From 8b39f57adef76600f36c35fb225b4d3a48b8d67a Mon Sep 17 00:00:00 2001 From: vonniechristop Date: Fri, 30 May 2025 13:07:44 +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..f58a835 --- /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 models are available through Amazon Bedrock and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://gitea.xiaolongkeji.net)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](https://www.indianpharmajobs.in) 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 similar [actions](https://thesecurityexchange.com) to deploy 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://interlinkms.lk) that [utilizes reinforcement](https://git.uzavr.ru) learning to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial differentiating function is its reinforcement knowing (RL) action, which was utilized to fine-tune the design's reactions beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately boosting both importance and [clarity](https://woowsent.com). In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it's geared up to break down complicated questions and reason through them in a detailed manner. This directed thinking procedure enables the design to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT abilities, aiming to create structured actions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has caught the market's attention as a flexible text-generation model that can be integrated into different workflows such as representatives, sensible thinking and data interpretation tasks.
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DeepSeek-R1 uses a [Mixture](https://usa.life) of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion criteria, allowing effective reasoning by routing inquiries to the most appropriate expert "clusters." This method enables the model to focus on different problem domains while maintaining overall effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance 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 reasoning capabilities of the main R1 model to more efficient architectures based on [popular](https://jobskhata.com) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of [training](https://www.tiger-teas.com) smaller sized, more efficient designs to mimic the habits and thinking patterns of the larger DeepSeek-R1 model, using it as an instructor model.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and examine models against essential security requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](https://gitlab.amepos.in) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you're utilizing 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 limit increase, create a limitation boost request and reach out to your account team.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For instructions, see Establish authorizations to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock [Guardrails enables](http://git.swordlost.top) you to introduce safeguards, prevent damaging material, and assess models against crucial safety requirements. You can [implement](http://code.exploring.cn) precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon [Bedrock console](https://103.1.12.176) or the API. For the example code to develop the guardrail, see the GitHub repo.
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The general circulation includes the following actions: First, the system receives an input for the model. This input is then processed through the [ApplyGuardrail API](https://git.citpb.ru). If the input passes the guardrail check, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:JerrellMatthes) it's sent to the design for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the [outcome](http://www.hxgc-tech.com3000). However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it took place at the input or [output stage](https://www.app.telegraphyx.ru). The examples showcased in the following areas demonstrate inference 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, complete the following steps:
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1. On the Amazon Bedrock console, select Model brochure 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 does not [support Converse](https://git.opskube.com) APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 design.
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The design detail page offers [vital details](https://clousound.com) about the design's capabilities, pricing structure, and implementation guidelines. You can discover detailed usage instructions, consisting of sample API calls and code bits for combination. The [model supports](http://106.15.235.242) different text generation tasks, consisting of content creation, code generation, and concern answering, utilizing its reinforcement learning optimization and CoT thinking capabilities. +The page likewise includes release alternatives and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, choose Deploy.
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You will be triggered to set up the implementation details for DeepSeek-R1. The design ID will be [pre-populated](https://www.tcrew.be). +4. For [Endpoint](https://hgarcia.es) name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, enter a number of instances (between 1-100). +6. For Instance type, choose your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, service function consents, and encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you may wish to examine these settings to line up with your [organization's security](https://www.dpfremovalnottingham.com) and compliance requirements. +7. Choose Deploy to start using the model.
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When the implementation is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can explore different triggers and change model specifications like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For example, content for inference.
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This is an outstanding way to explore the model's thinking and text generation abilities before integrating it into your applications. The play ground supplies instant feedback, helping you understand how the design responds to numerous inputs and letting you fine-tune your triggers for [ideal outcomes](https://love63.ru).
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You can quickly test the model in the play ground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning utilizing a released 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 the example code to create the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to carry out guardrails. The [script initializes](https://git.snaile.de) the bedrock_runtime customer, sets up inference specifications, and sends out a request to create 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 services that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers two hassle-free approaches: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you pick the method 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 triggered to create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design web browser shows available designs, with details like the service provider name and design capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card shows crucial details, consisting of:
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- Model name +- Provider name +- Task classification (for example, [yewiki.org](https://www.yewiki.org/User:EarnestineHiatt) Text Generation). +Bedrock Ready badge (if appropriate), [indicating](https://dubaijobzone.com) that this model can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
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5. Choose the [design card](http://124.222.181.1503000) to see the model details page.
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The model details page [consists](https://clickcareerpro.com) of the following details:
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- The model name and supplier details. +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
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The About tab consists of crucial details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage guidelines
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Before you release the design, it's advised to evaluate the model 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 generated name or create a custom-made one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the variety of circumstances (default: 1). +Selecting suitable circumstances types and counts is vital for cost and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for [sustained traffic](https://gitlab.profi.travel) and low latency. +10. Review all configurations for precision. For this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to release the design.
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The deployment process can take a number of minutes to finish.
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When implementation is total, your endpoint status will change to InService. At this moment, the model is ready to accept reasoning requests through the endpoint. You can keep track of the deployment development 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 client and incorporate it with your applications.
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Deploy DeepSeek-R1 using 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 necessary AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra demands against the predictor:
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Implement [guardrails](https://git.xutils.co) and run inference 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 produce a guardrail using the Amazon Bedrock console or the API, and [implement](https://washcareer.com) it as displayed in the following code:
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Clean up
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To avoid undesirable charges, finish the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you [deployed](https://palsyworld.com) the design using 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 deployments. +2. In the Managed implementations section, 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 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 model you released will sustain expenses if you leave it running. Use the following code to delete the endpoint if you desire 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 begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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](http://82.157.11.2243000) at AWS. He helps emerging [generative](https://europlus.us) [AI](https://jobs.careersingulf.com) business build innovative solutions utilizing AWS [services](http://tmdwn.net3000) and sped up calculate. Currently, he is focused on developing methods for fine-tuning and optimizing the [inference efficiency](http://chkkv.cn3000) of big [language](https://careers.express) designs. In his complimentary time, Vivek enjoys hiking, watching motion pictures, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://221.182.8.141:2300) Specialist Solutions Architect with the Third-Party Model [Science](http://gitlab.abovestratus.com) team at AWS. His area of focus is AWS [AI](https://harborhousejeju.kr) 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 dealing with generative [AI](http://47.94.142.235:10230) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://tj.kbsu.ru) hub. She is passionate about developing solutions that help clients accelerate their [AI](https://nse.ai) journey and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) unlock organization value.
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