Tag Archives: launch

AWS CodeBuild for macOS adds support for Fastlane

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/codebuild-for-macos-adds-support-for-fastlane/

I’m pleased to announce the availability of Fastlane in your AWS CodeBuild for macOS environments. AWS CodeBuild is a fully managed continuous integration service that compiles source code, runs tests, and produces ready-to-deploy software packages.

Fastlane is an open source tool suite designed to automate various aspects of mobile application development. It provides mobile application developers with a centralized set of tools to manage tasks such as code signing, screenshot generation, beta distribution, and app store submissions. It integrates with popular continuous integration and continuous deployment (CI/CD) platforms and supports both iOS and Android development workflows. Although Fastlane offers significant automation capabilities, developers may encounter challenges during its setup and maintenance. Configuring Fastlane can be complex, particularly for teams unfamiliar with the syntax and package management system of Ruby. Keeping Fastlane and its dependencies up to date requires ongoing effort, because updates to mobile platforms or third-party services may necessitate adjustments to existing workflows.

When we introduced CodeBuild for macOS in August 2024, we knew that one of your challenges was to install and maintain Fastlane in your build environment. Although it was possible to manually install Fastlane in a custom build environment, at AWS, we remove the undifferentiated heaving lifting from your infrastructure so you can spend more time on the aspects that matter for your business. Starting today, Fastlane is installed by default, and you can use the familiar command fastlane buildin your buildspec.yaml file.

Fastlane and code signing
To distribute an application on the App Store, developers must sign their binary with a private key generated on the Apple Developer portal. This private key, along with the certificate that validates it, must be accessible during the build process. This can be a challenge for development teams because they need to share the development private key (which allows deployment on selected test devices) among team members. Additionally, the distribution private key (which enables publishing on the App Store) must be available during the signing process before uploading the binary to the App Store.

Fastlane is a versatile build system in that it also helps developers with the management of development and distribution keys and certificates. Developers can use fastlane match to share signing materials in a team and make them securely and easily accessible on individual developers’ machines and on the CI environment. match allows the storage of private keys, the certificates, and the mobile provisioning profiles on a secured share storage. It makes sure that the local build environment, whether it’s a developer laptop or a server machine in the cloud, stays in sync with the shared storage. At build time, it securely downloads the required certificates to sign your app and configures the build machine to allow the codesign utility to pick them up.

match allows the sharing of signing secrets through GitHub, GitLab, Google Cloud Storage, Azure DevOps, and Amazon Simple Storage Service (Amazon S3).

If you already use one of these and you’re migrating your projects to CodeBuild, you don’t have much to do. You only need to make sure your CodeBuild build environment has access to the shared storage (see step 3 in the demo).

Let’s see how it works
If you’re new to Fastlane or CodeBuild, let’s see how it works.

For this demo, I start with an existing iOS project. The project is already configured to be built on CodeBuild. You can refer to my previous blog post, Add macOS to your continuous integration pipelines with AWS CodeBuild, to learn more details.

I’ll show you how to get started in three steps:

  • Import your existing signing materials to a shared private GitHub repository
  • Configure fastlane to build and sign your project
  • Use fastlanewith CodeBuild

Step 1: Import your signing materials

Most of the fastlane documentation I read explains how to create a new key pair and a new certificate to get started. Although this is certainly true for new projects, in real life, you probably already have your project and your signing keys. So, the first step is to import these existing signing materials.

Apple App Store uses different keys and certificates for development and distribution (there are also ad hoc and enterprise certificates, but these are outside the scope of this post). You must have three files for each usage (that’s a total of six files):

  • A .mobileprovision file that you can create and download from the Apple developer console. The provisioning profile links your identity, the app identity, and the entitlements the app might have.
  • A .cer file, which is the certificate emitted by Apple to validate your private key. You can download this from the Apple Developer portal. Select the certificate, then select Download.
  • A .p12 file, which contains your private key. You can download the key when you create it in the Apple Developer portal. If you didn’t download it but have it on your machine, you can export it from the Apple Keychain app. Note that the KeyChain.app is hidden in macOS 15.x. You can open it with open /System/Library/CoreServices/Applications/Keychain\ Access.app. Select the key you want to export and right click to select Export.
Export p12 files from Keychain

When you have these files, create a fastlane/Matchfile file with the following content:

git_url("https://github.com/sebsto/secret.git")
storage_mode("git")
type("development")
# or use appstore to use the distribution signing key and certificate
# type("appstore")

Be sure to replace the URL of your GitHub repository and make sure this repository is private. It will serve as a storage for your signing key and certificate.

Then, I import my existing files with the fastlane match import --type appstore command. I repeat the command for each environment: appstore and development.

The very first time, fastlane prompts me for my Apple Id username and password. It connects to App Store Connect to verify the validity of the certificates or to create new ones when necessary. The session cookie is stored in ~/.fastlane/spaceship/<your apple user id>/cookie.

fastlane match also asks for a password. It uses this password to generate a key to crypt the signing materials on the storage. Don’t forget this password because it will be used at build time to import the signing materials on the build machine.

Here is the command and its output in full:

 fastlane match import --type appstore

[✔] 🚀
[16:43:54]: Successfully loaded '~/amplify-ios-getting-started/code/fastlane/Matchfile' 📄

+-----------------------------------------------------+
| Detected Values from './fastlane/Matchfile'         |
+--------------+--------------------------------------+
| git_url.     | https://github.com/sebsto/secret.git |
| storage_mode | git                                  |
| type         | development                          |
+--------------+--------------------------------------+

[16:43:54]: Certificate (.cer) path:
./secrets/sebsto-apple-dist.cer
[16:44:07]: Private key (.p12) path:
./secrets/sebsto-apple-dist.p12
[16:44:12]: Provisioning profile (.mobileprovision or .provisionprofile) path or leave empty to skip
this file:
./secrets/amplifyiosgettingstarteddist.mobileprovision
[16:44:25]: Cloning remote git repo...
[16:44:25]: If cloning the repo takes too long, you can use the `clone_branch_directly` option in match.
[16:44:27]: Checking out branch master...
[16:44:27]: Enter the passphrase that should be used to encrypt/decrypt your certificates
[16:44:27]: This passphrase is specific per repository and will be stored in your local keychain
[16:44:27]: Make sure to remember the password, as you'll need it when you run match on a different machine
[16:44:27]: Passphrase for Match storage: ********
[16:44:30]: Type passphrase again: ********
security: SecKeychainAddInternetPassword <NULL>: The specified item already exists in the keychain.
[16:44:31]: 🔓 Successfully decrypted certificates repo
[16:44:31]: Repo is at: '/var/folders/14/nwpsn4b504gfp02_mrbyd2jr0000gr/T/d20250131-41830-z7b4ic'
[16:44:31]: Login to App Store Connect ([email protected])
[16:44:33]: Enter the passphrase that should be used to encrypt/decrypt your certificates
[16:44:33]: This passphrase is specific per repository and will be stored in your local keychain
[16:44:33]: Make sure to remember the password, as you'll need it when you run match on a different machine
[16:44:33]: Passphrase for Match storage: ********
[16:44:37]: Type passphrase again: ********
security: SecKeychainAddInternetPassword <NULL>: The specified item already exists in the keychain.
[16:44:39]: 🔒 Successfully encrypted certificates repo
[16:44:39]: Pushing changes to remote git repo...
[16:44:40]: Finished uploading files to Git Repo [https://github.com/sebsto/secret.git]

I verify that Fastlane imported my signing material to my Git repository.

Fastlane match - github repo after import

I can also configure my local machine to use these signing materials during the next build:

» fastlane match appstore 

[✔] 🚀 
[17:39:08]: Successfully loaded '~/amplify-ios-getting-started/code/fastlane/Matchfile' 📄

+-----------------------------------------------------+
|   Detected Values from './fastlane/Matchfile'       |
+--------------+--------------------------------------+
| git_url      | https://github.com/sebsto/secret.git |
| storage_mode | git                                  |
| type         | development                          |
+--------------+--------------------------------------+


+-------------------------------------------------------------------------------------------+
|                                 Summary for match 2.226.0                                 |
+----------------------------------------+--------------------------------------------------+
| type                                   | appstore                                         |
| readonly                               | false                                            |
| generate_apple_certs                   | true                                             |
| skip_provisioning_profiles             | false                                            |
| app_identifier                         | ["com.amazonaws.amplify.mobile.getting-started"] |
| username                               | xxxx@xxxxxxxxx                                   |
| team_id                                | XXXXXXXXXX                                       |
| storage_mode                           | git                                              |
| git_url                                | https://github.com/sebsto/secret.git             |
| git_branch                             | master                                           |
| shallow_clone                          | false                                            |
| clone_branch_directly                  | false                                            |
| skip_google_cloud_account_confirmation | false                                            |
| s3_skip_encryption                     | false                                            |
| gitlab_host                            | https://gitlab.com                               |
| keychain_name                          | login.keychain                                   |
| force                                  | false                                            |
| force_for_new_devices                  | false                                            |
| include_mac_in_profiles                | false                                            |
| include_all_certificates               | false                                            |
| force_for_new_certificates             | false                                            |
| skip_confirmation                      | false                                            |
| safe_remove_certs                      | false                                            |
| skip_docs                              | false                                            |
| platform                               | ios                                              |
| derive_catalyst_app_identifier         | false                                            |
| fail_on_name_taken                     | false                                            |
| skip_certificate_matching              | false                                            |
| skip_set_partition_list                | false                                            |
| force_legacy_encryption                | false                                            |
| verbose                                | false                                            |
+----------------------------------------+--------------------------------------------------+

[17:39:08]: Cloning remote git repo...
[17:39:08]: If cloning the repo takes too long, you can use the `clone_branch_directly` option in match.
[17:39:10]: Checking out branch master...
[17:39:10]: Enter the passphrase that should be used to encrypt/decrypt your certificates
[17:39:10]: This passphrase is specific per repository and will be stored in your local keychain
[17:39:10]: Make sure to remember the password, as you'll need it when you run match on a different machine
[17:39:10]: Passphrase for Match storage: ********
[17:39:13]: Type passphrase again: ********
security: SecKeychainAddInternetPassword <NULL>: The specified item already exists in the keychain.
[17:39:15]: 🔓  Successfully decrypted certificates repo
[17:39:15]: Verifying that the certificate and profile are still valid on the Dev Portal...
[17:39:17]: Installing certificate...

+-------------------------------------------------------------------------+
|                          Installed Certificate                          |
+-------------------+-----------------------------------------------------+
| User ID           | XXXXXXXXXX                                          |
| Common Name       | Apple Distribution: Sebastien Stormacq (XXXXXXXXXX) |
| Organisation Unit | XXXXXXXXXX                                          |
| Organisation      | Sebastien Stormacq                                  |
| Country           | US                                                  |
| Start Datetime    | 2024-10-29 09:55:43 UTC                             |
| End Datetime      | 2025-10-29 09:55:42 UTC                             |
+-------------------+-----------------------------------------------------+

[17:39:18]: Installing provisioning profile...

+-------------------------------------------------------------------------------------------------------------------+
|                                          Installed Provisioning Profile                                           |
+---------------------+----------------------------------------------+----------------------------------------------+
| Parameter           | Environment Variable                         | Value                                        |
+---------------------+----------------------------------------------+----------------------------------------------+
| App Identifier      |                                              | com.amazonaws.amplify.mobile.getting-starte  |
|                     |                                              | d                                            |
| Type                |                                              | appstore                                     |
| Platform            |                                              | ios                                          |
| Profile UUID        | sigh_com.amazonaws.amplify.mobile.getting-s  | 4e497882-d80f-4684-945a-8bfec1b310b9         |
|                     | tarted_appstore                              |                                              |
| Profile Name        | sigh_com.amazonaws.amplify.mobile.getting-s  | amplify-ios-getting-started-dist             |
|                     | tarted_appstore_profile-name                 |                                              |
| Profile Path        | sigh_com.amazonaws.amplify.mobile.getting-s  | /Users/stormacq/Library/MobileDevice/Provis  |
|                     | tarted_appstore_profile-path                 | ioning                                       |
|                     |                                              | Profiles/4e497882-d80f-4684-945a-8bfec1b310  |
|                     |                                              | b9.mobileprovision                           |
| Development Team ID | sigh_com.amazonaws.amplify.mobile.getting-s  | XXXXXXXXXX                                   |
|                     | tarted_appstore_team-id                      |                                              |
| Certificate Name    | sigh_com.amazonaws.amplify.mobile.getting-s  | Apple Distribution: Sebastien Stormacq       |
|                     | tarted_appstore_certificate-name             | (XXXXXXXXXX)                                 |
+---------------------+----------------------------------------------+----------------------------------------------+

[17:39:18]: All required keys, certificates and provisioning profiles are installed 🙌

Step 2: Configure Fastlane to sign your project

I create a Fastlane build configuration file in fastlane/Fastfile (you can use fastlane init command to get started):

default_platform(:ios)

platform :ios do
  before_all do
    setup_ci
  end

  desc "Build and Sign the binary"
  lane :build do
    match(type: "appstore", readonly: true)
    gym(
      scheme: "getting started",
      export_method: "app-store"
    )
  end
end

Make sure that the setup_ci action is added to the before_all section of Fastfile for the match action to function correctly. This action creates a temporary Fastlane keychain with correct permissions. Without this step, you may encounter build failures or inconsistent results.

And I test a local build with the command fastlane build. I enter the password I used when importing my keys and certificate, then I let the system build and sign my project. When everything is correctly configured, it produces a similar output.

...
[17:58:33]: Successfully exported and compressed dSYM file
[17:58:33]: Successfully exported and signed the ipa file:
[17:58:33]: ~/amplify-ios-getting-started/code/getting started.ipa

+---------------------------------------+
|           fastlane summary            |
+------+------------------+-------------+
| Step | Action           | Time (in s) |
+------+------------------+-------------+
| 1    | default_platform | 0           |
| 2    | setup_ci         | 0           |
| 3    | match            | 36          |
| 4    | gym              | 151         |
+------+------------------+-------------+

[17:58:33]: fastlane.tools finished successfully 🎉

Step 3: Configure CodeBuild to use Fastlane

Next, I create a project on CodeBuild. I’m not going into the step-by-step guide to help you to do so. You can refer to my previous post or to the CodeBuild documentation.

There is just one Fastlane-specific configuration. To access the signing materials, Fastlane requires access to three secret values that I’ll pass as environment variables:

  • MATCH_PASSWORD, the password I entered when importing the signing material. Fastlane uses this password to decipher the encrypted files in the GitHub repository
  • FASTLANE_SESSION, the value of the Apple Id session cookie, located at ~/.fastlane/spaceship/<your apple user id>/cookie. The session is valid from a couple of hours to multiple days. When the session expires, reauthenticate with the command fastlane spaceauth from your laptop and update the value of FASTLANE_SESSION with the new value of the cookie.
  • MATCH_GIT_BASIC_AUTHORIZATION, a base 64 encoding of your GitHub username, followed by a colon, followed by a personal authentication token (PAT) to access your private GitHub repository. You can generate PAT on the GitHub console in Your Profile > Settings > Developers Settings > Personal Access Token. I use this command to generate the value of this environment variable: echo -n my_git_username:my_git_pat | base64.

