aws-samples · shadhav · Apr 29, 2023 · Apr 10, 2023 · Apr 10, 2023 · Apr 10, 2023
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+# Streamlining ML and DS development with Amazon SageMaker spaces
+
+Amazon SageMaker Spaces is a feature that allows users to collaborate on machine learning projects in a secure and isolated environment. With SageMaker Spaces, users can create shared workspaces where they can share notebooks, files, experiments, and models with other users. Users can also control access to these resources, so they can be sure that their data is safe.
+
+SageMaker Spaces is a great way to accelerate the machine learning development process. By sharing resources with other users, users can get feedback on their work quickly and easily. Users can also collaborate on experiments and models, which can help them to improve the quality of their results.
+
+To get started with SageMaker Spaces, users first need to create a domain. A domain is a collection of shared workspaces. Once users have created a domain, they can create shared workspaces within it. To create a shared workspace, users need to specify a name for the workspace and a list of users who will have access to it.
+
+Once users have created a shared workspace, they can start sharing resources with other users. To share a notebook, file, experiment, or model, users need to select the resource and then click the "Share" button. In the "Share with" dialog box, users can select the users who they want to share the resource with.
+
+Users can also control access to resources by using IAM roles. When users create a shared workspace, they can specify an IAM role that will be used by default for all resources in the workspace. Users can also specify individual IAM roles for specific resources.
+
+SageMaker Spaces is a powerful new feature that can help users to collaborate on machine learning projects more effectively. By sharing resources with other users, users can get feedback on their work quickly and easily. Users can also collaborate on experiments and models, which can help them to improve the quality of their results.
+
+Here are some of the benefits of using Amazon SageMaker Spaces:
+* Collaboration: SageMaker Spaces makes it easy for users to collaborate on machine learning projects. Users can share notebooks, files, experiments, and models with other users in a secure and isolated environment.
+* Security: SageMaker Spaces provides a high level of security for user data. Users can control who has access to their resources and they can revoke access at any time.
+* Scalability: SageMaker Spaces is designed to scale to meet the needs of the user's team. Users can create as many shared workspaces as they need and they can add users to these workspaces as needed.
+* Cost-effectiveness: SageMaker Spaces is a cost-effective way for users to collaborate on machine learning projects. Users only pay for the resources that they use.
+If users are looking for a way to collaborate on machine learning projects, then Amazon SageMaker Spaces is a great option. It is secure, scalable, and cost-effective.
+
+
+This workshop consists of following modules:
+ - [AWS CodeCommit version control](./SETTING_UP.md#aws-codecommit-version-control)
+ - [IAM Policy to access the CodeCommit repository](./SETTING_UP.md#iam-policy-to-access-the-codecommit-repository)
+ - [SageMaker Spaces](./SETTING_UP.md#sagemaker-spaces)
+ - [Conclusion](./SETTING_UP.md#conclusion)
+ - [References](./SETTING_UP.md#references)
+
+
+## Getting Started
+[Setting up the environment and loading the notebooks in Sagemaker spaces](./SETTING_UP.md)
+
+## Security
+
+See [CONTRIBUTING](../CONTRIBUTING.md#security-issue-notifications) for more information.
+
+## License
+
+This library is licensed under the MIT-0 License. See the LICENSE file.
+
diff --git a/Sagemaker-Spaces-management/SETTING_UP.md b/Sagemaker-Spaces-management/SETTING_UP.md
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+
+## Amazon SageMaker Spaces configuration
+
+To get started, you first need to create a source code repository
+
+## AWS CodeCommit version control
+* In the web browser, log in with your AWS credentials to the AWS Management console at https://console.aws.amazon.com/
+* You can search for 'code' in the search bar and select 'CodeCommit'
+![Image](./images/codecommit-search.png)
+* When you go to the CodeCommit page, click on the 'Create repository' button.
+![Image](./images/codecommit-repos.png)
+* Enter a name for your repository and click on the 'Create' button at the bottom right corner.
