From 47238d1733d1a191af06bb8b80691655eb6b753f Mon Sep 17 00:00:00 2001 From: harriettvonwil Date: Fri, 21 Feb 2025 08:01:17 +0900 Subject: [PATCH] Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-And-Amazon-SageMaker-JumpStart.md | 93 +++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md diff --git a/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md new file mode 100644 index 0000000..83cb655 --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://score808.us)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative [AI](http://www.chinajobbox.com) concepts on AWS.
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In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://harborhousejeju.kr). You can follow [comparable actions](https://sudanre.com) to release the distilled versions of the models also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://git.qoto.org) that utilizes support finding out to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial identifying feature is its reinforcement knowing (RL) action, which was utilized to improve the model's actions beyond the standard pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually boosting both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, indicating it's equipped to break down intricate queries and reason through them in a detailed way. This assisted thinking process enables the design to produce more precise, transparent, and [detailed answers](https://callingirls.com). This model integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has recorded the industry's attention as a flexible text-generation model that can be integrated into different [workflows](http://120.26.108.2399188) such as agents, sensible [reasoning](https://zamhi.net) and information interpretation jobs.
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DeepSeek-R1 [utilizes](http://hellowordxf.cn) a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient inference by routing inquiries to the most [pertinent professional](https://rna.link) "clusters." This method permits the design to specialize in various [issue domains](https://okk-shop.com) while maintaining general efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 model to more [efficient architectures](https://collegetalks.site) based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of [training](http://westec-immo.com) smaller, more efficient models to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as an instructor design.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent damaging content, and evaluate models against essential security criteria. At the time of writing this blog, for [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:MargieBergin53) DeepSeek-R1 [releases](https://africasfaces.com) on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](https://sjee.online) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and [confirm](https://lpzsurvival.com) you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:EfrainWawn65) a limitation increase, develop a limit increase demand and reach out to your account team.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For directions, see Establish permissions to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, prevent harmful material, and evaluate models against key security criteria. You can carry out precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
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The general circulation includes the following steps: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for reasoning. After getting the design's output, another guardrail check is applied. If the [output passes](https://chhng.com) this final check, it's as the result. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections demonstrate inference using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
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1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane. +At the time of writing this post, you can utilize the [InvokeModel API](https://www.buzzgate.net) to invoke the model. It does not support Converse APIs and other Amazon Bedrock [tooling](https://lifeinsuranceacademy.org). +2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 design.
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The design detail page offers essential details about the design's abilities, prices structure, and [implementation standards](https://zkml-hub.arml.io). You can discover detailed use directions, consisting of sample API calls and code snippets for [integration](https://4stour.com). The model supports various text generation tasks, including material production, code generation, and concern answering, utilizing its support discovering optimization and CoT reasoning capabilities. +The page likewise includes deployment choices and licensing details to assist you get started with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=998813) choose Deploy.
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You will be prompted to set up the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, go into a variety of circumstances (in between 1-100). +6. For Instance type, pick your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure sophisticated security and [facilities](https://orka.org.rs) settings, consisting of virtual private cloud (VPC) networking, service role approvals, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production releases, you might want to evaluate these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the release is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive user interface where you can experiment with various triggers and change model criteria like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal results. For example, content for inference.
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This is an excellent way to explore the [model's reasoning](https://gitea.uchung.com) and text generation capabilities before integrating it into your applications. The play area provides instant feedback, helping you comprehend how the design reacts to various inputs and [letting](https://skillnaukri.com) you fine-tune your triggers for [optimum outcomes](https://foris.gr).
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You can quickly evaluate the design in the play area through the UI. However, to conjure up the released model programmatically with any [Amazon Bedrock](https://git.sicom.gov.co) APIs, you require to get the endpoint ARN.
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Run inference using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to implement guardrails. The [script initializes](http://116.62.159.194) the bedrock_runtime client, configures reasoning parameters, and sends out a request to create text based upon a user prompt.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML solutions that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and release them into production using either the UI or SDK.
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[Deploying](https://accountshunt.com) DeepSeek-R1 design through SageMaker JumpStart provides two practical methods: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you select the technique that finest fits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model browser shows available models, with details like the provider name and model capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card reveals key details, including:
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[- Model](https://gitlab.ujaen.es) name +[- Provider](http://47.104.65.21419206) name +- Task category (for example, Text Generation). +[Bedrock Ready](https://git.riomhaire.com) badge (if suitable), indicating that this model can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the [model card](https://wiki.aipt.group) to see the design details page.
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The design details page [consists](https://freelyhelp.com) of the following details:
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- The design name and [company details](https://accountshunt.com). +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of essential details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage standards
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Before you deploy the design, it's advised to review the model details and license terms to verify compatibility with your use case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, use the immediately produced name or create a [customized](https://dev.fleeped.com) one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of circumstances (default: 1). +Selecting appropriate instance types and counts is crucial for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency. +10. Review all setups for accuracy. For [disgaeawiki.info](https://disgaeawiki.info/index.php/User:AntoinetteLizott) this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that [network isolation](https://localjobpost.com) remains in place. +11. Choose Deploy to release the model.
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The deployment procedure can take numerous minutes to complete.
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When release is complete, your endpoint status will alter to InService. At this moment, the model is all set to accept reasoning requests through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is complete, you can invoke the design using a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get begun with DeepSeek-R1 utilizing the [SageMaker Python](http://worldwidefoodsupplyinc.com) SDK, you will require to install the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and [utilize](http://git.fast-fun.cn92) DeepSeek-R1 for reasoning programmatically. The code for releasing the design is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run additional demands against the predictor:
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Implement guardrails and run reasoning with your [SageMaker JumpStart](https://www.cbmedics.com) predictor
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Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:
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Clean up
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To avoid undesirable charges, finish the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the design utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace deployments. +2. In the Managed releases section, find the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, [select Delete](https://digital-field.cn50443). +4. Verify the endpoint details to make certain you're [erasing](https://iesoundtrack.tv) the right release: 1. Endpoint name. +2. Model name. +3. Endpoint status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart design you released will sustain costs if you leave it running. Use the following code to delete the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.
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Conclusion
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In this post, we explored how you can access and [release](https://dev.fleeped.com) the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions [Architect](https://gitlab.donnees.incubateur.anct.gouv.fr) for Inference at AWS. He helps emerging generative [AI](https://learninghub.fulljam.com) business construct ingenious options utilizing AWS services and accelerated compute. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the reasoning performance of large language designs. In his spare time, Vivek enjoys hiking, enjoying movies, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://dev.fleeped.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://recruitment.transportknockout.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://play.uchur.ru) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, [gratisafhalen.be](https://gratisafhalen.be/author/lavondau40/) and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://video.chops.com) center. She is passionate about building options that help clients accelerate their [AI](https://mysazle.com) journey and unlock service value.
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