From 00f4a2bdebb3445b85aae7941a494fa46d460a0b Mon Sep 17 00:00:00 2001 From: lynette0554989 Date: Thu, 27 Feb 2025 16:40:07 +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..0805407 --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://play.uchur.ru)'s [first-generation frontier](https://video.propounded.com) model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion parameters to develop, experiment, and [properly scale](http://git.baige.me) your generative [AI](http://47.97.159.144:3000) concepts on AWS.
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In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock [Marketplace](https://www.joboont.in) 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](http://www5f.biglobe.ne.jp) that uses reinforcement learning to enhance thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential distinguishing function is its reinforcement learning (RL) step, which was used to refine the model's responses beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually boosting both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, indicating it's geared up to break down complicated questions and reason through them in a detailed way. This directed thinking procedure allows the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually captured the industry's attention as a flexible text-generation design that can be integrated into various [workflows](https://kurva.su) such as representatives, rational thinking and information interpretation jobs.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion specifications, enabling efficient reasoning by routing queries to the most relevant professional "clusters." This approach allows the model to concentrate on different problem domains while maintaining overall [performance](http://47.106.228.1133000). DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for [inference](https://119.29.170.147). In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking capabilities 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 refers to a process of training smaller sized, more efficient models to imitate the behavior and thinking patterns of the bigger DeepSeek-R1 model, 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 suggest releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and evaluate designs against essential security requirements. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://git.augustogunsch.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. 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 instance in the AWS Region you are releasing. To ask for a limit boost, create a limit increase request and connect to your account group.
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Because you will be releasing this model with [Amazon Bedrock](https://posthaos.ru) Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For instructions, see Establish authorizations to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, avoid hazardous material, and evaluate models against key safety requirements. You can implement precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and design actions deployed on [Amazon Bedrock](https://maram.marketing) Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing 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 involves the following actions: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the design for inference. After receiving the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the outcome. 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](http://gitlab.suntrayoa.com). The [examples showcased](https://gitlab.zogop.com) in the following areas demonstrate inference 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](https://apyarx.com). To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, select Model catalog 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 APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and choose the DeepSeek-R1 design.
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The design detail page offers essential details about the design's abilities, rates structure, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1074946) and implementation guidelines. You can discover detailed use directions, consisting of sample API calls and code snippets for integration. The model supports different text generation tasks, consisting of content creation, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT thinking capabilities. +The page likewise consists of deployment alternatives and licensing [details](http://park7.wakwak.com) to help you get started with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, select Deploy.
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You will be triggered to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an [endpoint](https://videobox.rpz24.ir) name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, get in a variety of instances (between 1-100). +6. For Instance type, select your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure innovative security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function consents, and encryption settings. For many utilize cases, the default settings will work well. However, for production releases, you might wish to evaluate these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to begin using the design.
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When the release is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive interface where you can try out different triggers and change model specifications like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For example, material for inference.
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This is an outstanding way to check out the model's thinking and text [generation capabilities](https://www.eadvisor.it) before incorporating it into your applications. The playground offers instant feedback, [assisting](https://members.mcafeeinstitute.com) you comprehend how the design reacts to different inputs and letting you tweak your prompts for optimal outcomes.
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You can [rapidly](https://www.weben.online) evaluate the design 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 reasoning using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock [console](https://src.strelnikov.xyz) or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up reasoning specifications, and sends 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 solutions that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use 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 techniques: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you choose the approach that finest matches your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be triggered to produce a domain. +3. On the [SageMaker Studio](https://www.dataalafrica.com) console, select JumpStart in the navigation pane.
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The design internet browser shows available models, with details like the provider name and model abilities.
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4. Look 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 category (for example, Text Generation). +Bedrock Ready badge (if relevant), showing that this model can be signed up with Amazon Bedrock, [permitting](https://git.guaranteedstruggle.host) you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the [model card](https://www.tmip.com.tr) to view the model 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 includes essential details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage standards
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Before you deploy the design, it's suggested to evaluate the model details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, use the immediately produced name or develop a custom one. +8. For Instance type [ΒΈ select](http://103.242.56.3510080) a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the variety of [instances](https://www.jobs-f.com) (default: 1). +Selecting suitable instance types and counts is vital for expense and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and [low latency](https://seekinternship.ng). +10. Review all configurations for accuracy. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. [Choose Deploy](http://lty.co.kr) to deploy the model.
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The implementation procedure can take a number of minutes to complete.
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When deployment is complete, your endpoint status will alter to InService. At this point, the model is prepared to [accept reasoning](http://www.xn--2i4bi0gw9ai2d65w.com) demands through the endpoint. You can keep track of the release progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation is total, you can invoke the model using a [SageMaker runtime](http://112.126.100.1343000) client and [incorporate](https://weeddirectory.com) it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get started with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is offered in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional requests 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 also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2805250) the API, and [execute](https://academy.theunemployedceo.org) it as shown in the following code:
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Tidy up
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To prevent unwanted charges, complete the actions in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you released the model utilizing Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace implementations. +2. In the Managed releases 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 erasing 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 [released](http://damoa8949.com) 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 release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock now to start. 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 Starting 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 helps emerging generative [AI](https://pennswoodsclassifieds.com) [business develop](https://almanyaisbulma.com.tr) innovative services using AWS services and accelerated calculate. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the reasoning efficiency of big language models. In his spare time, Vivek enjoys treking, viewing films, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://jobasjob.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://one2train.net) [accelerators](http://165.22.249.528888) (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://git.xiaoya360.com) 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](https://955x.com) center. She is enthusiastic about building solutions that help customers accelerate their [AI](https://gitea.potatox.net) journey and unlock organization worth.
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