Organiser udvikling af maskinlæring ved hjælp af delte rum i SageMaker Studio for samarbejde i realtid

Amazon SageMaker Studio er det første fuldt integrerede udviklingsmiljø (IDE) til maskinlæring (ML). Det giver en enkelt, webbaseret visuel grænseflade, hvor du kan udføre alle ML-udviklingstrin, inklusive forberedelse af data og opbygning, træning og implementering af modeller.

Inden for en Amazon SageMaker-domæne, users can provision a personal Amazon SageMaker Studio IDE application, which runs a free JupyterServer with built‑in integrations to examine Amazon SageMaker-eksperimenter, orchestrate Amazon SageMaker Pipelines, and much more. Users only pay for the flexible compute on their notebook kernels. These personal applications automatically mount a respective user’s private Amazon Elastic File System (Amazon EFS) home directory so they can keep code, data, and other files isolated from other users. Amazon SageMaker Studio already supports sharing of notebooks between private applications, but the asynchronous mechanism can slow down the iteration process.

Nu med delte rum i Amazon SageMaker Studio, users can organize collaborative ML endeavors and initiatives by creating a shared IDE application that users utilize with their own Amazon SageMaker user profile. Data workers collaborating in a shared space get access to an Amazon SageMaker Studio environment where they can access, read, edit, and share their notebooks in real time, which gives them the quickest path to start iterating with their peers on new ideas. Data workers can even collaborate on the same notebook concurrently using real-time collaboration capabilities. The notebook indicates each co-editing user with a different cursor that shows their respective user profile name.

Shared spaces in SageMaker Studio automatically tag resources, such as Training jobs, Processing jobs, Experiments, Pipelines, and Model Registry entries created within the scope of a workspace with their respective sagemaker:space-arn. The space filters those resources within the Amazon SageMaker Studio user interface (UI) so users are only presented with SageMaker Experiments, Pipelines, and other resources that are pertinent to their ML endeavor.

Løsningsoversigt

Since shared spaces automatically tags resources, administrators can easily monitor costs associated with an ML endeavor and plan budgets using tools such as AWS budgetter , AWS Cost Explorer. As an administrator you’ll only need to attach a omkostningsfordelingsmærke forum sagemaker:space-arn.

Once that’s complete, you can use AWS Cost Explorer to identify how much individual ML projects are costing your organization.

Get started with shared spaces in Amazon SageMaker Studio

In this section, we’ll analyze the typical workflow for creating and utilizing shared spaces in Amazon SageMaker Studio.

Create a shared space in Amazon SageMaker Studio

You can use the Amazon SageMaker Console or the AWS kommandolinjegrænseflade (AWS CLI) to add support for spaces to an existing domain. For the most up to date information, please check Create a shared space. Shared spaces only work with a JupyterLab 3 SageMaker Studio image and for SageMaker Domains using AWS Identity and Access Management (AWS IAM) authentication.

Console creation

To create a space within a designated Amazon SageMaker Domain, you’ll first need to set a designated space default execution role. From the Domæneoplysninger side, vælg Domæneindstillinger Fanebladet og vælg Redigere. Then you can set a space default execution role, which only needs to be completed once per Domain, as shown in the following diagram:

Derefter kan du gå til Rumstyring tab within your domain and select the Opret button, as shown in the following diagram:

AWS CLI creation

You can also set a default Domain space execution role from the AWS CLI. In order to determine your region’s JupyterLab3 image ARN, check Setting a default JupyterLab version.

aws --region  
sagemaker update-domain 
--domain-id  
--default-space-settings "ExecutionRole="

Once that’s been completed for your Domain, you can create a shared space from the CLI.

aws --region  
sagemaker create-space 
--domain-id  
--space-name  

Launch a shared space in Amazon SageMaker Studio

Users can launch a shared space by selecting the Launch button next to their user profile within the AWS Console for their Amazon SageMaker Domain.

Efter at have valgt Spaces under the Collaborative section, then select which Space to launch:

Alternatively, users can generate a pre-signed URL to launch a space through the AWS CLI:

aws sagemaker create-presigned-domain-url 
--region  
--domain-id  
--space-name  
--user-profile-name  

Samarbejde i realtid

Once the Amazon SageMaker Studio shared space IDE has been loaded, users can select the Samarbejdspartnere tab on the left panel to see which users are actively working in your space and on what notebook. If more than one person is working on the same notebook, then you’ll see a cursor with the other user’s profile name where they are editing:

In the following screenshot, you can see the different user experiences for someone editing and viewing the same notebook:

Konklusion

In this post, we showed you how shared spaces in SageMaker Studio adds a real-time collaborative IDE experience to Amazon SageMaker Studio. Automated tagging helps users scope and filter their Amazon SageMaker resources, which includes: experiments, pipelines, and model registry entries to maximize user productivity. Additionally, administrators can use these applied tags to monitor the costs associated with a given space and set appropriate budgets using AWS Cost Explorer and AWS Budgets.

Accelerate your team’s collaboration today by setting up shared spaces in Amazon SageMaker Studio for your specific machine learning endeavors!

Om forfatterne

Sean Morgan er AI/ML Solutions Architect hos AWS. Han har erfaring inden for halvleder- og akademisk forskning og bruger sin erfaring til at hjælpe kunder med at nå deres mål på AWS. I sin fritid er Sean en aktiv open source-bidragyder/vedligeholder og er specialinteressegruppeleder for TensorFlow Add-ons.

Han Zhang er senior softwareingeniør hos Amazon Web Services. Hun er en del af lanceringsteamet for Amazon SageMaker Notebooks og Amazon SageMaker Studio og har fokuseret på at bygge sikre maskinlæringsmiljøer for kunderne. I sin fritid nyder hun at vandre og stå på ski i Pacific Northwest.

Arkaprava De er senior softwareingeniør hos AWS. Han har været hos Amazon i over 7 år og arbejder i øjeblikket på at forbedre Amazon SageMaker Studio IDE-oplevelsen. Du kan finde ham på LinkedIn.

Kunal Jha er Senior Product Manager hos AWS. Han er fokuseret på at bygge Amazon SageMaker Studio som den foretrukne IDE for alle ML-udviklingstrin. I sin fritid nyder Kunal at stå på ski og udforske det nordvestlige Stillehav. Du kan finde ham på LinkedIn.