From 43911e7b6ad674267274914d2cc3068170ded9ed Mon Sep 17 00:00:00 2001 From: mickeycherry62 Date: Wed, 26 Feb 2025 13:29:31 -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..ab3b43b --- /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 reveal that [DeepSeek](https://gitea.urkob.com) 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](https://www.frigorista.org)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion criteria to construct, experiment, and properly scale your [generative](http://62.234.201.16) [AI](https://stroijobs.com) ideas on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps 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 model (LLM) established by DeepSeek [AI](https://git.sunqida.cn) that utilizes support learning to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing feature is its reinforcement learning (RL) step, which was utilized to fine-tune the model's reactions beyond the standard pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually boosting both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, implying it's [equipped](http://1.94.30.13000) to break down complex inquiries and factor through them in a detailed way. This guided thinking procedure enables the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based [fine-tuning](https://www.bjs-personal.hu) with CoT capabilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually captured the industry's attention as a flexible text-generation design that can be incorporated into different workflows such as representatives, logical thinking and data analysis jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion specifications, allowing effective inference by routing [questions](https://git.magesoft.tech) to the most [relevant professional](http://cjma.kr) "clusters." This method permits the design to focus on different issue domains while maintaining total [efficiency](http://154.64.253.773000). DeepSeek-R1 requires 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 model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 model 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, more [effective models](http://59.110.125.1643062) to [simulate](http://internetjo.iwinv.net) the habits and thinking patterns of the bigger DeepSeek-R1 design, using 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 design, we advise releasing this design with guardrails in location. In this blog site, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and assess models against crucial safety criteria. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to different use cases and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:WilmaOrtega5) use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](https://tawtheaf.com) applications.
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Prerequisites
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To deploy 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, select Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To request a limitation boost, develop a limit increase request and connect to your account group.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For directions, see Set up consents to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, avoid harmful content, and evaluate designs against crucial safety requirements. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and model responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
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The basic circulation includes the following steps: First, the system [receives](http://team.pocketuniversity.cn) 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 getting the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas show reasoning utilizing 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, total the following steps:
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1. On the Amazon Bedrock console, choose Model brochure under Foundation designs in the [navigation](https://git.parat.swiss) pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [company](https://www.trabahopilipinas.com) and choose the DeepSeek-R1 model.
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The model detail page supplies essential details about the model's capabilities, rates structure, [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/halleybodin) and execution guidelines. You can find detailed usage guidelines, including sample API calls and code bits for combination. The model supports various text generation jobs, [consisting](https://azaanjobs.com) of [material](http://13.228.87.95) development, code generation, and concern answering, using its reinforcement learning optimization and CoT reasoning capabilities. +The page likewise includes deployment choices and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
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You will be triggered to set up the release details for DeepSeek-R1. The design ID will be [pre-populated](https://tapeway.com). +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, enter a variety of circumstances (between 1-100). +6. For example type, choose your circumstances type. For with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure sophisticated security and infrastructure settings, including virtual private cloud (VPC) networking, service role approvals, and encryption settings. For a lot of use cases, the default settings will work well. However, for [production](https://www.iratechsolutions.com) deployments, you may wish to examine these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the deployment is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive user interface where you can try out different triggers and change model parameters like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal outcomes. For example, material for inference.
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This is an excellent way to check out the model's reasoning and text generation abilities before integrating it into your applications. The play ground provides immediate feedback, helping you understand how the design reacts to various inputs and letting you fine-tune your prompts for optimum outcomes.
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You can rapidly check the model in the play area through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example [demonstrates](https://fumbitv.com) how to perform inference using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, and sends a demand to generate text based 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) hub with FMs, integrated algorithms, and prebuilt ML services that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through [SageMaker JumpStart](https://openedu.com) uses two practical techniques: using the [user-friendly SageMaker](http://120.77.209.1763000) JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to help you choose the approach that finest suits 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](https://gitlab.lycoops.be) JumpStart:
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1. On the [SageMaker](https://testgitea.educoder.net) console, pick Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The design web browser shows available designs, with details like the supplier name and design abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card reveals key details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if suitable), suggesting that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the model
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5. Choose the design card to view the design details page.
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The [model details](https://hgarcia.es) page includes the following details:
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- The model name and provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab includes important details, such as:
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- Model description. +- License details. +- Technical [specifications](https://git.coalitionofinvisiblecolleges.org). +- Usage standards
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Before you deploy the design, 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 proceed with deployment.
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7. For Endpoint name, utilize the automatically created name or [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1330524) create a custom-made one. +8. For example type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of instances (default: 1). +Selecting appropriate circumstances types and counts is vital for [yewiki.org](https://www.yewiki.org/User:EltonDalziel) cost and performance optimization. Monitor your [release](https://romancefrica.com) to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all [configurations](https://sebagai.com) for accuracy. For this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to release the model.
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The release procedure can take numerous minutes to finish.
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When deployment is total, your endpoint status will change to InService. At this point, [oeclub.org](https://oeclub.org/index.php/User:AntonyW4389122) the model is all set to [accept inference](http://64.227.136.170) requests through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation is complete, you can invoke the model using a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 [utilizing](http://47.92.27.1153000) the [SageMaker Python](https://myjobasia.com) SDK
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To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the [SageMaker Python](https://www.proathletediscuss.com) SDK and make certain you have the necessary AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model 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 predictor
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Similar to Amazon Bedrock, you can also utilize 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 displayed in the following code:
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Clean up
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To prevent undesirable charges, finish the actions in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the model using Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace deployments. +2. In the Managed deployments section, find the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:AlejandraMonsen) choose Delete. +4. Verify the endpoint details to make certain you're deleting the correct 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 expenses if you leave it running. Use the following code to delete the endpoint if you wish 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](https://www.buzzgate.net) 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](http://39.100.139.16) 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](https://gogs.jublot.com) 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://1024kt.com:3000) companies build innovative solutions utilizing AWS services and sped up calculate. Currently, he is focused on establishing strategies for fine-tuning and enhancing the inference performance of big language designs. In his leisure time, [Vivek delights](https://mixedwrestling.video) in hiking, seeing films, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://release.rupeetracker.in) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://www.ntcinfo.org) [accelerators](https://www.acaclip.com) (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](https://gitea.urkob.com) with the Third-Party Model [Science](http://www.xn--he5bi2aboq18a.com) team at AWS.
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Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://120.79.7.122:3000) hub. She is enthusiastic about developing options that help clients accelerate their [AI](https://git.uzavr.ru) journey and unlock service value.
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