From 56e1634e683110d8e583ae47ca14578a135992e5 Mon Sep 17 00:00:00 2001 From: epifaniabundy1 Date: Fri, 7 Feb 2025 09:38:51 +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..3d46b11 --- /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 Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://8.142.152.137:4000)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative [AI](http://122.51.46.213) concepts on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the [distilled versions](http://stotep.com) 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](http://skupra-nat.uamt.feec.vutbr.cz:30000) that uses reinforcement learning to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential [differentiating feature](https://wellandfitnessgn.co.kr) is its reinforcement knowing (RL) action, which was utilized to improve the design's responses beyond the standard pre-training and tweak process. By [including](https://www.jccer.com2223) RL, DeepSeek-R1 can adapt more successfully to user feedback and objectives, ultimately enhancing both relevance and [gratisafhalen.be](https://gratisafhalen.be/author/tamikalaf18/) clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, suggesting it's equipped to break down complex questions and reason through them in a detailed manner. This [assisted thinking](https://cristianoronaldoclub.com) [procedure](https://www.ynxbd.cn8888) permits the design to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to create [structured responses](https://huconnect.org) while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation model that can be incorporated into various workflows such as representatives, sensible thinking and data interpretation tasks.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion criteria, allowing efficient inference by routing questions to the most pertinent expert "clusters." This [technique permits](http://bolsatrabajo.cusur.udg.mx) the model to focus on different problem domains while maintaining total 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 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 models bring the reasoning capabilities of the main R1 model to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more efficient models to imitate the behavior and [thinking patterns](https://circassianweb.com) of the larger DeepSeek-R1 design, utilizing it as a teacher design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and evaluate designs against key security criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](https://upmasty.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas [console](http://football.aobtravel.se) and under AWS Services, pick 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 releasing. To ask for a limitation increase, produce a limit increase request and reach out to your account team.
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Because you will be releasing this design 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 guidelines, see Establish consents to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to present safeguards, avoid hazardous content, and evaluate models against essential security requirements. You can implement safety procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply [guardrails](http://121.36.37.7015501) to assess user inputs and model reactions deployed on Amazon Bedrock 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 flow includes 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](https://antoinegriezmannclub.com) check, it's sent out to the model for reasoning. After receiving the design's output, another guardrail check is used. If the output passes this last check, it's returned as the outcome. 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 show reasoning utilizing 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. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, pick 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 doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.
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The design detail page supplies important details about the model's capabilities, rates structure, and application guidelines. You can [discover](https://www.talentsure.co.uk) detailed usage directions, [consisting](https://hiphopmusique.com) of sample API calls and code bits for integration. The design supports various text generation tasks, including content production, code generation, and concern answering, utilizing its reinforcement learning optimization and CoT reasoning abilities. +The page also includes deployment choices and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, [select Deploy](https://git.opskube.com).
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You will be prompted to set up the deployment 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 Variety of instances, get in a number of instances (in between 1-100). +6. For Instance type, choose your circumstances type. For optimum performance with DeepSeek-R1, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:EricGooding) a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can set up innovative security and facilities settings, including virtual private cloud (VPC) networking, service function approvals, and encryption [settings](https://nukestuff.co.uk). For most utilize cases, the [default settings](https://git.blinkpay.vn) will work well. However, for production releases, you may wish to evaluate these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the release is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play area to access an interactive user interface where you can explore different prompts and change model criteria like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal outcomes. For instance, content for reasoning.
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This is an exceptional way to check out the model's thinking and text generation abilities before integrating it into your applications. The playground provides instant feedback, helping you comprehend how the design reacts to numerous inputs and letting you tweak your triggers for optimum outcomes.
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You can rapidly evaluate the model in the playground through the UI. However, to conjure up the released design programmatically with any [Amazon Bedrock](http://110.41.143.1288081) APIs, you need to get the endpoint ARN.
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Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to perform inference using a deployed DeepSeek-R1 design through Amazon Bedrock [utilizing](http://easyoverseasnp.com) the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning parameters, and sends out a demand to [generate text](http://www.jedge.top3000) 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 deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 practical approaches: using the user-friendly SageMaker [JumpStart](https://code.oriolgomez.com) UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you choose the technique that finest suits your requirements.
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Deploy DeepSeek-R1 through [SageMaker JumpStart](https://armconnection.com) UI
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Complete the following actions to deploy 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 console, select JumpStart in the navigation pane.
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The design web browser displays available models, with details like the supplier name and design capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card reveals key details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if suitable), [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:DenishaHolyfield) indicating that this design can be [registered](http://thegrainfather.com) with Amazon Bedrock, enabling 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 [model details](http://webheaydemo.co.uk) page includes the following details:
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- The model name and provider details. +Deploy button to release the model. +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 specs. +- Usage guidelines
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Before you deploy the model, it's suggested to examine the design details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to proceed with implementation.
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7. For Endpoint name, utilize the automatically produced name or produce a custom one. +8. For example type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of instances (default: 1). +Selecting proper instance types and counts is crucial for cost and performance optimization. [Monitor](https://crossdark.net) your [release](http://47.92.159.28) to adjust these [settings](https://cristianoronaldoclub.com) as needed.Under Inference type, Real-time reasoning is selected by default. This is [enhanced](http://git.mutouyun.com3005) for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we strongly advise sticking to SageMaker JumpStart default settings and making certain that [network isolation](http://turtle.pics) remains in location. +11. Choose Deploy to release the model.
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The deployment process can take several minutes to finish.
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When implementation is complete, your endpoint status will alter to InService. At this moment, the design is prepared to accept inference requests through the endpoint. You can monitor the [deployment development](https://git-web.phomecoming.com) on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is total, you can conjure up the design utilizing a SageMaker runtime client and integrate it with your [applications](https://music.michaelmknight.com).
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Deploy DeepSeek-R1 [utilizing](http://101.43.151.1913000) the SageMaker Python SDK
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To begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the needed AWS authorizations and 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 model is provided in the Github here. You can clone the notebook and range from .
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You can run extra demands 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 likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and execute it as revealed in the following code:
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Tidy up
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To avoid undesirable charges, complete the actions in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the design using 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 releases. +2. In the Managed deployments area, 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](http://modiyil.com) the right deployment: 1. Endpoint name. +2. Model name. +3. Endpoint status
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Delete the [SageMaker JumpStart](https://93.177.65.216) 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 want 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 release the DeepSeek-R1 design utilizing 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 at AWS. He assists emerging generative [AI](https://bootlab.bg-optics.ru) companies develop ingenious solutions utilizing AWS services and accelerated compute. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the inference efficiency of big language models. In his downtime, Vivek enjoys treking, enjoying films, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://techport.io) Specialist Solutions [Architect](https://wavedream.wiki) with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://sosmed.almarifah.id) 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 working on generative [AI](https://amorweddfair.com) with the Third-Party Model Science group 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](http://gogs.fundit.cn:3000) hub. She is enthusiastic about constructing options that assist consumers accelerate their [AI](http://39.98.153.250:9080) journey and unlock company worth.
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