Note that for each of these three values, I can enter the Amazon Resource Name (ARN) of the secret on AWS Secrets Manager or the plain text value. We strongly recommend using Secrets Manager to store security-sensitive values.

I’m a security-conscious user, so I store the three secrets in Secrets Manager with these commands:

aws --region $REGION secretsmanager create-secret --name /CodeBuild/MATCH_PASSWORD --secret-string MySuperSecretPassword
aws --region $REGION secretsmanager create-secret --name /CodeBuild/FASTLANE_SESSION --secret-string $(cat ~/.fastlane/spaceship/my_appleid_username/cookie)
aws --region $REGION secretsmanager create-secret --name /CodeBuild/MATCH_GIT_BASIC_AUTHORIZATION --secret-string $(echo -n my_git_username:my_git_pat | base64)

If your build project refers to secrets stored in Secrets Manager, the build project’s service role must allow the secretsmanager:GetSecretValue action. If you chose New service role when you created your project, CodeBuild includes this action in the default service role for your build project. However, if you chose Existing service role, you must include this action to your service role separately.

For this demo, I use this AWS Identity and Access Management (IAM) policy:

{
	"Version": "2012-10-17",
	"Statement": [
		{
			"Effect": "Allow",
			"Action": [
				"secretsmanager:GetSecretValue"
			],
			"Resource": [
				"arn:aws:secretsmanager:us-east-2:012345678912:secret:/CodeBuild/*"
			]
		}
	]
}

After I created the project in the CodeBuild section of the AWS Management Console, I enter the three environment variables. Notice that the value is the name of the secret in Secrets Manager.

Codebuild - environment variables for Fastlane match

You can also define the environment variables and their Secrets Manager secret name in your buildpsec.yaml file.

Next, I modify the buildspec.yaml file at the root of my project to use fastlane to build and sign the binary. My buildspec.yaml file now looks like this one:

# buildspec.yml
version: 0.2
phases:
  install:
    commands:
      - code/ci_actions/00_install_rosetta.sh
  pre_build:
    commands:
      - code/ci_actions/02_amplify.sh
  build:
    commands:
      - (cd code && fastlane build)
artifacts:
  name: getting-started-$(date +%Y-%m-%d).ipa
  files:
    - 'getting started.ipa'
  base-directory: 'code'

The Rosetta and Amplify scripts are required to receive the Amplify configuration for the backend. If you don’t use AWS Amplify in your project, you don’t need these.

Notice that there is nothing in the build file that downloads the signing key or prepares the keychain in the build environment; fastlane match will do that for me.

I add the new buildspec.yaml file and my ./fastlane directory to Git. I commit and push these files. git commit -m "add fastlane support" && git push

When everything goes well, I can see the build running on CodeBuild and the Succeeded message.

Codebuild - succeed message

Pricing and availability
Fastlane is now pre-installed at no extra cost on all macOS images that CodeBuild uses, in all Regions where CodeBuild for macOS is available. At the time of this writing, these are US East (Ohio, N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (Frankfurt).

In my experience, it takes a bit of time to configure fastlane match correctly. When it’s configured, having it working on CodeBuild is pretty straightforward. Before trying this on CodeBuild, be sure it works on your local machine. When something goes wrong on CodeBuild, triple-check the values of the environment variables and make sure CodeBuild has access to your secrets on AWS Secrets Manager.

Now go build (on macOS)!

AWS Weekly Roundup: DeepSeek-R1, S3 Metadata, Elastic Beanstalk updates, and more (February 3, 2024)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-deepseek-r1-s3-metadata-elastic-beanstalk-updates-and-more-february-3-2024/

Last week, I had an amazing time attending AWS Community Day Thailand in Bangkok. This event came at an exciting time, following the recent launch of the AWS Asia Pacific (Bangkok) Region. We had over 300 attendees and featured 15 speakers from the community, including an AWS Hero and 4 AWS Community Builders who shared their technical expertise and experiences.

The highlight was definitely Jeff Barr, AWS Vice President & Chief Evangelist, delivering an inspiring keynote titled “Next-Generation Software Development”, which set the perfect tone for the day. The day kicked off with welcoming remarks from Vatsun Thirapatarapong, AWS Country Manager for Thailand, and was made even more special thanks to the tremendous support from both the AWS User Group volunteers and the AWS Thailand team.

Here’s a photo capturing the excitement from the event: 

Last week’s AWS Launches
There are 30+ launches last week and here are some launches that caught my attention:

DeepSeek-R1 models now available on AWS — Channy wrote on how you can now deploy DeepSeek-R1 models in Amazon Bedrock and Amazon SageMaker AI. This helps you to build and scale generative AI applications with minimal infrastructure investment.

Amazon S3 Tables increases table limit to 10,000 per bucket — S3 Tables now supports creating up to 10,000 tables in each table bucket, allowing you to scale up to 100,000 tables across 10 buckets within an AWS Region per account.

Amazon S3 Metadata now generally available — S3 Metadata provides automated and easily queried metadata that updates in near real-time, simplifying business analytics and real-time inference applications. It supports both system-defined and custom metadata, including integration with AWS analytics services.

AWS Amplify adds TypeScript Data client support for Lambda functions — Developers can now use the Amplify Data client within AWS Lambda functions, enabling consistent type-safe data operations across frontend and backend applications.

AWS Elastic Beanstalk adds Python 3.13, .NET 9, and PHP 8.4 support on Amazon Linux 2023 — AWS Elastic Beanstalk brings the latest language features and improvements to application deployments while benefiting from Amazon Linux 2023 enhanced security and performance features.

From community.aws
Here’s my top 5 personal favorites posts from community.aws:

Upcoming AWS and community events
Check your calendars and sign up for upcoming AWS and community events:

  • AWS Korea re:Invent reCap Online, February 2-4 — A virtual event recapping key announcements and innovations from re:Invent 2023 for the Korean audience.
  • AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs. Upcoming AWS Community Day is in Ahmedabad (February 8).
  • AWS Public Sector Day London, February 27 — Join public sector leaders and innovators to explore how AWS is enabling digital transformation in government, education, and healthcare.
  • AWS Innovate GenAI + Data Edition — A free online conference focusing on generative AI and data innovations. Available in multiple Regions: APJC and EMEA (March 6), North America (March 13), Greater China Region (March 14), and Latin America (April 8).

Browse more upcoming AWS led in-person and virtual developer-focused events.

AWS Community re:Invent re:Caps

Lastly, if you want to learn about top announcements and innovations from AWS re:Invent, the AWS Community shares a summary from a community perspective of these announcements so you can get up to speed. Download the AWS Community re:Invent re:Caps deck

That’s all for this week. Check back next Monday for another Weekly Roundup!

Donnie

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

DeepSeek-R1 models now available on AWS

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/deepseek-r1-models-now-available-on-aws/

During this past AWS re:Invent, Amazon CEO Andy Jassy shared valuable lessons learned from Amazon’s own experience developing nearly 1,000 generative AI applications across the company. Drawing from this extensive scale of AI deployment, Jassy offered three key observations that have shaped Amazon’s approach to enterprise AI implementation.

First is that as you get to scale in generative AI applications, the cost of compute really matters. People are very hungry for better price performance. The second is actually quite difficult to build a really good generative AI application. The third is the diversity of the models being used when we gave our builders freedom to pick what they want to do. It doesn’t surprise us, because we keep learning the same lesson over and over and over again, which is that there is never going to be one tool to rule the world.

As Andy emphasized, a broad and deep range of models provided by Amazon empowers customers to choose the precise capabilities that best serve their unique needs. By closely monitoring both customer needs and technological advancements, AWS regularly expands our curated selection of models to include promising new models alongside established industry favorites. This ongoing expansion of high-performing and differentiated model offerings helps customers stay at the forefront of AI innovation.

This leads us to Chinese AI startup DeepSeek. DeepSeek launched DeepSeek-V3 on December 2024 and subsequently released DeepSeek-R1, DeepSeek-R1-Zero with 671 billion parameters, and DeepSeek-R1-Distill models ranging from 1.5–70 billion parameters on January 20, 2025. They added their vision-based Janus-Pro-7B model on January 27, 2025. The models are publicly available and are reportedly 90-95% more affordable and cost-effective than comparable models. Per Deepseek, their model stands out for its reasoning capabilities, achieved through innovative training techniques such as reinforcement learning.

Today, you can now deploy DeepSeek-R1 models in Amazon Bedrock and Amazon SageMaker AI. Amazon Bedrock is best for teams seeking to quickly integrate pre-trained foundation models through APIs. Amazon SageMaker AI is ideal for organizations that want advanced customization, training, and deployment, with access to the underlying infrastructure. Additionally, you can also use AWS Trainium and AWS Inferentia to deploy DeepSeek-R1-Distill models cost-effectively via Amazon Elastic Compute Cloud (Amazon EC2) or Amazon SageMaker AI.

With AWS, you can use DeepSeek-R1 models to build, experiment, and responsibly scale your generative AI ideas by using this powerful, cost-efficient model with minimal infrastructure investment. You can also confidently drive generative AI innovation by building on AWS services that are uniquely designed for security. We highly recommend integrating your deployments of the DeepSeek-R1 models with Amazon Bedrock Guardrails to add a layer of protection for your generative AI applications, which can be used by both Amazon Bedrock and Amazon SageMaker AI customers.

You can choose how to deploy DeepSeek-R1 models on AWS today in a few ways: 1/ Amazon Bedrock Marketplace for the DeepSeek-R1 model, 2/ Amazon SageMaker JumpStart for the DeepSeek-R1 model, 3/ Amazon Bedrock Custom Model Import for the DeepSeek-R1-Distill models, and 4/ Amazon EC2 Trn1 instances for the DeepSeek-R1-Distill models.

Let me walk you through the various paths for getting started with DeepSeek-R1 models on AWS. Whether you’re building your first AI application or scaling existing solutions, these methods provide flexible starting points based on your team’s expertise and requirements.

1. The DeepSeek-R1 model in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace offers over 100 popular, emerging, and specialized FMs alongside the current selection of industry-leading models in Amazon Bedrock. You can easily discover models in a single catalog, subscribe to the model, and then deploy the model on managed endpoints.

To access the DeepSeek-R1 model in Amazon Bedrock Marketplace, go to the Amazon Bedrock console and select Model catalog under the Foundation models section. You can quickly find DeepSeek by searching or filtering by model providers.

After checking out the model detail page including the model’s capabilities, and implementation guidelines, you can directly deploy the model by providing an endpoint name, choosing the number of instances, and selecting an instance type.

You can also configure advanced options that let you customize the security and infrastructure settings for the DeepSeek-R1 model including VPC networking, service role permissions, and encryption settings. For production deployments, you should review these settings to align with your organization’s security and compliance requirements.

With Amazon Bedrock Guardrails, you can independently evaluate user inputs and model outputs. You can control the interaction between users and DeepSeek-R1 with your defined set of policies by filtering undesirable and harmful content in generative AI applications. The DeepSeek-R1 model in Amazon Bedrock Marketplace can only be used with Bedrock’s ApplyGuardrail API to evaluate user inputs and model responses for custom and third-party FMs available outside of Amazon Bedrock. To learn more, read Implement model-independent safety measures with Amazon Bedrock Guardrails.

Amazon Bedrock Guardrails can also be integrated with other Bedrock tools including Amazon Bedrock Agents and Amazon Bedrock Knowledge Bases to build safer and more secure generative AI applications aligned with responsible AI policies. To learn more, visit the AWS Responsible AI page.

Refer to this step-by-step guide on how to deploy the DeepSeek-R1 model in Amazon Bedrock Marketplace. To learn more, visit Deploy models in Amazon Bedrock Marketplace.

2. The DeepSeek-R1 model in Amazon SageMaker JumpStart
Amazon SageMaker JumpStart is a machine learning (ML) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with just a few clicks. To deploy DeepSeek-R1 in SageMaker JumpStart, you can discover the DeepSeek-R1 model in SageMaker Unified Studio, SageMaker Studio, SageMaker AI console, or programmatically through the SageMaker Python SDK.

In the Amazon SageMaker AI console, open SageMaker Unified Studio or SageMaker Studio. In case of SageMaker Studio, choose JumpStart and search for “DeepSeek-R1” in the All public models page.

You can select the model and choose deploy to create an endpoint with default settings. When the endpoint comes InService, you can make inferences by sending requests to its endpoint.

You can derive model performance and ML operations controls with Amazon SageMaker AI features such as Amazon SageMaker Pipelines, Amazon SageMaker Debugger, or container logs. The model is deployed in an AWS secure environment and under your virtual private cloud (VPC) controls, helping to support data security.

As like Bedrock Marketpalce, you can use the ApplyGuardrail API in the SageMaker JumpStart to decouple safeguards for your generative AI applications from the DeepSeek-R1 model. You can now use guardrails without invoking FMs, which opens the door to more integration of standardized and thoroughly tested enterprise safeguards to your application flow regardless of the models used.

Refer to this step-by-step guide on how to deploy DeepSeek-R1 in Amazon SageMaker JumpStart. To learn more, visit Discover SageMaker JumpStart models in SageMaker Unified Studio or Deploy SageMaker JumpStart models in SageMaker Studio.

3. DeepSeek-R1-Distill models using Amazon Bedrock Custom Model Import
Amazon Bedrock Custom Model Import provides the ability to import and use your customized models alongside existing FMs through a single serverless, unified API without the need to manage underlying infrastructure. With Amazon Bedrock Custom Model Import, you can import DeepSeek-R1-Distill Llama models ranging from 1.5–70 billion parameters. As I highlighted in my blog post about Amazon Bedrock Model Distillation, the distillation process involves training smaller, more efficient models to mimic the behavior and reasoning patterns of the larger DeepSeek-R1 model with 671 billion parameters by using it as a teacher model.

After storing these publicly available models in an Amazon Simple Storage Service (Amazon S3) bucket or an Amazon SageMaker Model Registry, go to Imported models under Foundation models in the Amazon Bedrock console and import and deploy them in a fully managed and serverless environment through Amazon Bedrock. This serverless approach eliminates the need for infrastructure management while providing enterprise-grade security and scalability.

Refer to this step-by-step guide on how to deploy DeepSeek-R1 models using Amazon Bedrock Custom Model Import. To learn more, visit Import a customized model into Amazon Bedrock.

