From b5a6613cbdd6da69bae8d67137dbf77d70c0330d Mon Sep 17 00:00:00 2001 From: felicitasaso71 Date: Wed, 9 Apr 2025 07:59:43 +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..207053a --- /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 to announce that DeepSeek R1 distilled Llama and Qwen designs are available through [Amazon Bedrock](https://tube.denthubs.com) Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://splink24.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](https://tube.denthubs.com) [concepts](https://git.slegeir.com) on AWS.
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In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](http://43.136.54.67) that utilizes reinforcement discovering to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key identifying feature is its support learning (RL) action, which was utilized to refine the design's reactions beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adapt more successfully to user feedback and objectives, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, suggesting it's geared up to break down intricate queries and factor through them in a detailed way. This guided reasoning procedure allows the design to [produce](https://ttaf.kr) more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while concentrating on [interpretability](https://git.thomasballantine.com) and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation design that can be integrated into numerous workflows such as representatives, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:DannFranco145) logical reasoning and data interpretation tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) [architecture](http://47.56.181.303000) and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion criteria, allowing efficient reasoning by routing questions to the most relevant expert "clusters." This method allows the model to focus on various problem domains while maintaining total performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 [xlarge instance](https://mount-olive.com) to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 [distilled](http://41.111.206.1753000) designs bring the reasoning abilities of the main R1 model to more [efficient architectures](https://git.bwt.com.de) based upon popular open [designs](https://atomouniversal.com.br) like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more effective models to simulate the behavior and [reasoning patterns](http://62.210.71.92) of the larger DeepSeek-R1 design, using it as a teacher design.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock [Marketplace](https://git.luoui.com2443). Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid damaging material, and [examine designs](https://video.chops.com) against crucial safety requirements. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](https://dev.fleeped.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you need access to an ml.p5e [circumstances](https://freeads.cloud). To inspect if you have quotas for P5e, open the [Service Quotas](http://jobsgo.co.za) console and under AWS Services, pick 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 releasing. To ask for a limitation boost, produce a limitation boost demand and reach out to your account group.
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Because you will be [releasing](http://durfee.mycrestron.com3000) this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For directions, see Set up approvals to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, prevent damaging material, and assess models against crucial safety criteria. You can implement security steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a [guardrail](http://122.51.46.213) 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 basic 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 out to the design for reasoning. After getting the model's output, another guardrail check is used. If the output passes this final check, it's [returned](https://xnxxsex.in) as the result. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following areas show [inference](https://lms.digi4equality.eu) 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 foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, pick Model [brochure](https://pandatube.de) under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [provider](https://gitea.robertops.com) and select the DeepSeek-R1 model.
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The design detail page supplies important details about the design's capabilities, rates structure, and implementation standards. You can find detailed use instructions, including sample API calls and code snippets for combination. The design supports numerous text generation jobs, consisting of content production, code generation, and question answering, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1091356) utilizing its support finding out optimization and CoT thinking abilities. +The page likewise consists of release alternatives and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, select Deploy.
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You will be prompted to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, enter a variety of instances (in between 1-100). +6. For Instance type, select your circumstances type. For optimal performance with DeepSeek-R1, a [GPU-based instance](https://freeworld.global) type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you may desire to examine these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start using the model.
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When the implementation is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive interface where you can experiment with various triggers and change design specifications like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For instance, content for reasoning.
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This is an outstanding way to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The play ground offers instant feedback, assisting you comprehend how the design responds to different inputs and letting you tweak your prompts for optimum outcomes.
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You can quickly evaluate the model in the play ground through the UI. However, to conjure up the released model programmatically 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 [demonstrates](https://wakeuptaylor.boardhost.com) how to carry out 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 produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up reasoning specifications, 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, integrated algorithms, and prebuilt ML services that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 convenient methods: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both methods to help you select the method that finest fits your needs.
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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, select Studio in the navigation pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model internet browser shows available designs, with details like the service provider name and model capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card shows crucial details, consisting of:
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- Model name +- Provider name +- Task category (for example, Text Generation). +[Bedrock Ready](https://novashop6.com) badge (if applicable), suggesting that this design can be registered with Amazon Bedrock, enabling you to use 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 design details page includes the following details:
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- The design name and provider details. +Deploy button to release 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 guidelines
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Before you deploy the design, it's advised to review the design details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to continue with deployment.
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7. For [Endpoint](https://stagingsk.getitupamerica.com) name, utilize the automatically generated name or develop a custom-made one. +8. For example type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For [Initial](https://gitlab.digineers.nl) instance count, get in the variety of instances (default: 1). +Selecting suitable [circumstances types](http://git.ningdatech.com) and counts is vital for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by . This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the design.
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The release procedure can take a number of minutes to finish.
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When release is total, your endpoint status will alter to InService. At this moment, the model is ready to accept reasoning demands through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is complete, you can invoke the design utilizing a SageMaker runtime customer and incorporate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To begin 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 authorizations and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying 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 additional 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 develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
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Clean up
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To prevent unwanted charges, finish the actions in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you deployed the model using Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments. +2. In the Managed releases area, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the proper 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 model you released will sustain costs if you leave it running. Use the following code to erase 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 explored how you can access and deploy the DeepSeek-R1 model utilizing [Bedrock Marketplace](https://nojoom.net) and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, 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 Inference at AWS. He assists emerging generative [AI](https://followmylive.com) business construct innovative options utilizing AWS services and sped up calculate. Currently, he is focused on establishing techniques for [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:DavisIsaacs) fine-tuning and optimizing the inference performance of large language models. In his leisure time, Vivek takes pleasure in treking, enjoying motion pictures, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://hip-hop.id) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His [location](http://cjma.kr) of focus is AWS [AI](http://bristol.rackons.com) [accelerators](https://git.marcopacs.com) (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://pantalassicoembalagens.com.br) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://gitlab.awcls.com) hub. She is enthusiastic about building options that assist consumers accelerate their [AI](https://gitea.potatox.net) journey and unlock organization value.
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