From ca0a40523c65a082b3c506380587482c4d3a5d60 Mon Sep 17 00:00:00 2001 From: Adan Paramore Date: Fri, 28 Feb 2025 10:27:40 +0330 Subject: [PATCH] Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-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..4b25cc8 --- /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](http://222.121.60.403000) to announce that DeepSeek 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://2flab.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](https://foris.gr) concepts 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 actions to deploy the distilled versions of the models as well.
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[Overview](https://friendify.sbs) of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](http://hoenking.cn:3000) that utilizes support discovering to boost reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential differentiating function is its reinforcement learning (RL) step, which was utilized to refine the design's responses beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, ultimately boosting both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, suggesting it's geared up to break down intricate questions and factor through them in a detailed way. This guided thinking procedure enables the design to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured actions while [concentrating](http://39.101.167.1953003) on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation design that can be integrated into various workflows such as representatives, rational reasoning and data interpretation jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion criteria, enabling efficient inference by routing questions to the most pertinent expert "clusters." This method enables the design to specialize in various issue domains while maintaining general effectiveness. 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 design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the [reasoning abilities](https://desarrollo.skysoftservicios.com) of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient models to simulate the [behavior](https://divsourcestaffing.com) and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor design.
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You can release DeepSeek-R1 model either through [SageMaker JumpStart](https://wfsrecruitment.com) or Bedrock . Because DeepSeek-R1 is an emerging model, we recommend deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and assess designs against essential safety requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](https://wiki.idealirc.org) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas [console](https://smaphofilm.com) and under AWS Services, pick Amazon SageMaker, and validate you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 [xlarge circumstances](http://116.205.229.1963000) in the AWS Region you are releasing. To ask for a limitation boost, develop a limit increase demand and connect to your account group.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock [Guardrails](https://git.foxarmy.org). For instructions, see [Establish permissions](https://git.jamarketingllc.com) to use 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 harmful material, and examine models against key security criteria. You can execute security steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the [GitHub repo](https://www.contraband.ch).
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The general flow involves 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 model 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 last result. 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 happened at the input or output stage. 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 gives you access to over 100 popular, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:TawnyaWhitley87) emerging, and specialized foundation designs (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 pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 design.
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The design detail page provides essential details about the design's capabilities, pricing structure, and application standards. You can find detailed usage guidelines, consisting of [sample API](https://dubaijobzone.com) calls and code snippets for combination. The [model supports](http://optx.dscloud.me32779) different text generation jobs, including content creation, code generation, and concern answering, utilizing its support finding out optimization and CoT reasoning capabilities. +The page also includes release options 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](http://krzsyjtj.zlongame.co.kr9004) to configure the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For [Variety](https://www.valeriarp.com.tr) of circumstances, enter a variety of circumstances (between 1-100). +6. For example type, pick your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up innovative security and [facilities](https://suprabullion.com) settings, consisting of virtual private cloud (VPC) networking, service role permissions, and encryption settings. For a lot of use cases, the default settings will work well. However, for production deployments, you may wish to examine 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 test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play area to access an interactive user interface where you can experiment with different triggers and change model specifications like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal outcomes. For instance, material for reasoning.
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This is an exceptional way to check out the model's thinking and text generation abilities before incorporating it into your applications. The play area offers instant feedback, helping you [understand](http://jibedotcompany.com) how the design reacts to different inputs and letting you fine-tune your triggers for optimal outcomes.
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You can quickly check the design in the play ground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference using guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock [console](https://source.ecoversities.org) or the API. For the example code to create the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends a [request](https://www.contraband.ch) to generate text based on a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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[SageMaker JumpStart](https://higgledy-piggledy.xyz) is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML solutions that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into [production](https://ourehelp.com) using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 practical approaches: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you pick the method that best matches your needs.
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Deploy DeepSeek-R1 through [SageMaker JumpStart](http://114.55.171.2313000) UI
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Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the [navigation pane](https://wakeuptaylor.boardhost.com). +2. First-time users will be triggered to produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design browser shows available models, with details like the company name and model abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card shows crucial details, consisting of:
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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 use Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to see the model details page.
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The design [details](https://gogs.dzyhc.com) page consists of the following details:
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- The model name and company details. +Deploy button to release the model. +About and Notebooks tabs with [detailed](http://www.zjzhcn.com) details
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The About tab includes crucial details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage standards
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Before you deploy the model, it's suggested to examine the design details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, [utilize](https://cristianoronaldoclub.com) the immediately generated name or develop a custom one. +8. For example type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the number of circumstances (default: 1). +Selecting appropriate instance types and counts is vital for cost and [performance optimization](http://47.107.153.1118081). Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. For this model, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the model.
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The implementation procedure can take several minutes to complete.
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When release is total, your endpoint status will change to InService. At this point, the design is ready to accept inference demands through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is total, you can conjure up the model utilizing a SageMaker runtime client and incorporate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the necessary AWS authorizations and [yewiki.org](https://www.yewiki.org/User:LucianaChau79) environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the design is supplied 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 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 produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
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Tidy up
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To avoid undesirable charges, finish the steps in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the [model utilizing](https://nodlik.com) Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations. +2. In the Managed releases area, locate the [endpoint](http://soho.ooi.kr) 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 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 design you deployed will sustain expenses if you leave it running. Use the following code to erase the [endpoint](http://gnu5.hisystem.com.ar) 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 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 get started. For more details, refer to Use [Amazon Bedrock](https://www.50seconds.com) tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started with Amazon [SageMaker JumpStart](https://21fun.app).
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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://sharefriends.co.kr) business construct innovative options using AWS services and sped up compute. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the reasoning efficiency of big language models. In his spare time, Vivek enjoys hiking, seeing motion pictures, and [attempting](https://axionrecruiting.com) various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://git.j4nis05.ch) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://cristianoronaldoclub.com) accelerators (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://becalm.life) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://repo.correlibre.org) hub. She is passionate about developing solutions that assist consumers accelerate their [AI](https://allcollars.com) journey and unlock service worth.
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