+![Image](./images/codecommit-create.png)
+* AWS CodeCommit repository has been successfully created. Example: https://git-codecommit.us-east-1.amazonaws.com/v1/repos/spaces-repo 
+![Image](./images/codecommit-spaces.png)
+
+## IAM Policy to access the CodeCommit repository
+To create an IAM policy to access the CodeCommit repository, follow these steps:
+* In the AWS Management console and navigate to the IAM service.
+![Image](./images/console-iam.png)
+* Click on the 'Policies' link in the left-hand menu.
+![Image](./images/iam-leftnav.png)
+* Click on the 'Create policy' button.
+![Image](./images/iam-createpolicy.png)
+* Select the 'JSON' tab and enter the following policy document, replacing '123456789012' with the Account information of your CodeCommit repository.
+``````json
+{
+    "Version": "2012-10-17",
+    "Statement": [
+        {
+            "Sid": "CodeCommitPolicy",
+            "Effect": "Allow",
+            "Action": "codecommit:*",
+            "Resource": "arn:aws:codecommit:us-east-1:123456789012:spaces-repo"
+        }
+    ]
+}
+``````
+* Click on the 'Review policy' button.
+![Image](./images/iam-reviewpolicy.png)
+* Enter a name for your policy and click on the 'Create policy' button.
+![Image](./images/iam-cc-policy.png)
+
+
+## SageMaker Spaces
+Create a SageMaker domain, follow these steps:
+* Log in to the AWS Management Console and navigate to the SageMaker service.
+![Image](./images/console-sagemaker.png)
+* Click on the 'Create domain' button.
+![Image](./images/sm-createdomain.png)
+* Enter a name for your domain, user profile name, and execution role. Click on the "Submit" button.
+![Image](./images/sm-domaindetails.png)
+* The successfully created domain is displayed.
+![Image](./images/sm-domain.png)
+* You can view the recently created domain by selecting it and clicking 'View' button.
+![Image](./images/sm-domainview.png)
+* Create another user 'user2' by clicking 'Add user' button. Assign appropriate execution role 'AmazonSageMakerServiceCatalogProductsExecutionRole'.
+![Image](./images/sm-domain-users.png)
+* Select the user profile from the dropdown of 'user1' and then select Collabrative 'Spaces'
+![Image](./images/sm-domain-user1.png)
+* Creating the Jupyter notebook and launches the Amazon Sagemaker studio 
+* In Studio toolbar, go to File >> New >> Terminal launches CLI.
+* Check out the codecommit url using Git CLI command
+![Image](./images/sm-domain-user1-notebook.png)
+* Create Notebook file 'HelloWorld.ipynb' and commit to CodeCommit
+![Image](./images/sm-domain-user1-codecommit.png)
+* Launch user2 in new browser window using Collabrative Spaces.
+* Both users (user1 and user2) can expeiences for someone editing the notebook
+![Image](./images/users-collaboration.gif)
+
+
+## Conclusion
+In this post, I've shown how to use Amazon SageMaker spaces to organize your ML resources, collaborate with team members, and streamline ML workflow. I've also demostrated how to use AWS CodeCommit repositories to version control using ML code.
+
+
+## References
+ - [Onboard to Amazon SageMaker Domain Using Quick setup](https://docs.aws.amazon.com/sagemaker/latest/dg/onboard-quick-start.html)
+ - [How to manage Amazon SageMaker code with AWS CodeCommit](https://aws.amazon.com/blogs/publicsector/how-to-manage-amazon-sagemaker-code-aws-codecommit/)
+ - [AWS Policy Generator](https://awspolicygen.s3.amazonaws.com/policygen.html)
+ - [Collaborate with shared spaces](https://docs.aws.amazon.com/sagemaker/latest/dg/domain-space.html)
+ - [Organize machine learning development using shared spaces in SageMaker Studio for real-time collaboration](https://aws.amazon.com/blogs/machine-learning/organize-machine-learning-development-using-shared-spaces-in-sagemaker-studio-for-real-time-collaboration/)
+
+
+
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