From 9e786c266390347e6ed72630f1d56b0657e9c842 Mon Sep 17 00:00:00 2001 From: lorenzawilhoit Date: Sat, 5 Apr 2025 03:35:10 +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..262fc4b --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are excited 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](http://www.colegio-sanandres.cl)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative [AI](https://prime-jobs.ch) concepts on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock [Marketplace](http://161.97.176.30) and SageMaker JumpStart. You can follow comparable actions to release the distilled versions of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](https://hitechjobs.me) that uses support finding out to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating feature is its reinforcement learning (RL) step, which was utilized to fine-tune the design's actions beyond the basic pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately enhancing both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it's [equipped](https://boonbac.com) to break down complex inquiries and factor through them in a detailed way. This directed reasoning process permits the model to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has recorded the market's attention as a versatile text-generation design that can be incorporated into different workflows such as agents, rational thinking and information analysis tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, allowing effective inference by routing queries to the most pertinent professional "clusters." This approach enables the model to concentrate on various [issue domains](https://youarealways.online) while maintaining total efficiency. DeepSeek-R1 requires a minimum of 800 GB of [HBM memory](https://www.ayc.com.au) in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models 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 describes a process of training smaller sized, more efficient designs to simulate the behavior and thinking patterns of the larger DeepSeek-R1 design, using it as an instructor model.
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You can DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this design with guardrails in place. In this blog site, [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:WillaOliver18) we will utilize Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and evaluate models against key safety criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative [AI](https://app.joy-match.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you require 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 validate you're using 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 deploying. To request a limitation increase, produce a limit increase request and reach out to your account team.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To [Management](https://www.hyxjzh.cn13000) (IAM) permissions to use Amazon Bedrock Guardrails. For directions, see Establish authorizations 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, avoid hazardous content, and assess models against essential security criteria. You can execute precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the [GitHub repo](https://gitlab.iue.fh-kiel.de).
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The basic circulation involves the following steps: First, the system gets an input for the model. This input is then [processed](https://kennetjobs.com) through the ApplyGuardrail API. If the input passes the [guardrail](https://bethanycareer.com) check, it's sent to the design for reasoning. After getting the design's output, another guardrail check is applied. If the output passes this final check, it's [returned](https://git.hichinatravel.com) as the result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following areas show reasoning 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](http://it-viking.ch). To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for [DeepSeek](https://admithel.com) as a company and pick the DeepSeek-R1 design.
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The design detail page supplies vital [details](https://mediawiki1263.00web.net) about the model's abilities, pricing structure, and execution guidelines. You can discover detailed use guidelines, consisting of sample API calls and code bits for combination. The design supports various text generation tasks, including content development, code generation, and concern answering, utilizing its reinforcement learning optimization and CoT thinking abilities. +The page also consists of release alternatives and [licensing](http://114.55.54.523000) details to help you begin with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, choose Deploy.
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You will be prompted to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, get in a number of instances (in between 1-100). +6. For Instance type, pick your instance type. For optimum efficiency with DeepSeek-R1, a [GPU-based instance](https://nmpeoplesrepublick.com) type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up innovative security and infrastructure settings, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) including virtual personal cloud (VPC) networking, service role approvals, and file encryption settings. For most [utilize](http://47.100.3.2093000) cases, the default settings will work well. However, for production implementations, you might desire to examine these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to begin using the design.
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When the deployment is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play area to access an interactive interface where you can experiment with various triggers and adjust design criteria like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For example, content for inference.
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This is an exceptional way to check out the model's reasoning and text generation capabilities before integrating it into your applications. The play area supplies immediate feedback, helping you understand how the model reacts to various inputs and letting you fine-tune your triggers for optimum outcomes.
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You can rapidly evaluate the model in the play area through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need to get the [endpoint ARN](https://empleos.dilimport.com).
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Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to perform inference using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:BrittnyJsd) the example code to create the guardrail, see the GitHub repo. After you have actually [produced](http://stream.appliedanalytics.tech) the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up reasoning criteria, and sends a demand to create [text based](https://www.sedatconsultlimited.com) on a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML solutions that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and [release](https://www.wikispiv.com) them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides two hassle-free approaches: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you choose the technique that finest matches your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release 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 develop a domain. +3. On the SageMaker Studio console, [gratisafhalen.be](https://gratisafhalen.be/author/chasehuang/) select JumpStart in the navigation pane.
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The design web browser displays available models, with details like the company name and model capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card reveals key details, including:
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- Model name +[- Provider](https://xn--pm2b0fr21aooo.com) name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if appropriate), showing that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to [conjure](https://www.teamswedenclub.com) up the design
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5. Choose the model card to see the model details page.
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The design details page consists of the following details:
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- The model name and service provider details. +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 requirements. +- Usage guidelines
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Before you deploy the model, it's advised to review the design 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 immediately produced name or create a customized 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 proper instance types and counts is essential for cost and performance optimization. Monitor your deployment 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 configurations for precision. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. [Choose Deploy](https://freeads.cloud) to release the design.
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The deployment procedure can take numerous minutes to finish.
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When deployment is complete, your endpoint status will change to InService. At this point, the design is all set to accept inference requests through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation 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 started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for [releasing](https://aloshigoto.jp) the design is [offered](http://git.superiot.net) in the Github here. You can clone the note pad 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using 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 prevent undesirable charges, complete the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the model utilizing Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace implementations. +2. In the Managed deployments area, find the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're deleting the correct deployment: 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 checked out how you can access and deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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](http://git.yang800.cn) companies construct innovative options using AWS services and accelerated calculate. Currently, he is concentrated on developing strategies for fine-tuning and enhancing the inference performance of big language models. In his leisure time, [Vivek enjoys](https://bestremotejobs.net) hiking, viewing films, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://101.51.106.216) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://edge1.co.kr) [accelerators](http://123.207.52.1033000) (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect working on generative [AI](http://118.89.58.19:3000) with the Third-Party Model [Science](https://jobs.360career.org) group at AWS.
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[Banu Nagasundaram](http://82.156.184.993000) leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [it-viking.ch](http://it-viking.ch/index.php/User:ChassidyR67) generative [AI](https://www.designxri.com) center. She is passionate about constructing solutions that assist customers accelerate their [AI](https://howtolo.com) journey and unlock company value.
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