4. DeepSeek-R1-Distill models using AWS Trainium and AWS Inferentia
AWS Deep Learning AMIs (DLAMI) provides customized machine images that you can use for deep learning in a variety of Amazon EC2 instances, from a small CPU-only instance to the latest high-powered multi-GPU instances. You can deploy the DeepSeek-R1-Distill models on AWS Trainuim1 or AWS Inferentia2 instances to get the best price-performance.

To get started, go to Amazon EC2 console and launch a trn1.32xlarge EC2 instance with the Neuron Multi Framework DLAMI called Deep Learning AMI Neuron (Ubuntu 22.04).

Once you have connected to your launched ec2 instance, install vLLM, an open-source tool to serve Large Language Models (LLMs) and download the DeepSeek-R1-Distill model from Hugging Face. You can deploy the model using vLLM and invoke the model server.

To learn more, refer to this step-by-step guide on how to deploy DeepSeek-R1-Distill Llama models on AWS Inferentia and Trainium.

You can also visit the DeepSeek-R1-Distill-Llama-8B or deepseek-ai/DeepSeek-R1-Distill-Llama-70B model cards on Hugging Face. Choose Deploy and then Amazon SageMaker. From the AWS Inferentia and Trainium tab, copy the example code for deploy DeepSeek-R1-Distill Llama models.

Since the release of DeepSeek-R1, various guides of its deployment for Amazon EC2 and Amazon Elastic Kubernetes Service (Amazon EKS) have been posted. Here is some additional material for you to check out:

Things to know
Here are a few important things to know.

  • Pricing – For publicly available models like DeepSeek-R1, you are charged only the infrastructure price based on inference instance hours you select for Amazon Bedrock Markeplace, Amazon SageMaker JumpStart, and Amazon EC2. For the Bedrock Custom Model Import, you are only charged for model inference, based on the number of copies of your custom model is active, billed in 5-minute windows. To learn more, check out the Amazon Bedrock Pricing, Amazon SageMaker AI Pricing, and Amazon EC2 Pricing pages.
  • Data security – You can use enterprise-grade security features in Amazon Bedrock and Amazon SageMaker to help you make your data and applications secure and private. This means your data is not shared with model providers, and is not used to improve the models. This applies to all models—proprietary and publicly available—like DeepSeek-R1 models on Amazon Bedrock and Amazon SageMaker. To learn more, visit Amazon Bedrock Security and Privacy and Security in Amazon SageMaker AI.

Now available
DeepSeek-R1 is generally available today in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. You can also use DeepSeek-R1-Distill models using Amazon Bedrock Custom Model Import and Amazon EC2 instances with AWS Trainum and Inferentia chips.

Give DeepSeek-R1 models a try today in the Amazon Bedrock console, Amazon SageMaker AI console, and Amazon EC2 console, and send feedback to AWS re:Post for Amazon Bedrock and AWS re:Post for SageMaker AI or through your usual AWS Support contacts.

Channy

AWS Weekly roundup: EventBridge, SNS FIFO, Amazon Corretto, Amazon Connect, Amazon Bedrock, and more

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-eventbridge-sns-fifo-amazon-corretto-amazon-connect-amazon-bedrock-and-more/

I counted about 40 new launches from AWS since last week – back to our normal rhythm of releases. Services teams are listening to your feedback and developing little (or big) changes that makes your life easier when working with our services. The ability to support multiple sessions in the AWS Console is my favorite one so far in 2025.

But our teams didn’t stop there, let’s look at the last week’s new announcements.

Last week’s launches

Beside the usual Regional expansion (new capabilities that are now available in a new Region), here are the launches that got my attention.

Amazon EventBridge announces direct delivery to cross-account targetsAmazon EventBridge is now able to deliver events to targets in another AWS account directly without having to send them to the default bus in the target account first. This will simplify so many architectures out there! It supports any target that supports resource-based policies, including AWS Lambda, Amazon Simple Queue Service (Amazon SQS), Amazon Simple Notification Service (Amazon SNS), Amazon Kinesis, and Amazon API Gateway.

Amazon Corretto quaterly update – We announced quarterly security and critical updates for Amazon Corretto Long-Term Supported (LTS) and Feature Release (FR) versions of OpenJDK. Corretto 23.0.2, 21.0.6, 17.0.14, 11.0.26, 8u442 are now available for download. Amazon Corretto is a no-cost, multi-platform, production-ready distribution of OpenJDK. You can download the updates from the Corretto home page or just type apt-get or yum update.

High-throughput mode for Amazon SNS FIFO Topics – Amazon SNS now supports high-throughput mode for SNS FIFO topics, with default throughput matching SNS standard topics across all Regions. When you enable high-throughput mode, SNS FIFO topics will maintain order within message group, while reducing the deduplication scope to the message-group level. With this change, you can leverage up to 30K messages per second (MPS) per account by default in US East (N. Virginia) Region, and 9K MPS per account in US West (Oregon) and Europe (Ireland) Regions, and request quota increases for additional throughput in any Region.

Amazon Connect agent workspace now supports audio optimization for Citrix and Amazon WorkSpaces virtual desktopsAmazon Connect agent workspace now supports the ability to redirect audio from Citrix and Amazon WorkSpaces Virtual Desktop Infrastructure (VDI) environments to a customer service agent’s local device. Audio redirection improves voice quality and reduces latency for voice calls handled on virtual desktops, providing a better experience for both end customers and agents.

Amazon Redshift announces support for History Mode for zero-ETL integrationsThis new capability enables you to build Type 2 Slowly Changing Dimension (SCD 2) tables on your historical data from databases, out-of-the-box in Amazon Redshift, without writing any code. History mode simplifies the process of tracking and analyzing historical data changes, allowing you to gain valuable insights from your data’s evolution over time.

Finally, Amazon Bedrock has its own set of announcements. First, for anyone investing in retrieval-augmented generation, Bedrock now support multimodal content with Cohere Embed 3 Multilingual and Embed 3 English models. This enables you to create embeddings to not only index text, but also images.

Second, read Luma AI’s Ray2 visual AI model now available in Amazon Bedrock. Luma Ray2 is a large-scale video-generation model capable of creating realistic visuals with fluid, natural movement. With Luma Ray2 in Amazon Bedrock, you can generate production-ready video clips with seamless animations, ultrarealistic details, and logical event sequences with natural language prompts, removing the need for technical prompt engineering. Ray2 currently supports 5- and 9-second video generations with 540p and 720p resolution.

And finally, Amazon Bedrock Flows announces preview of multi-turn conversation support. Amazon Bedrock Flows enables you to link foundation models (FMs), Amazon Bedrock Prompts, Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails and other AWS services together to build and scale pre-defined generative AI workflows. This week, the team announced preview of multi-turn conversation support for agent nodes in Flows. This capability enables dynamic, back-and-forth conversations between users and flows, similar to a natural dialogue.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS events
Check your calendar and sign up for upcoming AWS events.

AWS Summits season is starting! I’m already working with the local team to prepare content for the Summits in Paris and London. Summits are free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Stay updated by visiting the official AWS Summit website and sign up for notifications to learn when registration opens for events in your area.

AWS GenAI Lofts are collaborative spaces and immersive experiences that showcase AWS expertise in cloud computing and AI. They provide startups and developers with hands-on access to AI products and services, exclusive sessions with industry leaders, and valuable networking opportunities with investors and peers. Find a GenAI Loft location near you, and don’t forget to register.

Browse all upcoming AWS led in-person and virtual events here.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— seb

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Luma AI’s Ray2 video model is now available in Amazon Bedrock

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/luma-ai-ray-2-video-model-is-now-available-in-amazon-bedrock/

As we preannounced at AWS re:Invent 2024, you can now use Luma AI Ray2 video model in Amazon Bedrock to generate high-quality video clips from text, creating captivating motion graphics from static concepts. AWS is the first and only cloud provider to offer fully managed models from Luma AI.

On January 16, 2025, Luma AI introduced Luma Ray2, the large–scale video generative model capable of creating realistic visuals with natural, coherent motion with strong understanding of text instructions. Luma Ray2 exhibits advanced capabilities as a result of being trained on Luma’s new multi-modal architecture. It scales to ten times compute of Ray1, enabling it to produce 5 second or 9 second video clips that show fast coherent motion, ultra-realistic details, and logical event sequences with 540p and 720p resolution.

With Luma Ray2 in Amazon Bedrock, you can add high-quality, realistic, production-ready videos generated from text in your generative AI application through a single API. Luma Ray2 video model understands the interactions between people, animals, and objects, and you can create consistent and physically accurate characters through state-of-the-art natural language instruction understanding and reasoning.

You can use Ray2 video generations for content creation, entertainment, advertising, and media use cases, streamlining the creative process, from concept to execution. You can generate smooth, cinematic, and lifelike camera movements that match the intended emotion of the scene. You can rapidly experiment with different camera angles and styles and deliver creative outputs for architecture, fashion, film, graphic design, and music.

Let’s take a look at the impressive video generations by Luma Ray2 that Luma has published.

Get started with Luma Ray2 model in Amazon Bedrock
Before getting started, if you are new to using Luma models, go to the Amazon Bedrock console and choose Model access on the bottom left pane. To access the latest Luma AI models, request access for Luma Ray2 in Luma AI.

To test the Luma AI model in Amazon Bedrock, choose Image/Video under Playgrounds in the left menu pane. Choose Select model, then select Luma AI as the category and Ray as the model.

For video generation models, you should have an Amazon Simple Storage Service (Amazon S3) bucket to store all generated videos. This bucket will be created in your AWS account, and Amazon Bedrock will have read and write permissions for it. Choose Confirm to create a bucket and generate a video.

I will generate a 5-second video with 720P and 24 frames per second with 16:9 aspect ratio for my prompt.

Here is an example prompt and generated video. You can download it stored in the S3 bucket.
a humpback whale swimming through space particles

Here are another featured examples to demonstrate Ray2 model.

Prompt 1: A miniature baby cat is walking and exploring on the surface of a fingertip

Prompt 2: A massive orb of water floating in a backlit forest

Prompt 3: A man plays saxophone by @ziguratt

Prompt 4: Macro closeup of a bee pollinating

To check out more examples and generated videos, visit the Luma Ray2 page.

By choosing View API request in the Bedrock console, you can also access the model using code examples in the AWS Command Line Interface (AWS CLI) and AWS SDKs. You can use luma.ray-v2:0 as the model ID.

Here is a sample of the AWS CLI command:

aws bedrock-runtime invoke-model \
    --model-id luma.ray-v2:0 \
    --region us-west-2 \
    --body "{\"modelInput\":{\"taskType\":\"TEXT_VIDEO\",\"textToVideoParams\":{\"text\":\"a humpback whale swimming through space particles\"},\"videoGenerationConfig\":{\"seconds\":6,\"fps\":24,\"dimension\":\"1280x720\"}},\"outputDataConfig\":{\"s3OutputDataConfig\":{\"s3Uri\":\"s3://your-bucket-name\"}}}"
     invoke-model-output.txt

You can use Converse API examples to generate videos using AWS SDKs to build your applications using various programming languages.

Now available
Luma Ray2 video model is generally available today in Amazon Bedrock in the US West (Oregon) AWS Region. Check the full Region list for future updates. To learn more, check out the Luma AI in Amazon Bedrock product page and the Amazon Bedrock Pricing page.

Give Luma Ray2 a try in the Amazon Bedrock console today, and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

Channy

AWS Weekly Roundup: New AWS Mexico (Central) Region, simultaneous sign-in for multiple AWS accounts, and more (January 20, 2025)

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-new-aws-mexico-central-region-simultaneous-sign-in-for-multiple-aws-accounts-and-more-january-20-2025/

As winter maintains its hold over where I live in the Netherlands, rare moments of sunlight become precious gifts. This weekend offered one such treasure—while cycling along a quiet canal, golden rays broke through the typically gray Dutch sky, creating a perfect moment of serenity. These glimpses of brightness feel particularly special during January, when daylight can be scarce in our corner of Europe. As we move deeper into 2025, the third week of the new year brings both reflection and forward momentum. While global conversations swirl around technological advancements, it’s these small, personal moments that remind us to pause and appreciate the simple pleasures among our rapidly evolving world.

Let’s look at the last week’s new announcements.

Last week’s launches
Here are the launches that got my attention.

AWS Mexico (Central) Region – In February 2024, we announced plans to expand infrastructure in Mexico, and we’ve now launched the AWS Mexico (Central) Region with three Availability Zones and API code mx-central-1. This marks the first AWS infrastructure Region in Mexico and adds to our growing presence in Latin America. The new Region provides you with local workload management, data storage capabilities, enhanced performance with lower latency, and robust security standards. It features advanced cloud technologies, including cutting-edge artificial intelligence and machine learning (AI/ML) capabilities with purpose-built processors, comprehensive security capabilities with support for 143 security standards and compliance certifications. With this launch, AWS now spans 114 Availability Zones within 36 geographic Regions.

AWS Management Console now supports simultaneous sign-in for multiple AWS accounts – Using multi-session capability in the AWS Management Console, you can now sign-in to multiple AWS accounts and manage your resources in a single browser. You can sign in to up to 5 sessions and this can be any combination of root, AWS Identity and Access Management (IAM), or federated roles in different accounts or in the same account. You can scale your applications using multiple accounts following AWS best-practice guidelines. You can use accounts for different environments, such as development, testing, and production, and compare resource configurations and status across multiple accounts for troubleshooting application issues and other application related jobs.

Introducing new larger sizes on Amazon EC2 Flex instances – We’re announcing the general availability of two new larger sizes (12xlarge and 16xlarge) on Amazon Elastic Compute Cloud (Amazon EC2) Flex (C7i-flex and M7i-flex) instances. The new sizes expand the EC2 Flex portfolio, providing additional compute options to scale up existing workloads or run larger-sized applications that need additional memory. These instances are powered by custom 4th Gen Intel Xeon Scalable processors, which are available only on AWS, and offer up to 15% better performance over comparable x86-based Intel processors used by other cloud providers. Flex instances are the easiest way to get price performance benefits and lower prices for a majority of compute-intensive and general-purpose workloads. They deliver up to 19% better price performance than comparable previous generation instances and are a great first choice for applications that don’t fully utilize the compute resources. Flex instances are ideal for web and application servers, batch processing, enterprise applications, databases, and more. For compute-intensive and general-purpose workloads that need even larger instance sizes (up to 192 vCPUs and 768 GiB memory) or continuous high CPU usage, you can use Amazon EC2 C7i and M7i instances.

