commit 5ef59932b2111b811dd46710daf15928c2a98d76 Author: Virgilio Therry Date: Tue Apr 8 00:56:49 2025 +0000 Update '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..f901653 --- /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 are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://gl.vlabs.knu.ua)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](http://ufidahz.com.cn:9015) [concepts](https://mp3talpykla.com) on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big [language model](https://www.locumsanesthesia.com) (LLM) established by DeepSeek [AI](https://lr-mediconsult.de) that uses reinforcement learning to boost thinking [abilities](http://git.armrus.org) through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential identifying feature is its reinforcement learning (RL) action, which was utilized to improve the design's responses beyond the basic [pre-training](https://cn.wejob.info) and tweak process. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately boosting both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, suggesting it's geared up to break down complicated questions and factor through them in a detailed manner. This guided reasoning procedure enables the design to produce more accurate, transparent, and [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1331161) detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation model that can be incorporated into different workflows such as agents, logical reasoning and information analysis tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion criteria, making it possible for [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:Sunny732248) effective inference by routing inquiries to the most appropriate expert "clusters." This method allows the model to concentrate on different issue domains while maintaining general [effectiveness](http://112.48.22.1963000). DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 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 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 mimic the behavior and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:ChristyPetherick) thinking patterns of the larger DeepSeek-R1 model, utilizing it as an [instructor design](https://recruitment.transportknockout.com).
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful content, and assess models against crucial security requirements. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, [Bedrock Guardrails](http://187.216.152.1519999) supports just the ApplyGuardrail API. You can produce several guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative [AI](http://plus.ngo) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you need access to an ml.p5e [instance](https://energypowerworld.co.uk). To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limitation boost, create a limitation increase request and reach out to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For instructions, see [Establish approvals](https://apkjobs.com) to utilize guardrails for [material filtering](https://social.acadri.org).
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, avoid damaging material, and examine designs against essential safety requirements. You can execute security procedures for the DeepSeek-R1 [model utilizing](https://git.sommerschein.de) the Amazon Bedrock ApplyGuardrail API. This allows you to apply 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 produce the guardrail, see the GitHub repo.
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The basic flow includes the following actions: 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://git.spitkov.hu) check, it's sent out to the model for inference. After receiving the design's output, [wakewiki.de](https://www.wakewiki.de/index.php?title=Benutzer:AlexWoolnough3) another guardrail check is used. If the output passes this last check, it's returned as the last outcome. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas show reasoning using 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 structure 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, pick Model brochure under Foundation designs in the navigation pane. +At the time of composing this post, you can use 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 company and pick the DeepSeek-R1 model.
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The model detail page supplies vital details about the model's capabilities, rates structure, and application guidelines. You can find detailed use directions, consisting of sample API calls and code snippets for integration. The model supports numerous text [generation](http://www.dahengsi.com30002) tasks, consisting of material production, code generation, and concern answering, using its support discovering optimization and CoT thinking abilities. +The page also consists of release alternatives and licensing details to help you begin with DeepSeek-R1 in your [applications](https://planetdump.com). +3. To start using DeepSeek-R1, select Deploy.
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You will be prompted to configure the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, get in a variety of circumstances (between 1-100). +6. For example type, choose your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up sophisticated security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function consents, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) for production deployments, you might want 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 complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can explore various triggers and adjust design parameters like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal outcomes. For instance, material for reasoning.
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This is an [outstanding](https://www.cartoonistnetwork.com) way to explore the model's reasoning and text generation abilities before integrating it into your applications. The play area offers instant feedback, helping you understand how the model responds to numerous inputs and letting you tweak your prompts for [optimum](http://kodkod.kr) results.
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You can quickly test the design in the [playground](https://sossdate.com) through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need 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 shows how to carry out reasoning utilizing a released DeepSeek-R1 model 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. After you have actually created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) sends out a [request](https://src.strelnikov.xyz) to generate text based upon 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 options that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained [designs](https://asixmusik.com) 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 design through SageMaker JumpStart offers two hassle-free methods: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you choose the technique that [finest suits](https://omegat.dmu-medical.de) your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design browser shows available models, with details like the provider name and design capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design card shows essential details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +[Bedrock Ready](http://mangofarm.kr) badge (if applicable), suggesting that this design can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the [model card](https://code.lanakk.com) to see the design details page.
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The design details page consists of the following details:
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- The design name and [company details](https://clujjobs.com). +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
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The About tab includes important details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage guidelines
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Before you deploy the design, it's suggested to examine the model details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, utilize the instantly created name or produce a customized one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For [Initial instance](http://13.209.39.13932421) count, get in the number of circumstances (default: 1). +Selecting appropriate circumstances types and counts is important for cost and performance optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all [configurations](https://gitlab.amatasys.jp) for [accuracy](http://park1.wakwak.com). For this design, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to release the model.
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The deployment process can take a number of minutes to finish.
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When implementation is total, your [endpoint status](http://139.224.253.313000) will alter to InService. At this moment, the design is ready to accept inference demands through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is total, you can invoke the model using 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 get started with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is offered in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run additional requests against the predictor:
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Implement guardrails and run [inference](https://inspirationlift.com) with your [SageMaker JumpStart](http://gungang.kr) predictor
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Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:
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Tidy up
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To prevent unwanted charges, finish the steps in this area to clean up your [resources](https://medifore.co.jp).
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Delete the Amazon Bedrock Marketplace implementation
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If you [deployed](https://www.isinbizden.net) the design using Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases. +2. In the Managed releases section, locate the [endpoint](https://www.weben.online) 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 deleting 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 design you released will sustain expenses if you leave it running. Use the following code to erase 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](https://bestwork.id) out how you can access and release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.
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About the Authors
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[Vivek Gangasani](http://sites-git.zx-tech.net) is a Lead Specialist Solutions [Architect](http://git.huixuebang.com) for Inference at AWS. He helps emerging generative [AI](https://www.yanyikele.com) companies build innovative solutions using AWS services and accelerated calculate. Currently, he is focused on establishing strategies for fine-tuning and enhancing the inference efficiency of large language models. In his leisure time, [Vivek enjoys](https://recruitment.transportknockout.com) treking, seeing motion pictures, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://intermilanfansclub.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://dubai.risqueteam.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on [generative](https://robbarnettmedia.com) [AI](https://8.129.209.127) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.isinbizden.net) center. She is enthusiastic about constructing services that help customers accelerate their [AI](http://60.204.229.151:20080) journey and unlock service value.
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