From 73dc11a7d96bd132fcd284ec1c3372e1a89cfbf8 Mon Sep 17 00:00:00 2001 From: rickbeebe91258 Date: Sun, 16 Feb 2025 13:16:15 +0800 Subject: [PATCH] Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart --- ...tplace 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..1b3971a --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are [thrilled](https://git.frugt.org) to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://git.bzgames.cn)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative [AI](https://links.gtanet.com.br) ideas on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled versions of the models as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](https://rightlane.beparian.com) that utilizes support discovering to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial identifying feature is its reinforcement knowing (RL) action, which was utilized to refine the design's reactions beyond the basic pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, eventually enhancing both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, indicating it's equipped to break down intricate questions and reason through them in a detailed manner. This assisted [thinking procedure](https://www.elitistpro.com) allows the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation design that can be integrated into numerous workflows such as agents, sensible thinking and information interpretation jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion [criteria](https://www.pakalljobz.com) in size. The MoE architecture allows activation of 37 billion specifications, allowing efficient reasoning by routing queries to the most pertinent expert "clusters." This approach enables the model to focus on various issue domains while maintaining overall efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 [xlarge features](https://www.vfrnds.com) 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more efficient designs to [simulate](https://audioedu.kyaikkhami.com) the habits and reasoning patterns of the larger DeepSeek-R1 model, using it as a teacher design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and evaluate models against essential safety requirements. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails [tailored](https://code.thintz.com) to various usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security throughout your generative [AI](https://socialcoin.online) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, [pick Amazon](https://nycu.linebot.testing.jp.ngrok.io) SageMaker, and verify 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 request a limit boost, develop a limitation boost request and connect to your [account](http://gitlab.andorsoft.ad) group.
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Because you will be [releasing](http://47.122.26.543000) this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For instructions, see Establish consents 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 introduce safeguards, prevent harmful material, and assess models against crucial safety requirements. You can carry out precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use [guardrails](http://git.spaceio.xyz) to assess user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon [Bedrock console](http://8.140.200.2363000) or the API. For the example code to create the guardrail, see the GitHub repo.
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The general circulation involves 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 inference. After receiving the design's output, another guardrail check is used. If the output passes this last check, it's returned as the last result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the [intervention](https://sagemedicalstaffing.com) and whether it took place at the input or output stage. The examples showcased in the following areas show inference utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, pick Model catalog under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 model.
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The model detail page supplies essential details about the design's abilities, prices structure, and execution guidelines. You can discover detailed use guidelines, including sample API calls and code bits for combination. The design supports numerous text generation jobs, consisting of content development, code generation, and concern answering, utilizing its support learning optimization and CoT reasoning abilities. +The page also consists of implementation alternatives and licensing details to help you get started with DeepSeek-R1 in your [applications](https://casajienilor.ro). +3. To begin utilizing DeepSeek-R1, pick Deploy.
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You will be triggered to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 [alphanumeric](http://b-ways.sakura.ne.jp) characters). +5. For Variety of instances, get in a variety of instances (between 1-100). +6. For example type, choose your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role permissions, and encryption settings. For many use cases, the default settings will work well. However, for production releases, you might wish to review these [settings](https://audioedu.kyaikkhami.com) to line up with your organization's security and compliance requirements. +7. [Choose Deploy](https://meetcupid.in) to start using the design.
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When the release is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive user interface where you can try out various prompts and change design criteria like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For instance, material for reasoning.
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This is an excellent way to check out the design's thinking and text generation capabilities before incorporating it into your applications. The play ground supplies immediate feedback, assisting you comprehend how the model reacts to numerous inputs and letting you tweak your triggers for optimum results.
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You can quickly check the design in the playground through the UI. However, to invoke the released design [programmatically](https://thedatingpage.com) with any Amazon Bedrock 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 carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock [console](https://gitea.qianking.xyz3443) or the API. For the example code to produce the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to carry out [guardrails](https://musicplayer.hu). The script initializes the bedrock_runtime client, sets up inference parameters, and sends out a demand to produce 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 services that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained [designs](https://git.luoui.com2443) to your usage case, with your information, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 hassle-free methods: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both approaches to help you pick the approach that finest fits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be prompted to produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model web browser displays available designs, with details like the company name and design capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card shows key details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if appropriate), suggesting that this model can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the design card to see the design details page.
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The model details page consists of the following details:
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- The design name and service provider details. +[Deploy button](https://gogolive.biz) 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](https://git.buzhishi.com14433) [description](https://ehrsgroup.com). +- License details. +- Technical specifications. +- Usage standards
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Before you deploy the design, it's suggested to review the model details and license terms to validate compatibility with your use case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, use the instantly produced name or produce a custom one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the number of instances (default: 1). +[Selecting proper](http://139.9.60.29) instance types and counts is [essential](http://39.98.79.181) for expense and [performance optimization](https://git.yuhong.com.cn). Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. [Choose Deploy](http://moyora.today) to deploy the design.
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The implementation procedure can take several minutes to finish.
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When release is complete, your endpoint status will alter to InService. At this point, the model is all set to accept inference demands through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation is complete, you can conjure up 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 going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS permissions and [environment setup](http://recruitmentfromnepal.com). The following is a [detailed](http://www.tomtomtextiles.com) code example that [demonstrates](https://edtech.wiki) how to deploy and use DeepSeek-R1 for inference programmatically. The code for [releasing](https://www.wakewiki.de) the model is offered in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra requests against the predictor:
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Implement guardrails and run reasoning with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
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Tidy up
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To avoid undesirable charges, complete the steps in this section to tidy up your [resources](http://daeasecurity.com).
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Delete the Amazon Bedrock Marketplace implementation
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If you released the design using Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under [Foundation](http://47.76.141.283000) models in the navigation pane, choose Marketplace deployments. +2. In the Managed releases area, find the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the proper implementation: 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 deployed will sustain costs if you leave it running. Use the following code to erase the endpoint if you desire to stop [sustaining charges](https://musicplayer.hu). For more details, see Delete Endpoints and Resources.
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Conclusion
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In this post, we [checked](https://www.jobzalerts.com) out how you can access and release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://git.jerl.dev) [companies develop](http://47.112.200.2063000) innovative solutions using AWS services and sped up calculate. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the reasoning performance of big language designs. In his spare time, Vivek enjoys treking, watching motion pictures, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://tayseerconsultants.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://jobsnotifications.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on [generative](https://eastcoastaudios.in) [AI](https://www.shwemusic.com) with the Third-Party Model [Science](http://47.112.106.1469002) group at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, [wavedream.wiki](https://wavedream.wiki/index.php/User:VeroniqueBernhar) SageMaker's artificial intelligence and generative [AI](https://code.dev.beejee.org) hub. She is passionate about constructing services that assist consumers accelerate their [AI](https://git.weingardt.dev) journey and unlock company worth.
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