Announcing AWS User Notifications general availability on AWS CloudFormation – You can use AWS User Notifications to configure notifications to be sent using the AWS Management Console Notifications Center, email, AWS Chatbot, or mobile push notifications to the AWS Console Mobile App to keep you informed about important events such as Amazon CloudWatch alarms. With this capability, you can define notification configurations as part of your infrastructure-as-code (IaC) practices and specify notification configurations for specific resource types within your AWS CloudFormation templates. For example, you can set up notifications to trigger when an Amazon EC2 Auto Scaling group scales out, an Elastic Load Balancing (ELB) load balancer is provisioned, or an Amazon Relational Database Service (Amazon RDS) database is modified. You have granular control over which events will trigger notifications and who should receive them.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

We launched existing services and instance types in additional Regions:

Other AWS events
Check your calendar and sign up for upcoming AWS events.

AWS Summits are free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Stay updated by visiting the official AWS Summit website and sign up for notifications to learn when registration opens for events in your area.

AWS GenAI Lofts are collaborative spaces and immersive experiences that showcase AWS expertise in cloud computing and AI. They provide startups and developers with hands-on access to AI products and services, exclusive sessions with industry leaders, and valuable networking opportunities with investors and peers. Find a GenAI Loft location near you and don’t forget to register.

Browse all upcoming AWS led in-person and virtual events here.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Esra

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Now open — AWS Mexico (Central) Region

Post Syndicated from Elizabeth Fuentes original https://aws.amazon.com/blogs/aws/now-open-aws-mexico-central-region/

In February 2024, we announced plans to expand Amazon Web Services (AWS) infrastructure in Mexico. Today, I’m excited to announce the general availability of the AWS Mexico (Central) Region with three Availability Zones and API code mx-central-1. This new AWS Region is the first AWS infrastructure Region in Mexico and adds to our growing presence in Latin America.

The AWS Region in Mexico represents a significant commitment to the country’s digital future. AWS is planning to invest more than $5 billion in Mexico over 15 years. This AWS Region will provide customers with advanced and secure cloud technologies, including cutting-edge artificial intelligence (AI) and machine learning (ML) capabilities with purpose-built processors, while supporting Mexico’s growing digital economy. With this effort, AWS will support an average of more than 7,000 full-time equivalent jobs annually in Mexico, adding more than $10 billion to Mexico’s gross domestic product (GDP). AWS has also launched a $300,000 AWS InCommunities Fund in Queretaro to help local groups, schools, and organizations initiate new community projects.

mexico city

Palacio de Bellas Artes, Mexico City

The AWS Mexico (Central) Region provides organizations in Mexico with a new option to run their workloads and store data locally. Organizations that need data residency capabilities, enhanced performance with lower latency, or robust security standards can now use infrastructure located in Mexico.

AWS in Mexico
AWS has operated infrastructure in Mexico since 2020. The infrastructure includes seven Amazon CloudFront edge locations, AWS Outposts, and strategic offerings such as AWS Local Zones in Queretaro and AWS Direct Connect. These infrastructure offerings help customers run low-latency applications while maintaining secure connectivity.

Performance and Innovation
The AWS Mexico (Central) Region brings AWS infrastructure and services closer to local customers. With this new Region, AWS provides lower latency for customers in Mexico compared to using other AWS Regions. Customers will also be able to use our innovation in purpose-built processors, notably AWS Graviton, that delivers up to 40% better price performance compared to x86-based Amazon EC2 instances across diverse workloads.

This technological advantage extends to our cutting-edge AI and ML capabilities, including:

  • Advanced ML infrastructure with AWS Trainium and AWS Inferentia for scalable generative AI deployment.
  • Purpose-built processors optimized for cloud workloads to deliver best price-performance.

Security and Compliance
AWS provides comprehensive security capabilities with support for 143 security standards and compliance certifications, including PCI-DSS, HIPAA/HITECH, FedRAMP, GDPR, FIPS 140-2, and NIST 800-171. All AWS customers own their data, choose where to store it, and decide if/when to move it. This means customers storing content in the AWS Mexico (Central) Region have the assurance that their content will not leave Mexico, unless they chose to move it.

AWS Customers in Mexico
Leading Mexican organizations are already achieving significant results with AWS. Companies such as Aeroméxico, Banco Santander Mexico, Cinépolis, Grupo Salinas, Kavak, Palace Resorts, and Vector Casa de Bolsa are running mission-critical workloads on AWS. Here are key examples:

BBVA, a leading multinational financial services company, is using AWS to accelerate its data-driven transformation. Using Amazon SageMaker and Amazon Bedrock, BBVA is empowering over 1,000 data scientists to build, train, and deploy machine learning models efficiently. This technology enables BBVA to explore advanced technologies and create innovative financial solutions, supporting their goal of becoming a true data and AI-driven digital organization.

Grupo Multimedios, a leading Mexican media group, is pioneering the use of generative AI, by implementing Amazon Bedrock for their media asset manager (MAM), reducing content research time by 88%, decreasing news generation time by 40%, and increasing content production by 70% (250 additional news items daily). As the fastest-growing media group embracing technological leadership, their AI implementation demonstrates a commitment to innovation while streamlining operations.

Bowhead Health, a digital healthcare company, is revolutionizing cancer research by using Amazon Bedrock to accelerate the research pipeline. The company has built a vast, de-identified dataset that’s ready for analysis without traditional recruitment barriers. Bowhead Health also delivers robust, real-world insights to drive faster breakthroughs in oncology drug development.

SkyAlert, an innovative technology company protecting millions in earthquake-prone areas, transformed its alert system by migrating to AWS in 2018. Before AWS, their system required 20 virtual machines and experienced significant delays during critical moments. Using AWS Lambda, AWS Fargate, and Amazon Pinpoint, they can now scale automatically and deliver messages to users quickly. With the opening of the AWS Mexico (Central) Region, SkyAlert anticipates further improvements to their services with local AWS infrastructure. As Santiago Cantú, Co-Founder of SkyAlert, explains, “The opening of the AWS Region in Mexico is an extremely important event for SkyAlert and for the security of those who trust us. Having local AWS infrastructure will improve our ability to deliver critical alerts, which potentially save lives, even faster and more reliably. This perfectly aligns with our mission to provide the most robust and advanced earthquake early warning system available. The new Region will allow us to take even greater advantage of AWS services, ensuring that we continue to be at the forefront of innovation in disaster preparedness.”

Building Skills Together
AWS has made significant investments in upskilling initiatives in Mexico including:

  • Training over 500,000 individuals in cloud technology since 2017.
  • Collaborating with the the Ministry of Economy to train 138,000 people in digital technology as of 2024.
  • Partnering with universities like Universidad Panamericana and Tec de Monterrey to teach digital skills.
  • Training programs with Canacintra for 20,000 Small and Medium Businesses (SMB) leaders.

AWS Commitment to Sustainability
Amazon is committed to reaching net-zero carbon across its business by 2040. A recent Accenture study shows that running workloads on AWS is up to 4.1 times more energy-efficient than on-premises environments. When workloads are optimized on AWS, the associated carbon footprint can be lowered by up to 99%. The AWS Mexico (Central) Region incorporates sustainable design practices, using air-cooling technology that eliminates the need for cooling water in operations. With this new Region, customers will also benefit from AWS sustainability efforts across its infrastructure. To learn more about sustainability at AWS, visit the AWS Cloud sustainability page.

Things to know
AWS Community in Mexico – The AWS Community in Mexico is one of the most vibrant in Latin America, with 26+ AWS Community Builders and 15 AWS User Groups. These groups are located in Jalisco, Puebla, Monterrey, Mérida, Mexico City, Mexicali, Cancún, León, Querétaro, San Luis Potosí, Ensenada, Saltillo, Tijuana, and Villahermosa, plus a specialized User Group called Embajadoras cloud (Cloud ambassadors) focused on women’s professional development. Together, these groups comprise 9,000+ total members.

AWS Global footprint – With this launch, AWS now spans 114 Availability Zones within 36 geographic Regions.

Available now – The new AWS Mexico (Central) Region is ready to support your business, and you can find a detailed list of the services available in this Region on the AWS Services by Region page.

To start building in mx-central-1, visit the AWS Global Infrastructure page.

Thanks to David Victoria for the AWS Community México 2024 photo.

Eli

AWS Weekly Roundup: New Asia Pacific Region, DynamoDB updates, Amazon Q developer, and more (January 13, 2025)

Post Syndicated from Betty Zheng (郑予彬) original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-new-asia-pacific-region-dynamodb-updates-amazon-q-developer-and-more-january-13-2025/

As we move into the second week of 2025, China is celebrating Laba Festival (腊八节), a traditional holiday, which marks the beginning of Chinese New Year preparations. On this day, Chinese people prepare Laba congee, a special porridge combining various grains, dried fruits, and nuts. This

nutritious mixture symbolizes harmony, prosperity, and good fortune — with each ingredient representing the diversity and abundance of life. This traditional practice dates back to when Buddha achieved enlightenment after consuming rice porridge, making it a symbol of both material and spiritual nourishment. The festival, occurring on the eighth day of the twelfth lunar month, marks the countdown to Spring Festival, China’s most significant traditional holiday celebrating family reunion and renewal.

As our global tech community grows, such cultural celebrations remind us of the importance of inclusive innovation and shared progress.

Last week’s launches

Let’s take a look at what Amazon Web Services (AWS) launched in this week.

New AWS Asia Pacific (Thailand) Region– AWS has expanded its global infrastructure with the launch of the new Asia Pacific (Thailand) AWS Region, featuring three Availability Zones. With this addition, customers in Thailand and throughout Southeast Asia can serve customers with reduced latency while maintaining data residency within Thailand. The newly launched Region supports the complete range of AWS services and strengthens our presence in the rapidly growing ASEAN market.

New AWS Direct Connect location in Bangkok – Following the launch of our Thailand Region, we’ve established a new AWS Direct Connect location in Bangkok and expanded our existing infrastructure. This addition provides customers in Thailand with improved connectivity options and reduced network latency when accessing AWS services.

Database and analytics

Configurable point-in-time recovery periods for Amazon DynamoDBAmazon DynamoDB now enables customizable point-in-time recovery (PITR) periods, which means customers can specify recovery durations ranging from 1 to 35 days on a per-table basis. This enhancement enables organizations to meet precise compliance requirements while maximizing cost-efficiency. The feature is now available across all AWS Regions, including AWS GovCloud (US West) and China Regions. This flexibility in data recovery periods empowers customers to align their backup policies precisely with their business requirements and regulatory obligations.

Amazon MSK Connect APIs with AWS PrivateLinkAmazon Managed Streaming for Apache Kafka Connect (Amazon MSK Connect) APIs now support AWS PrivateLink, giving customers access to MSK Connect APIs through private endpoints within their virtual private cloud (VPC). This enhancement provides increased security and reduced data exposure by keeping traffic within the AWS network.

Generative AI and machine learning

Amazon Q Developer in SageMaker Code EditorAmazon Q Developer is now integrated into the Amazon SageMaker Code Editor integrated development environment (IDE), enhancing the developer’s experience with AI-powered code assistance. Intelligent code suggestions, documentation assistance, and contextual recommendations are now directly available within the SageMaker development environment.

Management and governance

AWS Systems Manager Automation in AWS ChatbotAWS Chatbot now offers 20 additional AWS Systems Manager Automation runbook recommendations, expanding its capabilities for automated operations management. These new recommendations help customers streamline their operational tasks and implement best practices more efficiently through chat-based interactions.

AWS Transit Gateway cost analysis enhancement – We’ve introduced new capabilities for analyzing Transit Gateway data processing charges using cost allocation tags. This feature provides improved visibility and control over networking costs, enabling organizations to track and optimize AWS Transit Gateway usage efficiently. The enhanced cost analysis tools deliver detailed insights into network traffic patterns and associated costs.

Other AWS news and highlights

2024’s most popular DevOps blog posts – The retrospective blog post “The most visited DevOps and Developer Productivity blog posts in 2024” has reached the top one position on this week’s AWS most popular articles chart. This compilation presents the most influential DevOps content from 2024, offering insights into trending topics and best practices. The collection examines key developments in continuous integration and continuous development (CI/CD), infrastructure as code (IaC), and automation practices.

New security course for generative AIAWS Skill Builder has released a new course focusing on securing generative AI applications on AWS. This comprehensive training teaches professionals to implement security best practices for artificial intelligence and machine learning (AI/ML) workloads, addressing data protection, model security, and compliance requirements. The course meets the growing demand for specialized security knowledge in the rapidly evolving field of generative AI.

Amazon Connect Contact Lens free trials – We’re introducing free trials for first-time users of Amazon Connect Contact Lens conversational analytics and performance evaluations. New customers can process up to 100,000 voice minutes monthly at no cost for 2 months, and first-time performance evaluation users receive a 30-day free trial starting with their first evaluation. With this initiative, customers can experience Contact Lens capabilities in their environment without additional costs. The free trials are available across all AWS Regions where Contact Lens is supported.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Whether you’re a developer, architect, business leader, or you’re starting your cloud journey – and regardless of what 2024 brought your way – 2025 presents new opportunities for everyone.

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Betty

Announcing the new AWS Asia Pacific (Thailand) Region

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/announcing-the-new-aws-asia-pacific-thailand-region/

Today, we’re pleased to announce that the AWS Asia Pacific (Thailand) Region is now generally available with three Availability Zones and API name ap-southeast-7.

The AWS Asia Pacific (Thailand) Region is the first infrastructure Region in Thailand and the fourteenth Region in Asia Pacific, joining existing Regions in Hong Kong, Hyderabad, Jakarta, Malaysia, Melbourne, Mumbai, Osaka, Seoul, Singapore, Sydney, and Tokyo, as well as the Beijing and Ningxia China Regions.

Lumphini Park, one of the largest green spaces in central Bangkok spanning 142 acres.

The adoption of cloud computing has gained significant momentum in Thailand, driven by evolving business needs and government initiatives such as Thailand 4.0. These initiatives aim to transform Thailand into an innovation-driven economy by using emerging technologies to enhance productivity, competitiveness, and sustainable growth.

The new AWS Region will help startups, enterprises, government agencies, educational institutions, and nonprofit organizations run their applications and serve end users while maintaining data residency in Thailand. This aligns with Thailand’s digital transformation goals and the growing demand for cloud services. Over the next 15 years, Amazon Web Services (AWS) planned investments in Thailand are estimated to contribute $10B to Thailand’s Gross Domestic Product (GDP) and support an estimated average of 11,000 full-time equivalent (FTE) jobs in local Thai businesses annually.

Growing presence of AWS in Thailand
Our journey in Thailand began in 2013 with the first AWS office in Bangkok. Since then, AWS has continuously expanded its infrastructure and services in the country:

Amazon CloudFront – Since 2020, AWS has established six Amazon CloudFront edge locations throughout Thailand. These edge locations are part of the highly secure and programmable AWS content delivery network (CDN), designed to accelerate the delivery of data, videos, applications, and APIs to users worldwide with low latency and high transfer speeds.

