From cc63bd1ab68fcf2a8f153d2f73226d484bb43754 Mon Sep 17 00:00:00 2001 From: Lidia Kim Date: Fri, 7 Feb 2025 04:18:28 +0000 Subject: [PATCH] Add '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..e23e8ea --- /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 and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://clinicanevrozov.ru)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion specifications to construct, experiment, and properly scale your [generative](https://www.linkedaut.it) [AI](https://gitea.viamage.com) concepts on AWS.
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In this post, we show how to get going with DeepSeek-R1 on [Amazon Bedrock](https://git.ffho.net) Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the designs too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](https://titikaka.unap.edu.pe) that utilizes support learning to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial identifying function is its reinforcement learning (RL) step, which was utilized to fine-tune the model's reactions beyond the standard pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately improving both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, indicating it's equipped to break down complicated inquiries and factor through them in a detailed manner. This assisted reasoning procedure allows the design to produce more precise, transparent, and detailed responses. This [design combines](http://moyora.today) RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while focusing on interpretability and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:ChristyPetherick) user interaction. With its comprehensive abilities DeepSeek-R1 has actually captured the market's attention as a versatile text-generation design that can be integrated into various workflows such as agents, rational reasoning and information interpretation jobs.
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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, allowing efficient reasoning by routing queries to the most relevant professional "clusters." This technique allows the design to concentrate on different issue domains while maintaining total effectiveness. DeepSeek-R1 requires 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 model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of [GPU memory](http://git.mcanet.com.ar).
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DeepSeek-R1 distilled models bring the thinking abilities 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 refers to a procedure of training smaller sized, more efficient designs to imitate the behavior [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:RefugiaOLeary3) and thinking patterns of the larger DeepSeek-R1 design, utilizing 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 model, we advise releasing this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and examine designs against essential security criteria. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails [tailored](https://gogs.artapp.cn) to various usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative [AI](https://connectworld.app) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you [require access](https://www.gotonaukri.com) to an ml.p5e circumstances. To [inspect](https://surgiteams.com) if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and validate you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge [instance](https://www.cartoonistnetwork.com) in the AWS Region you are deploying. To request a limitation boost, create a limit increase demand 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 proper AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For directions, see Set up approvals to utilize guardrails for content [filtering](https://tiktack.socialkhaleel.com).
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, prevent hazardous material, and assess models against essential safety criteria. You can carry out safety procedures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine 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 produce the guardrail, see the GitHub repo.
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The general [circulation](https://medicalstaffinghub.com) includes the following steps: 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 getting the model's output, another guardrail check is [applied](https://git.serenetia.com). If the [output passes](https://u-hired.com) this last check, it's [returned](http://peterlevi.com) as the result. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the [intervention](http://www.xyais.com) and whether it happened at the input or output stage. The examples showcased in the following sections demonstrate reasoning utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
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1. On the Amazon Bedrock console, pick Model catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [company](https://www.finceptives.com) and select the DeepSeek-R1 design.
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The design detail page provides vital details about the model's capabilities, rates structure, and execution guidelines. You can find detailed use guidelines, including sample API calls and code snippets for combination. The design supports numerous text generation tasks, including content creation, code generation, and question answering, utilizing its reinforcement learning optimization and CoT reasoning abilities. +The page likewise includes implementation options and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be prompted to set up the [release details](https://inspirationlift.com) for DeepSeek-R1. The design ID will be [pre-populated](https://git.mxr612.top). +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, go into a number of instances (in between 1-100). +6. For example type, choose your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role approvals, and encryption settings. For a lot of use cases, the default settings will work well. However, for production deployments, you might desire to examine these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the deployment is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive interface where you can experiment with various prompts and adjust design criteria like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For instance, material for inference.
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This is an outstanding method to explore the model's thinking and text generation capabilities before integrating it into your applications. The [play ground](https://gitea.evo-labs.org) offers instant feedback, helping you understand how the [design responds](https://dev-social.scikey.ai) to different inputs and letting you fine-tune your triggers for ideal results.
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You can rapidly check the model in the playground through the UI. However, [wavedream.wiki](https://wavedream.wiki/index.php/User:ClaireSparling1) to invoke the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning utilizing a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_[runtime](http://47.242.77.180) customer, sets up inference parameters, and sends out a request to produce text based upon a user prompt.
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Deploy DeepSeek-R1 with [SageMaker](https://git.rggn.org) JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML services that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses two practical techniques: utilizing the user-friendly 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 matches your requirements.
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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, pick Studio in the navigation pane. +2. First-time users will be prompted to develop a domain. +3. On the console, select JumpStart in the navigation pane.
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The model browser shows available models, with details like the service provider name and [design abilities](https://younetwork.app).
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model 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 appropriate), showing that this model can be [registered](http://60.204.229.15120080) with Amazon Bedrock, enabling you to [utilize Amazon](https://kryza.network) [Bedrock APIs](http://repo.z1.mastarjeta.net) to invoke the model
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5. Choose the design card to view the design details page.
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The model details page includes the following details:
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- The model name and provider details. +Deploy button to release 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](https://h2bstrategies.com) description. +- License [details](https://www.matesroom.com). +- Technical requirements. +- Usage guidelines
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Before you deploy the design, it's suggested to review the design details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, utilize the immediately [produced](http://124.222.48.2033000) name or develop a customized one. +8. For example type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of circumstances (default: 1). +Selecting appropriate circumstances types and counts is essential for cost and efficiency optimization. Monitor your implementation to adjust these [settings](https://repo.beithing.com) as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to release the design.
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The release process can take numerous minutes to complete.
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When deployment is total, your endpoint status will alter to InService. At this point, the model is prepared to accept reasoning requests through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the [deployment](http://gogs.kexiaoshuang.com) is total, you can invoke the design using 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 get going with DeepSeek-R1 utilizing the [SageMaker Python](https://munidigital.iie.cl) SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that [demonstrates](https://derivsocial.org) how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is supplied in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run extra demands against the predictor:
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Implement guardrails and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:LeilaniCable73) run reasoning with your [SageMaker JumpStart](https://gitlab.tiemao.cloud) 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 using the Amazon Bedrock console or the API, and [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/bagjanine969) execute 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 area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you released the design utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases. +2. In the Managed releases area, find the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the proper 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 design 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 checked out how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](https://git.dsvision.net) or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, [Amazon Bedrock](https://www.securityprofinder.com) Marketplace, and Beginning 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](http://119.167.221.14:60000) business construct ingenious options utilizing AWS services and sped up calculate. Currently, he is concentrated on developing methods for fine-tuning and optimizing the reasoning efficiency of large language models. In his free time, Vivek delights in treking, enjoying movies, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://woodsrunners.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://securityjobs.africa) [accelerators](http://orcz.com) (AWS Neuron). He holds a Bachelor's degree in Computer [technology](https://hesdeadjim.org) and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://bcstaffing.co) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial [intelligence](https://galgbtqhistoryproject.org) and generative [AI](http://121.37.208.192:3000) hub. She is enthusiastic about building options that assist clients [accelerate](http://47.104.65.21419206) their [AI](https://natgeophoto.com) journey and unlock organization value.
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