From b9efb822f8d2b78fdb3f7d7e0896a8c9d44f3e2a Mon Sep 17 00:00:00 2001 From: derickoshanass Date: Thu, 6 Mar 2025 04:53:59 +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..da452f7 --- /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 reveal that DeepSeek R1 [distilled Llama](http://47.103.108.263000) and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://47.112.200.206:3000)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion parameters to develop, experiment, and [responsibly scale](http://szelidmotorosok.hu) your generative [AI](https://propveda.com) ideas on AWS.
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In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled variations of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](https://innovator24.com) that utilizes support discovering to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key distinguishing feature is its reinforcement knowing (RL) step, 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 efficiently to user feedback and goals, eventually boosting both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, suggesting it's equipped to break down complicated inquiries and factor through them in a detailed manner. This assisted reasoning procedure permits the model to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually captured the market's attention as a versatile text-generation design that can be integrated into numerous workflows such as agents, logical thinking and data interpretation jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion specifications, allowing efficient reasoning by routing inquiries to the most [pertinent expert](https://www.opad.biz) "clusters." This the model to focus on various problem domains while maintaining general efficiency. 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 circumstances to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the [thinking abilities](https://barokafunerals.co.za) of the main R1 model to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective designs to simulate the behavior and thinking patterns of the larger DeepSeek-R1 model, utilizing 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 deploying this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and assess designs against crucial safety [criteria](https://members.advisorist.com). At the time of [composing](https://sondezar.com) this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative [AI](https://bnsgh.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. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify 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 deploying. To ask for a limitation increase, produce a limit boost request and reach out to your account group.
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Because you will be [releasing](https://gallery.wideworldvideo.com) this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Set up approvals to utilize guardrails for content [filtering](http://globalchristianjobs.com).
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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 key safety criteria. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon [Bedrock ApplyGuardrail](http://www.xn--he5bi2aboq18a.com) API. This allows you to apply guardrails to evaluate user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a [guardrail utilizing](http://git.maxdoc.top) the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the [GitHub repo](http://acs-21.com).
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The basic circulation involves the following steps: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, [surgiteams.com](https://surgiteams.com/index.php/User:Jean26M82681) it's sent out to the design for reasoning. After getting the design's output, another guardrail check is [applied](http://git.maxdoc.top). If the [output passes](http://202.164.44.2463000) 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 indicating the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following [sections demonstrate](http://116.63.157.38418) reasoning [utilizing](https://git.becks-web.de) 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 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 models in the navigation pane. +At the time of composing this post, you can utilize 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 model detail page supplies important details about the model's capabilities, prices structure, and execution guidelines. You can find detailed use directions, consisting of sample API calls and code bits for combination. The [model supports](https://www.yewiki.org) various text generation tasks, including material creation, code generation, and question answering, utilizing its reinforcement finding out [optimization](https://www.talentsure.co.uk) and CoT thinking capabilities. +The page likewise includes release alternatives and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To begin [utilizing](https://app.zamow-kontener.pl) DeepSeek-R1, choose Deploy.
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You will be prompted to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, get in a variety of instances (in between 1-100). +6. For example type, select your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure innovative security and facilities settings, including virtual private cloud (VPC) networking, service role authorizations, and file 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 and compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the deployment is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive user interface where you can explore different triggers and change model parameters like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For instance, material for inference.
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This is an excellent method to check out the design's thinking and [text generation](http://1.12.255.88) capabilities before integrating it into your applications. The play ground provides instant feedback, helping you comprehend how the design reacts to different inputs and letting you fine-tune your prompts for optimal outcomes.
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You can rapidly test the design in the play area through the UI. However, to conjure up the [released design](https://embargo.energy) programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning using guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and [ApplyGuardrail API](https://www.worlddiary.co). 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. After you have actually produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends a demand to [produce text](https://www.mpowerplacement.com) 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 (ML) hub with FMs, integrated algorithms, and prebuilt ML services that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers two [convenient](https://git.panggame.com) approaches: using the instinctive SageMaker [JumpStart](http://93.104.210.1003000) UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to help you pick the approach that best fits your requirements.
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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, pick Studio in the navigation pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design internet browser displays available models, with details like the supplier name and design capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card reveals essential details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if suitable), showing that this model can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the design
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5. Choose the model card to view the model details page.
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The design details page consists of the following details:
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- The model name and supplier details. +Deploy button to deploy 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 release the model, it's recommended to examine the design details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, utilize the automatically produced name or create a customized one. +8. For Instance type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the variety of circumstances (default: 1). +Selecting proper instance types and counts is important for expense and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we highly advise adhering to SageMaker JumpStart default [settings](https://demo.wowonderstudio.com) and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the design.
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The implementation process can take numerous minutes to complete.
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When release is total, your endpoint status will alter to InService. At this point, 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 show pertinent metrics and status details. When the release is complete, you can conjure up the model using a SageMaker runtime client and integrate it with your [applications](http://47.112.200.2063000).
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the needed AWS approvals and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for deploying the model is provided in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run extra 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 utilizing the Amazon [Bedrock console](https://223.130.175.1476501) or the API, and implement it as displayed in the following code:
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Tidy up
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To prevent undesirable charges, complete 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 released the model using Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under [Foundation](https://bnsgh.com) designs in the navigation pane, pick Marketplace deployments. +2. In the Managed releases section, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the right 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 delete 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](https://www.joboont.in) how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, [Amazon SageMaker](https://www.telix.pl) JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun with [Amazon SageMaker](https://job.da-terascibers.id) JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions [Architect](http://www.zjzhcn.com) for Inference at AWS. He assists emerging generative [AI](https://pipewiki.org) business develop innovative solutions using [AWS services](https://git.pandaminer.com) and sped up compute. Currently, he is concentrated on developing techniques for fine-tuning and optimizing the reasoning efficiency of large language models. In his downtime, Vivek delights in hiking, viewing motion pictures, and trying various foods.
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[Niithiyn Vijeaswaran](https://edenhazardclub.com) is a Generative [AI](http://42.192.80.21) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://jobidream.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a [Professional](http://101.42.41.2543000) Solutions Architect dealing with generative [AI](https://www.jobzalerts.com) 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](https://sondezar.com) [AI](http://38.12.46.84:3333) hub. She is enthusiastic about developing services that help clients accelerate their [AI](https://git.healthathome.com.np) journey and unlock service worth.
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