AWS Outposts – In the same year, 2020, AWS introduced AWS Outposts to the Thai market. As a fully managed solution, AWS Outposts brings AWS infrastructure and services to virtually any on-premises or edge location, enabling a truly consistent hybrid experience. This service is particularly valuable for workloads that require low latency, local data processing, or local data storage.

AWS Local Zones – In 2022, AWS strengthened its commitment to Thailand by launching AWS Local Zones in Bangkok. This infrastructure deployment places compute, storage, database, and other select services closer to large population, industry, and IT centers. As a result, customers can deliver applications requiring single-digit millisecond latency to end users.

AWS Direct Connect – AWS established a AWS Direct Connect location in Bangkok in 2023 to enhance connectivity options and added a new AWS Direct Connect location with the launch of the AWS Asia Pacific (Thailand) Region. Customers can use AWS Direct Connect to establish secure and dedicated network connections to their AWS resources, providing improved network performance and reduced bandwidth costs.

AWS customer success stories in Thailand
Organizations in Thailand are using our services to drive innovation and transformation. Here are a few examples:

2C2P
2C2P, a leading Thailand-based FinTech startup, chose AWS for its robust security capabilities. As an omnichannel payment service provider in Southeast Asia, the company processes millions of customer payments globally using AWS CloudHSM for cryptographic key management, AWS Shield for distributed denial of service (DDoS) protection, and AWS Secrets Manager to safeguard sensitive credentials.

“Through AWS, we’ve unlocked the power to securely, dynamically, and compliantly scale to meet the surge in payment transaction volumes. AWS CloudHSM plays a pivotal role in fulfilling compliance requirements and propelling us toward accelerated business expansion,” says Myo Zaw, Chief Technology Officer at 2C2P.

aCommerce
aCommerce, the largest ecommerce enabler in Southeast Asia, has revolutionized market intelligence by launching AskIQ, a generative AI–powered feature on AWS. This software as a service (SaaS) platform provides the world’s leading brands with comprehensive competitor and category performance tracking capabilities across Southeast Asia’s largest ecommerce sites.

Leena Chanvirach, VP of Data Products at aCommerce Group, emphasizes the strategic value of their AWS collaboration: “Our collaboration with AWS allows clients to double down on their core competencies and business priorities. This best-of-both-worlds approach gives brands a competitive edge without the burden of building and maintaining sophisticated data infrastructure in-house.”

Ascend Money
Ascend Money, a leading Southeast Asian FinTech company, achieved a 70 percent reduction in compute costs while simultaneously improving application performance by up to 40 percent in certain workloads. Ascend Money implemented a sophisticated compute strategy using Amazon EC2 instances, resulting in significant operational improvements.

“AWS has significantly improved our performance, enabling us to deliver more innovative services to our customers,” says Peerawit Phuangkaeo, Head of Technical Operations at Ascend Money.

Building cloud skills together
AWS has built comprehensive programs for cloud education and skills development in Thailand, training more than 50,000 individuals in cloud skills since 2017. Here are some of the programs:

AWS Skill Builder
AWS Skill Builder is an online learning center where you can learn from AWS experts and build cloud skills online. AWS has made cloud education more accessible to Thai learners by offering more than 600 courses, with 106 courses specifically available in Thai language. The recent launch of the Amazon AI Ready initiative has further expanded the learning opportunities, particularly in the growing field of AI.

AWS Educate
Since its introduction in 2016, AWS Educate has played a transformative role in Thai education. The program has successfully integrated cloud computing into educational curricula across Thailand, providing students with direct access to AWS resources and hands-on experience. The impact has been substantial, with over 20,000 Thai students enrolled in the program. Beyond student education, AWS Educate has invested in training Thai educators, preparing them to deliver engaging and practical cloud computing courses that prepare students for the demands of the digital economy.

AWS Academy
AWS Academy has been instrumental in connecting academic learning with industry needs since its launch in Thailand in 2017. Through strategic partnerships with more than 30 leading universities and colleges across the country, AWS Academy has created a robust pipeline of cloud-skilled professionals. The program provides educational institutions with comprehensive cloud computing curricula that align with industry needs, making sure that students graduate with practical, job-ready skills.

Through these various initiatives and programs, AWS is not just providing educational resources, it’s building a foundation for Thailand’s digital future by helping equip the workforce with the necessary skills to use cloud technologies effectively.

Supporting sustainable innovation in Thailand
The AWS commitment to sustainability extends to supporting innovative companies in Thailand that are driving environmental initiatives.

BODA Technology & Consultancy
BODA, an AWS powered sustainability startup, uses AWS IoT Core to develop AI-powered solutions for energy efficiency optimization. The company has successfully improved operations in over 100,000 buildings and factories across Thailand, enabling these facilities to maximize efficiency while reducing costs and environmental impact.

GSPC Group
GSPC Group, a leading sustainable power company in Thailand, demonstrates how AWS supports the energy sector’s digital transformation. Following the merger between Global Power Synergy Public Company and Glow Energy, the group chose AWS Cloud for migrating its photovoltaic solar plant operations. Working with AWS and AWS Partner Dailitech, GSPC Group has achieved a 20–25 percent reduction in hardware, software, and licensing costs since moving to the cloud.

Things to know
AWS Community in Thailand — Thailand is home to two AWS Heroes, seven AWS Community Builders, and more than 17,000 members of the AWS User Group. If you’re interested in joining AWS User Group Thailand, visit their Facebook page.

AWS Global footprint — AWS now spans 111 Availability Zones within 35 geographic regions worldwide. We have announced plans for 15 more Availability Zones and five more AWS Regions in Germany, Taiwan, Mexico, the Kingdom of Saudi Arabia, and New Zealand. 

The new Asia Pacific (Thailand) Region is ready to support your business. To learn more, visit the AWS Global Infrastructure page and start building on ap-southeast-7!

Happy building!
Donnie

Stable Diffusion 3.5 Large is now available in Amazon Bedrock

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/stable-diffusion-3-5-large-is-now-available-in-amazon-bedrock/

As we preannounced at AWS re:Invent 2024, you can now use Stable Diffusion 3.5 Large in Amazon Bedrock to generate high-quality images from text descriptions in a wide range of styles to accelerate the creation of concept art, visual effects, and detailed product imagery for customers in media, gaming, advertising, and retail.

In October 2024, Stability AI introduced Stable Diffusion 3.5 Large, the most powerful model in the Stable Diffusion family at 8.1 billion parameters trained on Amazon SageMaker HyperPod, with superior quality and prompt adherence. Stable Diffusion 3.5 Large can accelerate storyboarding, concept art creation, and rapid prototyping of visual effects. You can quickly generate high-quality 1-megapixel images for campaigns, social media posts, and advertisements, saving time and resources while maintaining creative control.

Stable Diffusion 3.5 Large offers users nearly endless creative possibilities, including:

  • Versatile Styles – You can generate images in a wide range of styles and aesthetics, including 3-dimentional, photography, painting, line art, and virtually any visual style you can imagine.
  • Prompt Adherence – You can use Stable Diffusion 3.5 Large’s advanced prompt adherence to closely follow your text prompts, making it a top choice for efficient, high-quality performance.
  • Diverse Outputs – You can create images representative of the diverse world around you, featuring people with different skin tones and features, without the need for extensive prompting.

Today, Stable Image Ultra in Amazon Bedrock has been updated to include Stable Diffusion 3.5 Large in the model’s underlying architecture. Stable Image Ultra, powered by Stability AI’s most advanced models, including Stable Diffusion 3.5, sets a new standard in image generation. It excels in typography, intricate compositions, dynamic lighting, vibrant colors, and artistic cohesion.

With the latest update of Stable Diffusion models in Amazon Bedrock, you have a broader set of solutions to boost your creativity and accelerate image generation workflows.

Get started with Stable Diffusion 3.5 Large in Amazon Bedrock
Before getting started, if you are new to using Stability AI models, go to the Amazon Bedrock console and choose Model access on the bottom left pane. To access the latest Stability AI models, request access for Stable Diffusion 3.5 Large in Stability AI.

To test the Stability AI models in Amazon Bedrock, choose Image/Video under Playgrounds in the left menu pane. Then choose Select model and select Stability AI as the category and Stable Diffusion 3.5 Large as the model.

You can generate an image with your prompt. Here is a sample prompt to generate the image:

High-energy street scene in a neon-lit Tokyo alley at night, where steam rises from food carts, and colorful neon signs illuminate the rain-slicked pavement.

By choosing View API request, you can also access the model using code examples in the AWS Command Line Interface (AWS CLI) and AWS SDKs. You can use stability.sd3-5-large-v1:0 as the model ID.

To get the image with a single command, I write the output JSON file to standard output and use the jq tool to extract the encoded image so that it can be decoded on the fly. The output is written in the img.png file.

Here is a sample of the AWS CLI command:

$ aws bedrock-runtime invoke-model \
   --model-id stability.sd3-5-large-v1:0 \
   --body "{\"text_prompts\":[{\"text\":\"High-energy street scene in a neon-lit Tokyo alley at night, where steam rises from food carts, and colorful neon signs illuminate the rain-slicked pavement.\",\"weight\":1}],\"cfg_scale\":0,\"steps\":10,\"seed\":0,\"width\":1024,\"height\":1024,\"samples\":1}" \
   --cli-binary-format raw-in-base64-out \
   --region us-west-2 \
/dev/stdout | jq -r '.images[0]' | base64 --decode > img.jpg

Here’s how you can use Stable Image Ultra 1.1 to include Stable Diffusion 3.5 Large in the model’s underlying architecture with the AWS SDK for Python (Boto3). This simple application interactively asks for a text-to-image prompt and then calls Amazon Bedrock to generate the image with stability.stable-image-ultra-v1:1 as the model ID.

import base64
import boto3
import json
import os

MODEL_ID = "stability.stable-image-ultra-v1:1"

bedrock_runtime = boto3.client("bedrock-runtime", region_name="us-west-2")

print("Enter a prompt for the text-to-image model:")
prompt = input()

body = {
    "prompt": prompt,
    "mode": "text-to-image"
}
response = bedrock_runtime.invoke_model(modelId=MODEL_ID, body=json.dumps(body))

model_response = json.loads(response["body"].read())

base64_image_data = model_response["images"][0]

i, output_dir = 1, "output"
if not os.path.exists(output_dir):
    os.makedirs(output_dir)
while os.path.exists(os.path.join(output_dir, f"img_{i}.png")):
    i += 1

image_data = base64.b64decode(base64_image_data)

image_path = os.path.join(output_dir, f"img_{i}.png")
with open(image_path, "wb") as file:
    file.write(image_data)

print(f"The generated image has been saved to {image_path}")

The application writes the resulting image in an output directory that is created if not present. To not overwrite existing files, the code checks for existing files to find the first file name available with the img_<number>.png format.

To learn more, visit the Invoke API examples using AWS SDKs to build your applications to generate an image using various programming languages.

Interesting examples
Here are a few images created with Stable Diffusion 3.5 Large.

Prompt: Full-body university students working on a tech project with the words Stable Diffusion 3.5 in Amazon Bedrock, cheerful cursive typography font in the foreground.
Prompt: Photo of three potions: the first potion is blue with the label "MANA", the second potion is red with the label "HEALTH", the third potion is green with the label "POISON". Old apothecary.
Prompt: Photography, pink rose flowers in the twilight, glowing, tile houses in the background. Prompt: 3D animation scene of an adventurer traveling the world with his pet dog.

Now available
Stable Diffusion 3.5 Large model is generally available today in Amazon Bedrock in the US West (Oregon) AWS Region. Check the full Region list for future updates. To learn more, check out the Stability AI in Amazon Bedrock product page and the Amazon Bedrock Pricing page.

Give Stable Diffusion 3.5 Large a try in the Amazon Bedrock console today and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

Channy

New Amazon EC2 High Memory U7inh instance on HPE Server for large in-memory databases

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/new-amazon-ec2-high-memory-u7inh-instance-on-hpe-server-for-large-in-memory-databases/

Today we’re announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) U7inh instance, a new addition to EC2 High Memory family, built in collaboration with Hewlett Packard Enterprise (HPE). Amazon EC2 U7inh instance runs on the 16-socket HPE Compute Scale-up Server 3200, and are built on the AWS Nitro System to deliver a fully integrated and managed experience consistent with other EC2 instances.

Powered by the fourth generation Intel® Xeon® Scalable processors (Sapphire Rapids), U7inh instance supports 32 TB of memory and 1920 vCPUs. This instance offers the highest compute performance, largest compute and memory size in the Amazon Web Services (AWS) Cloud for running large, mission-critical database workloads, like SAP HANA.

In May 2024, we launched U7i instances to support up to 896 vCPUs and up to 32 TB of memory, which our enterprise customers could use to successfully migrate their large mission-critical in-memory databases to AWS and benefit from the flexibility, scalability, reliability, and cost advantages that AWS offers.

As customers continue to scale their business applications, they wanted the performance combined with the additional CPUs and memory along with SAP certification to generate real-time business insights. Other customers that currently run on-premises with HPE servers have also asked how we can help them migrate to AWS to take advantage of cloud benefits while continuing to use HPE hardware.

Here are the detailed specs of new U7inh instance:

Instance name vCPUs Memory (DDR5) EBS bandwidth Network bandwidth
U7inh-32tb.480xlarge 1920 32,768 GiB 160 Gbps 200 Gbps

U7inh instance offers up to two times vCPUs and 1.6 times EBS bandwidth in a single instance, compared with the largest U7i instance. You can run your largest in-memory database workloads like SAP HANA or seamlessly migrate workloads running on HPE hardware to AWS.

U7inh instance supports Amazon Linux, Red Hat Enterprise Linux, and SUSE Enterprise Linux Server. Operating system support for SAP HANA workloads on High Memory instances include: SUSE Linux Enterprise Server 15 SP3 for SAP and above and Red Hat Enterprise Linux 8.6/9.0 for SAP and above.

U7inh instance is SAP certified to run Business Suite on HANA (SoH), Business Suite S/4HANA, Business Warehouse on HANA (BW), and SAP BW/4HANA in production environments. U7inh instance is also certified for scale-out SAP HANA OLTP workloads such as S/4HANA and customers can deploy up to four U7inh instance (128TB) in a cluster for even larger SAP HANA workloads.

To learn more about how to migrate, visit Migrating SAP HANA on AWS to an EC2 High Memory Instance in the SAP HANA on AWS Guides and AWS Launch Wizard for SAP in the AWS Launch Wizard User Guide.

Now available
Amazon EC2 U7inh instance is available in the US East (N. Virginia) and US West (Oregon) AWS Regions.

To learn more, visit the U7i instance product page and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

Channy

And that’s a wrap!

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/and-thats-a-wrap/

After 20 years, and 3283 posts adding up to 1,577,106 words I am wrapping up my time as the lead blogger on the AWS News Blog.

