From 691684a148c9408c0362dd3d113afb41082d8ce0 Mon Sep 17 00:00:00 2001 From: tiaraslover135 Date: Sun, 6 Apr 2025 06:36:12 +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..099cc43 --- /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://abalone-emploi.ch) Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://www.genbecle.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative [AI](https://dinle.online) concepts on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and [SageMaker JumpStart](http://120.55.59.896023). You can follow similar actions to release the distilled versions of the designs too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://gitlab.zogop.com) that uses reinforcement learning to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential distinguishing function is its support knowing (RL) action, which was used to improve the model's reactions beyond the basic pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, eventually enhancing both significance and clearness. In addition, DeepSeek-R1 utilizes 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 assisted thinking process enables the model to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation design that can be integrated into numerous workflows such as representatives, sensible thinking and information interpretation tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion [criteria](https://jobs.but.co.id) in size. The MoE architecture permits activation of 37 billion parameters, enabling efficient reasoning by routing queries to the most relevant expert "clusters." This method enables the design to [concentrate](https://sujansadhu.com) on various problem domains while maintaining general performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance 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 thinking abilities 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 designs to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher design.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an [emerging](https://iraqitube.com) model, we suggest deploying this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and assess designs against [essential security](https://git.tool.dwoodauto.com) criteria. 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 several guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, improving user experiences and [standardizing safety](https://www.mapsisa.org) controls throughout your generative [AI](https://haloentertainmentnetwork.com) applications.
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Prerequisites
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To release the DeepSeek-R1 design, 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 instance in the AWS Region you are deploying. To request a limit increase, produce a limit increase request and reach out to your account team.
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Because you will be [releasing](http://president-park.co.kr) this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For instructions, see Set up [consents](https://git.sofit-technologies.com) to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, prevent hazardous content, and examine designs against essential security criteria. You can implement precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use [guardrails](http://gitlab.gavelinfo.com) to assess user inputs and model actions deployed on Amazon Bedrock [Marketplace](http://101.43.151.1913000) and SageMaker JumpStart. 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.
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The general flow includes the following steps: First, the system [receives](https://dev.yayprint.com) an input for the design. This input is then [processed](https://www.aspira24.com) through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for reasoning. After receiving the model's output, another guardrail check is used. If the output passes this last check, it's returned as the result. 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 phase. The examples showcased in the following sections show 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 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 pane. +At the time of composing 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 service provider and choose the DeepSeek-R1 model.
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The model detail page supplies vital details about the design's abilities, prices structure, and implementation standards. You can find detailed usage guidelines, including sample API calls and code snippets for integration. The design supports different text generation tasks, consisting of content production, code generation, and concern answering, utilizing its reinforcement discovering optimization and CoT reasoning abilities. +The page likewise includes release choices and licensing details to help you begin 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 release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, get in a number of circumstances (in between 1-100). +6. For example type, choose your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure sophisticated security and infrastructure settings, including virtual private cloud (VPC) networking, service role authorizations, and encryption settings. For most utilize cases, the default settings will work well. However, for production releases, you might desire to review 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 release is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can try out 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 example, material for reasoning.
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This is an excellent way to explore the model's thinking and text generation capabilities before integrating it into your applications. The playground supplies immediate feedback, helping you comprehend how the design reacts to different inputs and letting you tweak your [prompts](https://activitypub.software) for optimal results.
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You can rapidly check the model in the [playground](https://tiwarempireprivatelimited.com) through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need 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 shows how to carry out reasoning using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have actually produced the guardrail, [utilize](https://gitter.top) the following code to execute guardrails. The [script initializes](http://duberfly.com) the bedrock_[runtime](http://194.67.86.1603100) customer, sets up specifications, and sends a demand to produce 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](http://47.103.91.16050903) (ML) hub 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 deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides two practical methods: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you select the technique 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 using 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 develop a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model browser shows available designs, with details like the supplier name and model abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card shows crucial details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if relevant), indicating that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to view the model details page.
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The model details page consists of the following details:
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- The design name and company details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
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The About tab includes crucial details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage standards
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Before you deploy the design, it's advised to examine the model details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the [automatically generated](http://101.43.151.1913000) name or develop a customized one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the number of instances (default: 1). +Selecting appropriate circumstances types and counts is essential for expense and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for precision. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the design.
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The implementation process can take a number of minutes to complete.
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When release is complete, your endpoint status will change to InService. At this moment, the design is all set to accept reasoning requests through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is complete, you can invoke the design using a [SageMaker runtime](http://zhangsheng1993.tpddns.cn3000) client and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) integrate 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 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the needed AWS permissions 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](http://103.197.204.1633025) 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](http://grainfather.asia) and run inference with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and implement it as [revealed](http://114.55.54.523000) in the following code:
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Clean up
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To prevent undesirable charges, finish the steps in this section to clean up your [resources](https://gitea.egyweb.se).
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Delete the Amazon Bedrock [Marketplace](http://bristol.rackons.com) release
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If you released the design utilizing Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under [Foundation](https://nationalcarerecruitment.com.au) models in the navigation pane, pick Marketplace implementations. +2. In the Managed deployments section, locate 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](https://sing.ibible.hk) 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 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 explored how you can access and release the DeepSeek-R1 design utilizing [Bedrock Marketplace](https://dinle.online) and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. 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 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://naijascreen.com) business develop ingenious options using AWS services and sped up compute. Currently, he is focused on developing strategies for fine-tuning and enhancing the reasoning efficiency of large [language designs](http://www.gz-jj.com). In his spare time, Vivek enjoys hiking, enjoying motion pictures, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://loveyou.az) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://teengigs.fun) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](http://music.afrixis.com) with the Third-Party Model [Science](https://www.wakewiki.de) team 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://gitlab.lizhiyuedong.com) center. She is passionate about developing services that assist customers accelerate their [AI](https://novashop6.com) journey and unlock organization worth.
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