From 4a72fbe855b28e9c7174c05e47902948c915e43e Mon Sep 17 00:00:00 2001 From: denice83v74389 Date: Mon, 7 Apr 2025 05:32:44 +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..3b467e0 --- /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://www.mudlog.net)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](https://jktechnohub.com) ideas on AWS.
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In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the [distilled variations](https://git.teygaming.com) of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) developed by [DeepSeek](https://www.soundofrecovery.org) [AI](https://www.bisshogram.com) that uses support discovering to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key differentiating function is its reinforcement learning (RL) action, which was utilized to fine-tune the model's reactions beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, ultimately improving both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, implying it's equipped to break down complicated inquiries and factor through them in a detailed manner. This [directed](https://right-fit.co.uk) thinking procedure permits the model to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation model that can be integrated into numerous workflows such as representatives, rational reasoning and data analysis jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, making it possible for effective reasoning by routing questions to the most pertinent professional "clusters." This method allows the model to concentrate on different issue domains while maintaining overall effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient models to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher model.
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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 recommend deploying this model with guardrails in place. In this blog site, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1384084) we will [utilize Amazon](http://www.thynkjobs.com) Bedrock Guardrails to introduce safeguards, avoid damaging material, and assess designs against essential security requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative [AI](http://carpetube.com) applications.
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Prerequisites
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To [release](https://pipewiki.org) 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 verify you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limit increase, create a limitation boost demand and reach out to your account team.
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Because you will be releasing this model 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 guidelines, 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](https://nusalancer.netnation.my.id) Guardrails allows you to present safeguards, prevent harmful content, and assess designs against [key security](https://git.ombreport.info) criteria. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and model responses 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 develop the guardrail, see the GitHub repo.
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The general flow 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 out to the model for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the last outcome. However, if either the input or output is stepped in by the guardrail, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2684771) a message is returned suggesting the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections show [inference](https://brotato.wiki.spellsandguns.com) using 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 actions:
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1. On the Amazon Bedrock console, select Model catalog under Foundation models in the [navigation](https://usa.life) pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and pick the DeepSeek-R1 model.
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The model detail page provides necessary details about the model's capabilities, prices structure, and application guidelines. You can find detailed usage guidelines, including sample API calls and code bits for [combination](http://81.70.93.2033000). The design supports different text [generation](https://git.mm-music.cn) tasks, consisting of material development, code generation, and question answering, [utilizing](https://gitlab.minet.net) its reinforcement learning optimization and CoT reasoning abilities. +The page also consists of release options and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, choose Deploy.
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You will be triggered to set up the implementation details for DeepSeek-R1. The model ID will be [pre-populated](https://scienetic.de). +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, go into a number of instances (in between 1-100). +6. For example type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure innovative security and facilities settings, consisting of virtual private cloud (VPC) networking, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:ChetBrauer26) service function approvals, and encryption settings. For the majority of use cases, [surgiteams.com](https://surgiteams.com/index.php/User:HannaJohnston36) the default settings will work well. However, for production releases, you may want to evaluate these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the implementation is total, you can check 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 different prompts and change design criteria like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For instance, content for reasoning.
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This is an outstanding method to check out the model's reasoning and text [generation abilities](http://81.70.93.2033000) before integrating it into your applications. The play area provides immediate feedback, [assisting](http://football.aobtravel.se) you understand how the design reacts to different inputs and letting you fine-tune your triggers for optimum outcomes.
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You can [rapidly](http://182.92.143.663000) test the model in the play ground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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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 reasoning [utilizing](https://20.112.29.181) a deployed DeepSeek-R1 model through Amazon Bedrock [utilizing](http://221.131.119.210030) 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, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning parameters, and sends out a request to generate text based on a user prompt.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML options 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 information, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides two hassle-free methods: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you choose the approach that best fits 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 produce a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model browser displays available designs, with details like the service provider name and design [abilities](http://svn.ouj.com).
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card shows crucial details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if relevant), [suggesting](http://demo.ynrd.com8899) that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to see the design details page.
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The design details page includes the following details:
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- The model name and company details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
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The About tab consists of important details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage guidelines
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Before you deploy the model, it's advised to evaluate the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, use the instantly created name or create a customized one. +8. For example type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of [circumstances](http://124.223.222.613000) (default: 1). +Selecting suitable circumstances types and counts is vital for expense and performance optimization. Monitor your deployment to change 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 accuracy. For this design, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to deploy the model.
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The release procedure can take several minutes to complete.
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When implementation is total, your endpoint status will change to InService. At this point, the design is ready to accept inference demands through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is complete, you can invoke the design using a SageMaker runtime [customer](http://39.106.177.1608756) and incorporate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the needed AWS consents and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for [releasing](https://ssconsultancy.in) the design is provided in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run extra demands against the predictor:
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Implement guardrails and run inference 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 create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as revealed in the following code:
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Tidy up
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To prevent unwanted charges, complete the steps in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the design using Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under [Foundation designs](http://www.buy-aeds.com) in the navigation pane, choose Marketplace deployments. +2. In the Managed releases section, find the [endpoint](https://coolroomchannel.com) you wish to erase. +3. Select the endpoint, and on the Actions menu, pick 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 model you released will sustain expenses if you leave it running. Use the following code to erase the endpoint if you wish to stop sustaining charges. For more details, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:ConnorPoorman) see Delete Endpoints and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:TerriJasso9) 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 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. 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, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:Bettina5096) and Getting going with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions Architect for at AWS. He assists emerging generative [AI](https://rejobbing.com) companies build ingenious solutions utilizing AWS services and accelerated compute. Currently, he is focused on establishing strategies for fine-tuning and enhancing the reasoning performance of big language designs. In his leisure time, Vivek takes pleasure in treking, seeing movies, and attempting different [cuisines](https://atomouniversal.com.br).
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Niithiyn Vijeaswaran is a Generative [AI](https://wishjobs.in) Specialist Solutions Architect with the Third-Party Model [Science](https://git.wisder.net) team at AWS. His area of focus is AWS [AI](https://git.alternephos.org) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://executiverecruitmentltd.co.uk) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://pioneerayurvedic.ac.in) center. She is enthusiastic about building solutions that help clients accelerate their [AI](http://182.92.143.66:3000) journey and unlock business value.
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