It has been a privilege to be able to “live in the future” and to get to learn and write about so many of our innovations over the last two decades: message queuing, storage, on-demand computing, serverless, and quantum computing to name just a few and to leave many others out. It has also been a privilege to be able to meet and to hear from so many of you that have faithfully read and (hopefully) learned from my content over the years. I treasure those interactions and your kind words, and I keep both in mind when I write.

Next for Jeff
I began my career as a builder. Over the years I have written tens of thousands of lines of assembly code (6502, Z80, and 68000), Visual Basic, and PHP, along with hundreds of thousands of lines of C. However, over the years I’ve progressively spent less time building and more time talking about building. As each new service and feature whizzed past my eyes I would reminiscence about days and decades past, when I could actually use these goodies to create something cool. I went from being a developer who could market, to a marketer who used to be able to develop. There’s absolutely nothing wrong with that, but I like to build. The medium could be code, 3D printing, LEGO bricks, electronics components, or even cardboard –creating and innovating is what motivates and sustains me.

With that as my driving force, my goal for the next step of my career is to invest more time focused on learning and using fewer things, building cool stuff, and creating fresh, developer-focused content as I do so. I’m still working to figure out the form that this will take, so stay tuned. I am also going to continue to make my weekly appearances at AWS OnAir (our Friday Twitch show), and I will continue to speak at AWS community events around the globe.

Next for the Blog
As for the AWS News Blog, it has long been backed by an awesome team, both visible and invisible. Here we are at the recent AWS re:Invent celebration of the blog’s 20th anniversary (photo courtesy of Liz Fuentes with edits by Channy Yun to add those who were otherwise occupied):

During the celebration I told the team that I look forward to celebrating the 30 year anniversary with them at re:Invent 2034.

Going forward, the team will continue to grow and the goal remains the same: to provide our customers with carefully chosen, high-quality information about the latest and most meaningful AWS launches. The blog is in great hands and this team will continue to keep you informed even as the AWS pace of innovation continues to accelerate.

Thanks Again
Once again I need to thank all of you for the very kind words and gestures over the years. Once in your life, if you work hard and get really lucky, you get a unique opportunity to do something that really and truly matters to people. And I have been lucky.

Jeff;

AWS Weekly Roundup: Amazon EC2 F2 instances, Amazon Bedrock Guardrails price reduction, Amazon SES update, and more (December 16, 2024)

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-ec2-f2-instances-amazon-bedrock-guardrails-price-reduction-amazon-ses-update-and-more-december-16-2024/

The week after AWS re:Invent builds on the excitement and energy of the event and is a good time to learn more and understand how the recent announcements can help you solve your challenges. As usual, we have you covered with our top announcements of AWS re:Invent 2024 post.

You can now watch keynotes and sessions on the AWS Event YouTube channel. This year Andy Jassy, now President and CEO at Amazon, returned to re:Invent and shared some thoughts in these videos.

Drawing on experiences Amazon has had building distributed systems at massive scale, Werner Vogels, VP and CTO at Amazon, shared critical lessons and strategies he has learned for managing complex systems in his keynote.

Last week’s launches
Here are the launches that got my attention.

Amazon Elastic Compute Cloud (Amazon EC2) – A new generation of FPGA-powered instances (F2) is now available. In contrast to a purpose-built chip designed with a single function in mind and then hard-wired to implement it, a field programmable gate array (FPGA) can be programmed in the field, after it has been plugged in to a socket on a PC board. We’re also introducing Amazon EC2 High Memory U7i instances with 6TiB and 8TiB of memory. U7i instances are ideal to run large in-memory databases such as SAP HANA, Oracle, and SQL Server. Graviton-based 8th generation instances now support bandwidth configurations for Amazon VPC and Amazon EBS.

Amazon Bedrock Guardrails – We are reducing pricing by up to 85% to help you implement safeguards for your generative AI applications. Also, we’re adding multilingual capabilities with support for Spanish and French languages.

Amazon Simple Email Services (SES) – Now offers Global Endpoints for multi-region sending resilience and announces the availability of Deterministic Easy DKIM (DEED), a new form of global identity which simplifies the use of DomainKeys Identified Mail (DKIM) management.

AWS CloudFormation – An enhanced version of the AWS Secrets Manager transform introducing automatic AWS Lambda upgrades.

Amazon Lex – Launches new multilingual streaming speech recognition models that enhance recognition accuracy through two specialized groupings: a European-based model (for Portuguese, Catalan, French, Italian, German, and Spanish) and a Asia Pacific-based model (for Chinese, Korean, and Japanese).

Amazon Connect – Now supports push notifications for mobile chat on iOS and Android devices. In this way, you can be proactively notified as soon as there is a new message from an agent or chatbot, even when not actively chatting. You can now also configure holidays and other variances to your contact center hours of operation.

AWS Security Hub – Now supports automated security checks aligned to the Payment Card Industry Data Security Standard (PCI DSS) v4.0.1, a compliance framework that provides a set of rules and guidelines for safely handling credit and debit card information.

AWS Resource ExplorerSupports 59 new resource types including Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Kendra, AWS Identity and Access Management (IAM) Access Analyzer, and Amazon SageMaker.

Amazon SageMaker AI – Inference optimized Amazon EC2 G6e instances (powered by NVIDIA L40S Tensor Core GPUs) and P5e (powered by NVIDIA H200 Tensor Core GPUs) are now available on Amazon SageMaker.

Amazon Redshift – Now supports automatically and incrementally refreshable materialized views on tables in a zero-ETL integration. Previously, in this case, you had to run a full refresh.

AWS Toolkit for Visual Studio Code – Now includes Amazon CloudWatch Logs Live Tail, an interactive log streaming and analytics capability that provides real-time visibility into your logs and makes it easier to develop and troubleshoot applications.

Other AWS news
Here are some additional projects, blog posts, and news items that you might find interesting:

Build a managed transactional data lake with Amazon S3 Tables – Just introduced at re:Invent 2024, Amazon S3 Tables is the first cloud object store with built-in Apache Iceberg support and the easiest way to store tabular data at scale. This post on the AWS Storage Blog provides an overview of S3 Tables and an example of how to build a transactional data lake with S3 Tables using Apache Spark on Amazon EMR.

Introducing Cross-Region Connectivity for AWS PrivateLink – More information on this recent launch that can be used to share and access Amazon Virtual Private Cloud (Amazon VPC) endpoint services across different AWS Regions.

Marc Brooker, VP/Distinguished Engineer at AWS, shared on his personal blog a few posts about what Amazon Aurora DSQL is, how it works, and how to make the best use of it:

That’s all for this week. Check back next Monday for another Weekly Roundup!

Danilo

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Now Available – Second-Generation FPGA-Powered Amazon EC2 instances (F2)

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-available-second-generation-fpga-powered-amazon-ec2-instances-f2/

Equipped with up to eight AMD Field-Programmable Gate Arrays (FPGAs), AMD EPYC (Milan) processors with up to 192 cores, High Bandwidth Memory (HBM), up to 8 TiB of SSD-based instance storage, and up to 2 TiB of memory, the new F2 instances are available in two sizes, and are ready to accelerate your genomics, multimedia processing, big data, satellite communication, networking, silicon simulation, and live video workloads.

A Quick FPGA Recap
Here’s how I explained the FPGA model when we previewed the first generation of FPGA-powered Amazon Elastic Compute Cloud (Amazon EC2) instances

One of the more interesting routes to a custom, hardware-based solution is known as a Field Programmable Gate Array, or FPGA. In contrast to a purpose-built chip which is designed with a single function in mind and then hard-wired to implement it, an FPGA is more flexible. It can be programmed in the field, after it has been plugged in to a socket on a PC board. Each FPGA includes a fixed, finite number of simple logic gates. Programming an FPGA is “simply” a matter of connecting them up to create the desired logical functions (AND, OR, XOR, and so forth) or storage elements (flip-flops and shift registers). Unlike a CPU which is essentially serial (with a few parallel elements) and has fixed-size instructions and data paths (typically 32 or 64 bit), the FPGA can be programmed to perform many operations in parallel, and the operations themselves can be of almost any width, large or small.

Since that launch, AWS customers have used F1 instances to host many different types of applications and services. With a newer FPGA, more processing power, and more memory bandwidth, the new F2 instances are an even better host for highly parallelizable, compute-intensive workloads.

Each of the AMD Virtex UltraScale+ HBM VU47P FPGAs has 2.85 million system logic cells and 9,024 DSP slices (up to 28 TOPS of DSP compute performance when processing INT8 values). The FPGA Accelerator Card associated with each F2 instance provides 16 GiB of High Bandwidth Memory and 64 GiB of DDR4 memory per FPGA.

Inside the F2
F2 instances are powered by 3rd generation AMD EPYC (Milan) processors. In comparison to F1 instances, they offer up to 3x as many processor cores, up to twice as much system memory and NVMe storage, and up to 4x the network bandwidth. Each FPGA comes with 16 GiB High Bandwidth Memory (HBM) with up to 460 GiB/s bandwidth. Here are the instance sizes and specs:

Instance Name vCPUs
FPGAs
FPGA Memory
HBM / DDR4
Instance Memory
NVMe Storage
EBS Bandwidth
Network Bandwidth
f2.12xlarge 48 2 32 GiB /
128 GiB
512 GiB 1900 GiB
(2x 950 GiB)
15 Gbps 25 Gbps
f2.48xlarge 192 8 128 GiB /
512 GiB
2,048 GiB 7600 GiB
(8x 950 GiB)
60 Gbps 100 Gbps

The high-end f2.48xlarge instance supports the AWS Cloud Digital Interface (CDI) to reliably transport uncompressed live video between applications, with instance-to-instance latency as low as 8 milliseconds.

Building FPGA Applications
The AWS EC2 FPGA Development Kit contains the tools that you will use to develop, simulate, debug, compile, and run your hardware-accelerated FPGA applications. You can launch the kit’s FPGA Developer AMI on a memory-optimized or compute-optimized instance for development and simulation, then use an F2 instance for final debugging and testing.

The tools included in the developer kit support a variety of development paradigms, tools, accelerator languages, and debugging options. Regardless of your choice, you will ultimately create an Amazon FPGA Image (AFI) which contains your custom acceleration logic and the AWS Shell which implements access to the FPGA memory, PCIe bus, interrupts, and external peripherals. You can deploy AFIs to as many F2 instances as desired, share with other AWS accounts or publish on AWS Marketplace.

If you have already created an application that runs on F1 instances, you will need to update your development environment to use the latest AMD tools, then rebuild and validate before upgrading to F2 instances.

FPGA Instances in Action
Here are some cool examples of how F1 and F2 instances can support unique and highly demanding workloads:

Genomics – Multinational pharmaceutical and biotechnology company AstraZeneca used thousands of F1 instances to build the world’s fastest genomics pipeline, able to process over 400K whole genome samples in under two months. They will adopt Illumina DRAGEN for F2 to realize better performance at a lower cost, while accelerating disease discovery, diagnosis, and treatment.

Satellite Communication – Satellite operators are moving from inflexible and expensive physical infrastructure (modulators, demodulators, combiners, splitters, and so forth) toward agile, software-defined, FPGA-powered solutions. Using the digital signal processor (DSP) elements on the FPGA, these solutions can be reconfigured in the field to support new waveforms and to meet changing requirements. Key F2 features such as support for up to 8 FPGAs per instance, generous amounts of network bandwidth, and support for the Data Plan Development Kit (DPDK) using Virtual Ethernet can be used to support processing of multiple, complex waveforms in parallel.

AnalyticsNeuroBlade‘s SQL Processing Unit (SPU) integrates with Presto, Apache Spark, and other open source query engines, delivering faster query processing and market-leading query throughput efficiency when run on F2 instances.

Things to Know
Here are a couple of final things that you should know about the F2 instances:

Regions – F2 instances are available today in the US East (N. Virginia) and Europe (London) AWS Regions, with plans to extend availability to additional regions over time.

Operating Systems – F2 instances are Linux-only.

Purchasing Options – F2 instances are available in On-Demand, SpotSavings Plan, Dedicated Instance, and Dedicated Host form.

Jeff;

An integrated experience for all your data and AI with Amazon SageMaker Unified Studio (preview)

Post Syndicated from Noritaka Sekiyama original https://aws.amazon.com/blogs/big-data/an-integrated-experience-for-all-your-data-and-ai-with-amazon-sagemaker-unified-studio-preview/

Organizations are building data-driven applications to guide business decisions, improve agility, and drive innovation. Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Data engineers use data warehouses, data lakes, and analytics tools to load, transform, clean, and aggregate data. Data scientists use notebook environments (such as JupyterLab) to create predictive models for different target segments.

However, building advanced data-driven applications poses several challenges. First, it can be time consuming for users to learn multiple services’ development experiences. Second, because data, code, and other development artifacts like machine learning (ML) models are stored within different services, it can be cumbersome for users to understand how they interact with each other and make changes. Third, configuring and governing access to appropriate users for data, code, development artifacts, and compute resources across services is a manual process.

To address these challenges, organizations often build bespoke integrations between services, tools, and their own access management systems. Organizations want the flexibility to adopt the best services for their use cases while empowering their data practitioners with a unified development experience.

We launched Amazon SageMaker Unified Studio in preview to tackle these challenges. SageMaker Unified Studio is an integrated development environment (IDE) for data, analytics, and AI. Discover your data and put it to work using familiar AWS tools to complete end-to-end development workflows, including data analysis, data processing, model training, generative AI app building, and more, in a single governed environment. Create or join projects to collaborate with your teams, share AI and analytics artifacts securely, and discover and use your data stored in Amazon S3, Amazon Redshift, and more data sources through the Amazon SageMaker Lakehouse. As AI and analytics use cases converge, transform how data teams work together with SageMaker Unified Studio.

This post demonstrates how SageMaker Unified Studio unifies your analytic workloads.

The following screenshot illustrates the SageMaker Unified Studio.

The SageMaker Unified Studio provides the following quick access menu options from Home:

  • Discover:
    • Data catalog – Find and query data assets and explore ML models
    • Generative AI playground – Experiment with the chat or image playground
    • Shared generative AI assets – Explore generative AI applications and prompts shared with you.
  • Build with projects:
    • ML and generative AI model – Build, train, and deploy ML and foundation models with fully managed infrastructure, tools, and workflows.
    • Generative AI app development – Build generative AI apps and experiment with foundation models, prompts, agents, functions, and guardrails in Amazon Bedrock IDE.
    • Data processing and SQL analytics – Analyze, prepare, and integrate data for analytics and AI using Amazon Athena, Amazon EMR, AWS Glue, and Amazon Redshift.
    • Data and AI governance – Publish your data products to the catalog with glossaries and metadata forms. Govern access securely in the Amazon SageMaker Catalog built on Amazon DataZone.

