 . According to the specific environment set up for you, please select corresponding instructions below to start developing your projects. You can also find the instructions in the section "[[How to develop Machine Learning projects on cloud]]" in the navigation menu on the left side of this page.<br/>
-== Using AWS Sagemaker==
+== Using Cloud Services (Sagemaker, ML Workspace, etc.)==
-Please follow the instructions at "[[Using AWS Sagemaker]]".
+For streamlined development, deployment, and management of your machine learning models, we leverage cloud-based services that offer a comprehensive suite of tools and infrastructure.
-== Using AWS EC2 Virtual Machine ==
+Please follow the instructions at "[[Using AWS Sagemaker]]", "[[Using AWS SageMaker Studio]]", "[[Using Azure ML Workspace]]".
-We are configuring GCP Colab, High-Performance Computing clusters on GCP and AWS, and data processing pipeline on GCP and AWS. So more cloud computing platforms will be coming soon. We will keep you updated.
+== Using HPC Platforms on AWS and Azure ==

← Older revision		Revision as of 22:59, 12 March 2023
Line 16:		Line 16:
	Please follow the instructions at "[[Using AWS EC2]]".		Please follow the instructions at "[[Using AWS EC2]]".

−	We are configuring GCP Colab, High-Performance Computing clusters on GCP and AWS, and data processing pipeline on GCP and AWS. We will keep you updated.	+	We are configuring GCP Colab, High-Performance Computing clusters on GCP and AWS, and data processing pipeline on GCP and AWS. So more cloud computing platforms will be coming soon. We will keep you updated.

@@ Line 14: / Line 14: @@
 == Using AWS EC2 Virtual Machine ==
-Please following the instructions at "[[Using AWS EC2]]".
+Please follow the instructions at "[[Using AWS EC2]]".
 We are configuring GCP Colab, High-Performance Computing clusters on GCP and AWS, and data processing pipeline on GCP and AWS. We will keep you updated.

@@ Line 1: / Line 1: @@
-Welcome to the DeepSense cloud computing user guide. You don't have to have background of cloud computing, but it is helpful if you can have a 5 minute read of our wiki page "[[Introduction to Cloud Computing]]".
+Welcome to the DeepSense cloud computing user guide. You don't have to have background of cloud computing, but it is helpful if you spend 5 minutes reading our wiki page "[[Introduction to Cloud Computing]]".
-Unlike using on-premises systems, you don't have to have a Linux user account to ssh into a Linux boxes unless your workload would need a virtual machine. What you will have are IAM accounts on AWS, GCP, or Azure. DeepSense will setup virtual machines, severless Machine Learning compute platform, data processing pipeline, and unlimited data storage for users.
+Unlike using on-premises systems, you don't have to have a Linux user account to ssh into a Linux boxes unless your workloads have to deploy to a virtual machine. What you will have are Identity and Access Management(IAM) accounts on AWS, GCP, or Azure. DeepSense will setup virtual machines, severless Machine Learning compute platform, data processing pipeline, and unlimited data storage for users. We will provide step by step guidance to help you develop your Machine Learning or data processing projects in cloud. You don't have to stick to any specific cloud. DeepSense will help you find the best solution to make your project development process straightforward in cloud.
-== Amazon Web Services (AWS) ==
+== Using AWS Sagemaker==
-* The cloud administrator will provide you with an IAM User account of AWS.
+Please follow the instructions at "[[Using AWS Sagemaker]]".
-== Google Cloud Platform (GCP) ==
+== Using AWS EC2 Virtual Machine ==
-* Google HPC cloud toolkit still under development for DeepSense.
+Please following the instructions at "[[Using AWS EC2]]".

← Older revision		Revision as of 19:44, 12 March 2023
Line 1:		Line 1:
		+	Welcome to the DeepSense cloud computing user guide. You don't have to have background of cloud computing, but it is helpful if you can have a 5 minute read of our wiki page "[[Introduction to Cloud Computing]]".
		+
		+	Unlike using on-premises systems, you don't have to have a Linux user account to ssh into a Linux boxes unless your workload would need a virtual machine. What you will have are IAM accounts on AWS, GCP, or Azure. DeepSense will setup virtual machines, severless Machine Learning compute platform, data processing pipeline, and unlimited data storage for users.
		+
		+
	== Amazon Web Services (AWS) ==		== Amazon Web Services (AWS) ==
	* The cloud administrator will provide you with an IAM User account of AWS.		* The cloud administrator will provide you with an IAM User account of AWS.

@@ Line 1: / Line 1: @@
 == Amazon Web Services (AWS) ==
 * The cloud administrator will provide you with an IAM User account of AWS.
-* Based on the project requirements, you can select available resources from the table.
+* Based on the project requirements, you can select available [[Resources]] from the table.
 * Access the account by following the instructions given here.
 * For S3 storage, an S3 bucket will be provided if needed to store project data.