commit bb187eee9997718d19c1835fdb3d1a00b7322bd4 Author: ronnykunkle081 Date: Wed Feb 19 12:39:32 2025 +1100 Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart 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..edb1755 --- /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 to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon [Bedrock Marketplace](http://wiki.lexserve.co.ke) and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://coatrunway.partners)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](https://www.lakarjobbisverige.se) concepts on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on [Amazon Bedrock](https://maram.marketing) [Marketplace](https://gitlab.cranecloud.io) and [SageMaker JumpStart](https://cinetaigia.com). You can follow comparable steps to release the distilled versions of the models also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](https://classificados.diariodovale.com.br) that utilizes reinforcement learning to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key differentiating function is its reinforcement knowing (RL) step, which was utilized to fine-tune the design's responses beyond the standard pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually improving both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, meaning it's equipped to break down complicated inquiries and reason through them in a detailed manner. This assisted thinking process enables the model to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to [produce structured](https://soehoe.id) [reactions](http://47.93.56.668080) while focusing on [interpretability](http://shiningon.top) and user interaction. With its [wide-ranging abilities](http://406.gotele.net) DeepSeek-R1 has recorded the industry's attention as a flexible text-generation model that can be integrated into different workflows such as representatives, logical reasoning and data analysis tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion specifications, enabling efficient reasoning by routing inquiries to the most pertinent professional "clusters." This method allows the model to concentrate on different problem domains while maintaining general performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more [effective architectures](https://sajano.com) based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more efficient designs to simulate the habits and thinking patterns of the larger DeepSeek-R1 model, using it as an [instructor design](http://47.112.158.863000).
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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 recommend deploying this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent damaging content, and assess models against crucial safety requirements. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative [AI](http://xn--289an1ad92ak6p.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you require access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and [validate](https://www.valeriarp.com.tr) you're utilizing 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 ask for a limit boost, create a limit boost request and connect to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For guidelines, see Set up approvals to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging content, and assess models against key safety requirements. You can execute safety measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
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The general flow involves the following steps: First, the system receives an input for the model. 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 getting the model's output, another guardrail check is applied. If the [output passes](http://git.sanshuiqing.cn) this final check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following sections demonstrate inference 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 models (FMs) through Amazon Bedrock. To [gain access](http://mohankrishnareddy.com) to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, pick Model brochure under Foundation models in the navigation pane. +At the time of writing 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 provider and select the DeepSeek-R1 design.
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The design detail page supplies important details about the design's capabilities, prices structure, and implementation standards. You can discover detailed usage guidelines, including sample API calls and code snippets for combination. The model supports various text generation jobs, consisting of material production, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT reasoning abilities. +The page also consists of release options and licensing details to help you get started with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick 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, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, enter a number of instances (between 1-100). +6. For Instance type, choose your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up and facilities settings, consisting of virtual personal cloud (VPC) networking, service function permissions, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production deployments, you may desire to evaluate these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the implementation is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive interface where you can experiment with various triggers and change design specifications like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For example, material for reasoning.
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This is an excellent method to explore the model's thinking and text generation abilities before incorporating it into your applications. The play ground offers instant feedback, helping you [understand](http://profilsjob.com) how the design reacts to various inputs and letting you fine-tune 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 APIs, you need to get the [endpoint ARN](http://xiaomu-student.xuetangx.com).
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Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the [GitHub repo](https://nepalijob.com). After you have developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, configures reasoning parameters, and sends a request to create 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) center with FMs, built-in algorithms, and prebuilt ML [options](https://topcareerscaribbean.com) that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](http://git.e365-cloud.com) models to your usage case, with your data, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 hassle-free methods: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you pick the technique that finest fits 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 utilizing SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. [First-time](http://cgi3.bekkoame.ne.jp) users will be prompted to create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design web browser displays available designs, with details like the provider name and design capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card shows key details, including:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +[Bedrock Ready](https://git.ivran.ru) badge (if suitable), [suggesting](https://investsolutions.org.uk) that this model 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 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 provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of essential details, such as:
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[- Model](http://cgi3.bekkoame.ne.jp) description. +- License details. +- Technical requirements. +- Usage guidelines
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Before you deploy the model, it's recommended to examine the model details and license terms to confirm compatibility with your usage case.
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6. [Choose Deploy](https://www.megahiring.com) to continue with release.
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7. For Endpoint name, use the instantly produced name or develop a [customized](https://gitea.oio.cat) one. +8. For [Instance type](https://tweecampus.com) ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of circumstances (default: 1). +Selecting appropriate instance types and counts is essential for cost and efficiency optimization. [Monitor](https://sugarmummyarab.com) your release to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we highly recommend adhering 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 process can take numerous minutes to finish.
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When implementation is total, your endpoint status will change to InService. At this point, the model is prepared to accept inference demands through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is complete, you can conjure up the model utilizing a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To start with DeepSeek-R1 [utilizing](https://cristianoronaldoclub.com) the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS consents and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is offered in the Github here. You can clone the notebook 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and [execute](https://www.laciotatentreprendre.fr) it as displayed in the following code:
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Clean up
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To [prevent undesirable](https://celticfansclub.com) charges, finish 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 utilizing Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases. +2. In the Managed deployments section, find the endpoint you want to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're deleting the correct deployment: 1. [Endpoint](https://www.hyxjzh.cn13000) 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 costs](https://profesional.id) 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, [oeclub.org](https://oeclub.org/index.php/User:CarltonEichmann) we explored how you can access and release the DeepSeek-R1 design utilizing Bedrock Marketplace and [SageMaker](https://pediascape.science) 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 designs, SageMaker JumpStart pretrained designs, Amazon SageMaker [JumpStart Foundation](http://82.156.184.993000) 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 helps emerging generative [AI](https://jobsthe24.com) business develop innovative services utilizing AWS services and accelerated calculate. Currently, he is [concentrated](https://groupeudson.com) on establishing techniques for fine-tuning and optimizing the reasoning efficiency of big language designs. In his downtime, Vivek enjoys treking, seeing films, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://git.micahmoore.io) Specialist Solutions Architect with the [Third-Party Model](https://subamtv.com) Science group at AWS. His location of focus is AWS [AI](https://macphersonwiki.mywikis.wiki) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://www.selfhackathon.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and strategic collaborations for [Amazon SageMaker](http://133.242.131.2263003) JumpStart, SageMaker's artificial intelligence and [generative](https://www.cittamondoagency.it) [AI](https://lovematch.vip) hub. She is enthusiastic about building options that help consumers accelerate their [AI](https://palkwall.com) journey and unlock business worth.
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