With SageMaker Unified Studio, you now have a unified development experience across these services. You only need to learn these tools once and then you can use them across all services.

With SageMaker Unified Studio notebooks, you can use Python or Spark to interactively explore and visualize data, prepare data for analytics and ML, and train ML models. With the SQL editor, you can query data lakes, databases, data warehouses, and federated data sources. The SageMaker Unified Studio tools are integrated with Amazon Q, can quickly build, refine, and maintain applications with text-to-code capabilities.

In addition, SageMaker Unified Studio provides a unified view of an application’s building blocks such as data, code, development artifacts, and compute resources across services to approved users. This allows data engineers, data scientists, business analysts, and other data practitioners working from the same tool to quickly understand how an application works, seamlessly review each other’s work, and make the required changes.

Furthermore, SageMaker Unified Studio automates and simplifies access management for an application’s building blocks. After these building blocks are added to a project, they are automatically accessible to approved users from all tools—SageMaker Unified Studio configures any required service-specific permissions. With SageMaker Unified Studio, data practitioners can access all the capabilities of AWS purpose-built analytics, AI/ML, and generative AI services from a single unified development experience.

In the following sections, we walk through how to get started with SageMaker Unified Studio and some example use cases.

Create a SageMaker Unified Studio domain

Complete the following steps to create a new SageMaker Unified Studio domain:

  1. On the SageMaker platform console, choose Domains in the navigation pane.
  2. Choose Create domain.
  3. For How do you want to set up your domain?, select Quick setup (recommended for exploration).

Initially, no virtual private cloud (VPC) has been specifically set up for use with SageMaker Unified Studio, so you will see a dialog box prompting you to create a VPC.

  1. Choose Create VPC.

You’re redirected to the AWS CloudFormation console to deploy a stack to configure VPC resources.

  1. Choose Create stack, and wait for the stack to complete.
  2. Return to the SageMaker Unified Studio console, and inside the dialog box, choose the refresh icon.
  3. Under Quick setup settings, for Name, enter a name (for example, demo).
  4. For Domain Execution role, Domain Service role, Provisioning role, and Manage Access role, leave as default.
  5. For Virtual private cloud (VPC), verify that the new VPC you created in the CloudFormation stack is configured.
  6. For Subnets, verify that the new private subnets you created in the CloudFormation stack are configured.
  7. Choose Continue.
  8. For Create IAM Identity Center user, search for your SSO user through your email address.

If you don’t have an IAM Identity Center instance, you will be prompted to enter your name after your email address. This will create a new local IAM Identity Center instance.

  1. Choose Create domain.

Log in to the SageMaker Unified Studio

Now that you have created your new SageMaker Unified Studio domain, complete the following steps to visit the SageMaker Unified Studio:

  1. On the SageMaker platform console, open the details page of your domain.
  2. Choose the link for Amazon SageMaker Unified Studio URL.
  3. Log in with your SSO credentials.

Now you signed in to the SageMaker Unified Studio.

Create a project

The next step is to create a project. Complete the following steps:

  1. On the SageMaker Unified Studio, choose Select a project on the top menu, and choose Create project.
  2. For Project name, enter a name (for example, demo).
  3. For Project profile, choose Data analytics and AI-ML model development.
  4. Choose Continue.
  5. Review the input, and choose Create project.

You need to wait for the project to be created. Project creation can take about 5 minutes. Then the SageMaker Unified Studio console navigates you to the project’s home page.

Now you can use a variety of tools for your analytics, ML, and AI workload. In the following sections, we provide a few example use cases.

Process your data through a multi-compute notebook

SageMaker Unified Studio provides a unified JupyterLab experience across different languages, including SQL, PySpark, and Scala Spark. It also supports unified access across different compute runtimes such as Amazon Redshift and Amazon Athena for SQL, Amazon EMR Serverless, Amazon EMR on EC2, and AWS Glue for Spark.

Complete the following steps to get started with the unified JupyterLab experience:

  1. Open your SageMaker Unified Studio project page.
  2. On the top menu, choose Build, and under IDE & APPLICATIONS, choose JupyterLab.
  3. Wait for the space to be ready.
  4. Choose the plus sign and for Notebook, choose Python 3.

The following screenshot shows an example of the unified notebook page.

There are two dropdown menus on the top left of each cell. The Connection Type menu corresponds to connection types such as Local Python, PySpark, SQL, and so on.

The Compute menu corresponds to compute options such as Athena, AWS Glue, Amazon EMR, and so on.

  1. For the first cell, choose PySpark, spark, which defaults to AWS Glue for Spark, and enter the following code to initialize SparkSession and create a DataFrame from an Amazon Simple Storage Service (Amazon S3) path, then run the cell:
    from pyspark.sql import SparkSession
    
    spark = SparkSession.builder.getOrCreate()
    
    df1 = spark.read.format("csv") \
        .option("multiLine", "true") \
        .option("header", "false") \
        .option("sep", ",") \
        .load("s3://aws-blogs-artifacts-public/artifacts/BDB-4798/data/venue.csv")
    
    df1.show()

  2. For the next cell, enter the following code to rename columns and filter the records, and run the cell:
    df1_renamed = df1.withColumnsRenamed(
        {
            "_c0" : "venueid", 
            "_c1" : "venuename", 
            "_c2" : "venuecity", 
            "_c3" : "venuestate", 
            "_c4" : "venueseats"
        }
    )
    
    df1_filtered = df1_renamed.filter("`venuestate` == 'DC'")
    
    df1_filtered.show()

  3. For the next cell, enter the following code to create another DataFrame from another S3 path, and run the cell:
    df2 = spark.read.format("csv") \
        .option("multiLine", "true") \
        .option("header", "false") \
        .option("sep", ",") \
        .load("s3://aws-blogs-artifacts-public/artifacts/BDB-4798/data/events.csv")
    df2_renamed = df2.withColumnsRenamed(
        {
            "_c0" : "eventid", 
            "_c1" : "e_venueid", 
            "_c2" : "catid", 
            "_c3" : "dateid", 
            "_c4" : "eventname", 
            "_c5" : "starttime"
        }
    )
    
    df2_renamed.show()

  4. For the next cell, enter the following code to join the frames and apply custom SQL, and run the cell:
    df_joined = df2_renamed.join(df1_filtered, (df2_renamed['e_venueid'] == df1_filtered['venueid']), "inner")
    
    df_sql = spark.sql("""
        select 
            venuename, 
            count(distinct eventid) as eventid_count
        from {myDataSource}
        group by venuename
    """, myDataSource = df_joined)
    
    df_sql.show()

  5. For the next cell, enter following code to write to a table, and run the cell (replace the AWS Glue database name with your project database name, and the S3 path with your project’s S3 path):
    df_sql.write.format("parquet") \
        .option("path", "s3://amazon-sagemaker-123456789012-us-east-2-xxxxxxxxxxxxx/dzd_1234567890123/xxxxxxxxxxxxx/dev/venue_event_agg/") \
        .option("header", False) \
        .option("compression", "snappy") \
        .mode("overwrite") \
        .saveAsTable("`glue_db_abcdefgh`.`venue_event_agg`")

Now you have successfully ingested data to Amazon S3 and created a new table called venue_event_agg.

  1. In the next cell, switch the connection type from PySpark to SQL.
  2. Run following SQL against the table (replace the AWS Glue database name with your project database name):
    SELECT * FROM glue_db_abcdefgh.venue_event_agg

The following screenshot shows an example of the results.

The SQL ran on AWS Glue for Spark. Optionally, you can switch to other analytics engines like Athena by switching the compute.

Explore your data through a SQL Query Editor

In the previous section, you learned how the unified notebook works with different connection types and different compute engines. Next, let’s use the data explorer to explore the table you created using a notebook. Complete the following steps:

  1. On the project page, choose Data.
  2. Under Lakehouse, expand AwsDataCatalog.
  3. Expand your database starting from glue_db_.
  4. Choose venue_event_agg, choose Query with Athena.
  5. Choose Run all.

The following screenshot shows an example of the query result.

As you enter text in the query editor, you will notice it provides suggestions for statements. The SQL query editor provides real-time autocomplete suggestions as you write SQL statements, covering DML/DDL statements, clauses, functions, and schemas of your catalogs like databases, tables, and columns. This enables faster, error-free query building.

You can complete editing the query and run it.

You can also open a generative SQL assistant powered by Amazon Q to help your query authoring experience.

For example, you can ask “Calculate the sum of eventid_count across all venues” in the assistant, and the query is automatically suggested. You can choose Add to querybook to copy the suggested query is copied to the querybook, and run it.

Next, coming back to the original query, and let’s try a quick visualization to analyze the data distribution.

  1. Choose the chart view icon.
  2. Under Structure, choose Traces.
  3. For Type, choose Pie.
  4. For Values, choose eventid_count.
  5. For Labels, choose venuename.

The query result will display as a pie chart like the following example. You can customize the graph title, axis title, subplot styles, and more on the UI. The generated images can also be downloaded as PNG or JPEG files.

In the above instruction, you learned how the data explorer works with different visualizations.

Clean up

To clean up your resources, complete the following steps:

  1. Delete the AWS Glue table venue_event_agg and S3 objects under the table S3 path.
  2. Delete the project you created.
  3. Delete the domain you created.
  4. Delete the VPC named SageMakerUnifiedStudioVPC.

Conclusion

In this post, we demonstrated how SageMaker Unified Studio (preview) unifies your analytics workload. We also explained the end-to-end user experience of the SageMaker Unified Studio for two different use cases of notebook and query. Discover your data and put it to work using familiar AWS tools to complete end-to-end development workflows, including data analysis, data processing, model training, generative AI app building, and more, in a single governed environment. Create or join projects to collaborate with your teams, share AI and analytics artifacts securely, and discover and use your data stored in Amazon S3, Amazon Redshift, and more data sources through the Amazon SageMaker Lakehouse. As AI and analytics use cases converge, transform how data teams work together with SageMaker Unified Studio.

To learn more, visit Amazon SageMaker Unified Studio (preview).


About the Authors

Noritaka Sekiyama is a Principal Big Data Architect on the AWS Glue team. He works based in Tokyo, Japan. He is responsible for building software artifacts to help customers. In his spare time, he enjoys cycling with his road bike.

Chiho Sugimoto is a Cloud Support Engineer on the AWS Big Data Support team. She is passionate about helping customers build data lakes using ETL workloads. She loves planetary science and enjoys studying the asteroid Ryugu on weekends.

Zach Mitchell is a Sr. Big Data Architect. He works within the product team to enhance understanding between product engineers and their customers while guiding customers through their journey to develop data lakes and other data solutions on AWS analytics services.

Chanu Damarla is a Principal Product Manager on the Amazon SageMaker Unified Studio team. He works with customers around the globe to translate business and technical requirements into products that delight customers and enable them to be more productive with their data, analytics, and AI.

Introducing Buy with AWS: an accelerated procurement experience on AWS Partner sites, powered by AWS Marketplace

Post Syndicated from Prasad Rao original https://aws.amazon.com/blogs/aws/introducing-buy-with-aws-an-accelerated-procurement-experience-on-aws-partner-sites-powered-by-aws-marketplace/

Today, we are announcing Buy with AWS, a new way to discover and purchase solutions available in AWS Marketplace from AWS Partner sites. You can use Buy with AWS to accelerate and streamline your product procurement process on websites outside of Amazon Web Services (AWS). This feature provides you the ability to find, try, and buy solutions from Partner websites using your AWS account

AWS Marketplace is a curated digital store for you to find, buy, deploy, and manage cloud solutions from Partners. Buy with AWS is another step towards AWS Marketplace making it easy for you to find and procure the right Partner solutions, when and where you need them. You can conveniently find and procure solutions in AWS Marketplace, through integrated AWS service consoles, and now on Partner websites.

Accelerate cloud solution discovery and evaluation

You can now discover solutions from Partners available for purchase through AWS Marketplace as you explore solutions on the web beyond AWS.

Look for products that are “Available in AWS Marketplace” when browsing on Partner sites, then accelerate your evaluation process with fast access to free trials, demo requests, and inquiries for custom pricing.

For example, I want to evaluate Wiz to see how it can help with my cloud security requirements. While browsing the Wiz website, I come across a page where I see “Connect Wiz with Amazon Web Services (AWS)”.

Wiz webpage featuring Buy With AWS

I choose Try with AWS. It asks me to sign in to my AWS account if I’m not signed in already. I’m then presented with a Wiz and AWS co-branded page for me to sign up for the free trial.

Wiz and AWS co-branded page to sign up for free trial using Buy with AWS through AWS Marketplace

The discovery experience that you see will vary depending on type of the Partner website you’re shopping from. Wiz is an example of how Buy with AWS can be implemented by an independent software vendor (ISV). Now, let’s look at an example of an AWS Marketplace Channel Partner, or reseller, who operates a storefront of their own.

I browse to the Bytes storefront with product listings from AWS Marketplace. I have the option to filter and search from the curated product listings, which are available in AWS Marketplace, on the Bytes site.

Bytes storefront with product listings from AWS Marketplace

I choose View Details for Fortinet and see an option to Request Private Offer from AWS.

Bytes storefront with option to Request Private Offer for Fortinet from AWS Marketplace

As you can tell, on a Channel Partner site, you can browse curated product listings available in AWS Marketplace, filter products, and request custom pricing using your AWS account directly from their website.

Streamline product procurement on AWS Partner sites
I had a seamless experience using Buy with AWS to access a free trial for Wiz and browse through the Bytes storefront to request a private offer.

Now I want to try Databricks for one of the applications I’m building. I sign up for a Databricks trial through their website.

Database homepage after login with option to Upgrade

I chose Upgrade and see Databricks is available in AWS Marketplace, which gives me the option to Buy with AWS.

Option to upgrade to Databricks premium using Buy with AWS feature of AWS marketplace

I choose Buy with AWS, and after I sign in to my AWS account, I land on a Databricks and AWS Marketplace co-branded procurement page.

Databricks and AWS co-branded page to subscribe using Buy with AWS

I complete the purchase on the co-branded procurement page and continue to set up my Databricks account.

Databricks and AWS co-branded page after subscribing using Buy with AWS

As you can tell, I didn’t have to navigate the challenge of managing procurement processes for multiple vendors. I also didn’t have to speak with a sales representative or onboard a new vendor in my billing system, which would have required multiple approvals and delayed the overall process.

Access centralized billing and benefits through AWS Marketplace
Because Buy with AWS purchases are transacted through and managed in AWS Marketplace, you also benefit from the post-purchase experience of AWS Marketplace, including consolidated AWS billing, centralized subscription management, and access to cost optimization tools.

For example, through the AWS Billing and Cost Management console, I can centrally manage all my AWS purchases, including Buy with AWS purchases, from one dashboard. I can easily access and process invoices for all of my organization’s AWS purchases. I also need to have valid AWS Identity and Access Management (IAM) permissions to manage subscriptions and make a purchase through AWS Marketplace.

AWS Marketplace not only simplifies my billing but also helps in maintaining governance over spending by helping me manage purchasing authority and subscription access for my organization with centralized visibility and controls. I can manage my budget with pricing flexibility, cost transparency, and AWS cost management tools.

Buy with AWS for Partners
Buy with AWS enables Partners who sell or resell products in AWS Marketplace to create new solution discovery and buying experiences for customers on their own websites. By adding call to action (CTA) buttons to their websites such as “Buy with AWS”, “Try free with AWS”, “Request private offer”, and “Request demo”, Partners can help accelerate product evaluation and the path-to-purchase for customers.

By integrating AWS Marketplace APIs, Partners can display products from the AWS Marketplace catalog, allow customers to sort and filter products, and streamline private offers. Partners implementing Buy with AWS can access AWS Marketplace creative and messaging resources for guidance on building their own web experiences. Partners who implement Buy with AWS can access metrics for insights into engagement and conversion performance.

The Buy with AWS onboarding guide in the AWS Marketplace Management Portal details how Partners can get started.

Learn more
Visit the Buy with AWS page to learn more and explore Partner sites that offer Buy with AWS.

To learn more about selling or reselling products using Buy with AWS on your website, visit:

Prasad

Accelerate foundation model training and fine-tuning with new Amazon SageMaker HyperPod recipes

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/accelerate-foundation-model-training-and-fine-tuning-with-new-amazon-sagemaker-hyperpod-recipes/

Today, we’re announcing the general availability of Amazon SageMaker HyperPod recipes to help data scientists and developers of all skill sets to get started training and fine-tuning foundation models (FMs) in minutes with state-of-the-art performance. They can now access optimized recipes for training and fine-tuning popular publicly available FMs such as Llama 3.1 405B, Llama 3.2 90B, or Mixtral 8x22B.

At AWS re:Invent 2023, we introduced SageMaker HyperPod to reduce time to train FMs by up to 40 percent and scale across more than a thousand compute resources in parallel with preconfigured distributed training libraries. With SageMaker HyperPod, you can find the required accelerated compute resources for training, create the most optimal training plans, and run training workloads across different blocks of capacity based on the availability of compute resources.

SageMaker HyperPod recipes include a training stack tested by AWS, removing tedious work experimenting with different model configurations, eliminating weeks of iterative evaluation and testing. The recipes automate several critical steps, such as loading training datasets, applying distributed training techniques, automating checkpoints for faster recovery from faults, and managing the end-to-end training loop.

With a simple recipe change, you can seamlessly switch between GPU- or Trainium-based instances to further optimize training performance and reduce costs. You can easily run workloads in production on SageMaker HyperPod or SageMaker training jobs.

SageMaker HyperPod recipes in action
To get started, visit the SageMaker HyperPod recipes GitHub repository to browse training recipes for popular publicly available FMs.

You only need to edit straightforward recipe parameters to specify an instance type and the location of your dataset in cluster configuration, then run the recipe with a single line command to achieve state-of-art performance.

You need to edit the recipe config.yaml file to specify the model and cluster type after cloning the repository.

$ git clone --recursive https://github.com/aws/sagemaker-hyperpod-recipes.git
$ cd sagemaker-hyperpod-recipes
$ pip3 install -r requirements.txt.
$ cd ./recipes_collections
$ vim config.yaml

The recipes support SageMaker HyperPod with Slurm, SageMaker HyperPod with Amazon Elastic Kubernetes Service (Amazon EKS), and SageMaker training jobs. For example, you can set up a cluster type (Slurm orchestrator), a model name (Meta Llama 3.1 405B language model), an instance type (ml.p5.48xlarge), and your data locations, such as storing the training data, results, logs, and so on.

defaults:
- cluster: slurm # support: slurm / k8s / sm_jobs
- recipes: fine-tuning/llama/hf_llama3_405b_seq8k_gpu_qlora # name of model to be trained
debug: False # set to True to debug the launcher configuration
instance_type: ml.p5.48xlarge # or other supported cluster instances
base_results_dir: # Location(s) to store the results, checkpoints, logs etc.

You can optionally adjust model-specific training parameters in this YAML file, which outlines the optimal configuration, including the number of accelerator devices, instance type, training precision, parallelization and sharding techniques, the optimizer, and logging to monitor experiments through TensorBoard.

run:
  name: llama-405b
  results_dir: ${base_results_dir}/${.name}
  time_limit: "6-00:00:00"
restore_from_path: null
trainer:
  devices: 8
  num_nodes: 2
  accelerator: gpu
  precision: bf16
  max_steps: 50
  log_every_n_steps: 10
  ...
exp_manager:
  exp_dir: # location for TensorBoard logging
  name: helloworld 
  create_tensorboard_logger: True
  create_checkpoint_callback: True
  checkpoint_callback_params:
    ...
  auto_checkpoint: True # for automated checkpointing
use_smp: True 
distributed_backend: smddp # optimized collectives
# Start training from pretrained model
model:
  model_type: llama_v3
  train_batch_size: 4
  tensor_model_parallel_degree: 1
  expert_model_parallel_degree: 1
  # other model-specific params

To run this recipe in SageMaker HyperPod with Slurm, you must prepare the SageMaker HyperPod cluster following the cluster setup instruction.

Then, connect to the SageMaker HyperPod head node, access the Slurm controller, and copy the edited recipe. Next, you run a helper file to generate a Slurm submission script for the job that you can use for a dry run to inspect the content before starting the training job.

$ python3 main.py --config-path recipes_collection --config-name=config

After training completion, the trained model is automatically saved to your assigned data location.

To run this recipe on SageMaker HyperPod with Amazon EKS, clone the recipe from the GitHub repository, install the requirements, and edit the recipe (cluster: k8s) on your laptop. Then, create a link between your laptop and running the EKS cluster and subsequently use the HyperPod Command Line Interface (CLI) to run the recipe.

$ hyperpod start-job –recipe fine-tuning/llama/hf_llama3_405b_seq8k_gpu_qlora \
--persistent-volume-claims fsx-claim:data \
--override-parameters \
'{
  "recipes.run.name": "hf-llama3-405b-seq8k-gpu-qlora",
  "recipes.exp_manager.exp_dir": "/data/<your_exp_dir>",
  "cluster": "k8s",
  "cluster_type": "k8s",
  "container": "658645717510.dkr.ecr.<region>.amazonaws.com/smdistributed-modelparallel:2.4.1-gpu-py311-cu121",
  "recipes.model.data.train_dir": "<your_train_data_dir>",
  "recipes.model.data.val_dir": "<your_val_data_dir>",
}'

You can also run recipe on SageMaker training jobs using SageMaker Python SDK. The following example is running PyTorch training scripts on SageMaker training jobs with overriding training recipes.

...
recipe_overrides = {
    "run": {
        "results_dir": "/opt/ml/model",
    },
    "exp_manager": {
        "exp_dir": "",
        "explicit_log_dir": "/opt/ml/output/tensorboard",
        "checkpoint_dir": "/opt/ml/checkpoints",
    },   
    "model": {
        "data": {
            "train_dir": "/opt/ml/input/data/train",
            "val_dir": "/opt/ml/input/data/val",
        },
    },
}
pytorch_estimator = PyTorch(
           output_path=<output_path>,
           base_job_name=f"llama-recipe",
           role=<role>,
           instance_type="p5.48xlarge",
           training_recipe="fine-tuning/llama/hf_llama3_405b_seq8k_gpu_qlora",
           recipe_overrides=recipe_overrides,
           sagemaker_session=sagemaker_session,
           tensorboard_output_config=tensorboard_output_config,
)
...

As training progresses, the model checkpoints are stored on Amazon Simple Storage Service (Amazon S3) with the fully automated checkpointing capability, enabling faster recovery from training faults and instance restarts.

Now available
Amazon SageMaker HyperPod recipes are now available in the SageMaker HyperPod recipes GitHub repository. To learn more, visit the SageMaker HyperPod product page and the Amazon SageMaker AI Developer Guide.

Give SageMaker HyperPod recipes a try and send feedback to AWS re:Post for SageMaker or through your usual AWS Support contacts.

Channy

AWS Education Equity Initiative: Applying generative AI to educate the next wave of innovators

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-education-equity-initiative-applying-generative-ai-to-educate-the-next-wave-of-innovators/

Building on the work that we and our partners have been doing for many years, Amazon is committing up to $100 million in cloud technology and technical resources to help existing, dedicated learning organizations reach more learners by creating new and innovative digital learning solutions, all as part of the AWS Education Equity Initiative.

The Work So Far
AWS and Amazon have a long-standing commitment to learning and education. Here’s a sampling of what we have already done:

AWS AI & ML Scholarship Program – This program has awarded $28 million in scholarships to approximately 6000 students.

Machine Learning University – MLU offers a free program helping community colleges and Historically Black Colleges and Universities (HBCUs) teach data management, artificial intelligence, and machine learning concepts. The program is designed to address opportunity gaps by supporting students who are historically underserved and underrepresented in technology disciplines.

Amazon Future Engineer – Since 2021, up to $46 million in scholarships has been awarded to 1150 students through this program. In the past year, more than 2.1 million students received over 17 million hours of STEM education, literacy, and career exploration courses through this and other Amazon philanthropic education programs in the United States. I was able to speak to one such session last year and it was an amazing experience:

Free Cloud Training – In late 2020 we set a goal of helping 29 million people grow their tech skills with free cloud computing training by 2025. We worked hard and met that target a year ahead of time!

There’s More To Do
Despite all of this work and progress, there’s still more to be done. The future is definitely not evenly distributed: over half a billion students cannot be reached by digital learning today.

We believe that Generative AI can amplify the good work that socially-minded edtech organizations, non-profits, and governments are already doing. Our goal is to empower them to build new and innovative digital learning systems that can amplify their work and allow them to reach a bigger audience.

With the launch of the AWS Education Equity Initiative, we want to help pave the way for the next generation of technology pioneers as they build powerful tools, train foundation models at scale, and create AI-powered teaching assistants.

We are committing up to $100 million in cloud technology and comprehensive technical advising over the next five years. The awardees will have access to the portfolio of AWS services and technical expertise so that they can build and scale learning management systems, mobile apps, chatbots, and other digital learning tools. As part of the application process, applicants will be asked to demonstrate how their proposed solution will benefit students from underserved and underrepresented communities.

As I mentioned earlier, our partners are already doing a lot of great work in this area. For example:

Code.org has already used AWS to scale their free computer science curriculum to millions of students in more than 100 countries. With this initiative, they will expand their use of Amazon Bedrock to provide an automated assessment of student projects, freeing up educator time that can be use for individual instruction and tailored learning.

Rocket Learning focuses on early childhood education in India. They will use Amazon Q in QuickSight to enhance learning outcomes for more than three million children.

I’m super excited about this initiative and look forward to seeing how it will help to create and educate the next generation of technology pioneers!

Jeff;

Solve complex problems with new scenario analysis capability in Amazon Q in QuickSight

Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/solve-complex-problems-with-new-scenario-analysis-capability-in-amazon-q-in-quicksight/

Today, we announced a new capability of Amazon Q in QuickSight that helps users perform scenario analyses to find answers to complex problems quickly. This AI-assisted data analysis experience helps business users find answers to complex problems by guiding them step-by-step through in-depth data analysis—suggesting analytical approaches, automatically analyzing data, and summarizing findings with suggested actions—using natural language prompts. This new capability eliminates hours of tedious and error-prone manual work traditionally required to perform analyses using spreadsheets or other alternatives. In fact, Amazon Q in QuickSight enables business users to perform complex scenario analysis up to 10x faster than spreadsheets. This capability expands upon existing data Q&A capabilities of Amazon QuickSight so business professionals can start their analysis by simply asking a question.

How it works
Business users are often faced with complex questions that have traditionally required specialized training and days or weeks of time analyzing data in spreadsheets or other tools to address. For example, let’s say you’re a franchisee with multiple locations to manage. You might use this new capability in Amazon Q in QuickSight to ask, “How can I help our new Chicago store perform as well as the flagship store in New York?” Using an agentic approach, Amazon Q would then suggest analytical approaches needed to address the underlying business goal, automatically analyze data, and present results complete with visualizations and suggested actions. You can conduct this multistep analysis in an expansive analysis canvas, giving you the flexibility to make changes, explore multiple analysis paths simultaneously, and adapt to situations over time.

This new analysis experience is part of Amazon QuickSight meaning it can read from QuickSight dashboards which connect to sources such as Amazon Athena, Amazon Aurora, Amazon Redshift, Amazon Simple Storage Service (Amazon S3), and Amazon OpenSearch Service. Specifically, this new experience is part of Amazon Q in QuickSight, which allows it to seamlessly integrate with other generative business intelligence (BI) capabilities such as data Q&A. You can also upload either a .csv or a single-table, single-sheet .xlsx file to incorporate into your analysis.

Here’s a visual walkthrough of this new analysis experience in Amazon Q in QuickSight.

I’m planning a customer event, and I’ve received an Excel spreadsheet of all who’ve registered to attend the event. I want to learn more about the attendees, so I analyze the spreadsheet and ask a few questions. I start by describing what I want to explore.

I upload the spreadsheet to start my analysis. Firstly, I want to understand how many people have registered for the event.

To design an agenda that’s suitable for the audience, I want to understand the various roles that will be attending. I select on the + icon to add a new block for asking a question following along the thread from the previous block.

I can continue to ask more questions. However, there are suggested questions for analyzing my data even further, and I now select one of these suggested questions. I want to increase marketing efforts at companies that don’t currently have a lot of attendees in this case, companies with fewer than two attendees.

Amazon Q executes the required analysis and keeps me updated of the progress. Step 1 of the process identifies companies that have fewer than two attendees and lists them.

Step 2 gives an estimate of how many more attendees I might get from each company if marketing efforts are increased.

In Step 3 I can see the potential increase in total attendees (including the percentage increase) in line with the increase in marketing efforts.

Lastly, Step 4 goes even further to highlight companies I should prioritize for these increased marketing efforts.

To increase the potential number of attendees even more, I wanted to change the analysis to identify companies with fewer than three attendees instead of two attendees. I choose the AI sparkle icon in the upper right to launch a modal that I then use to provide more context and make specific changes to the previous result.


This change resulted in new projections, and I can choose to consider them for my marketing efforts or keep to the previous projections.


Now available
Amazon Q in QuickSight Pro users can use this new capability in preview in the following AWS Regions at launch: US East (N. Virginia) and US West (Oregon). Get started with a free 30-day trial of QuickSight today. To learn more, visit the Amazon QuickSight User Guide. You can submit your questions to AWS re:Post for Amazon QuickSight, or through your usual AWS Support contacts.

Veliswa.