From 841ff738cec1da64c2a06c4093d2282fb24e629c Mon Sep 17 00:00:00 2001 From: princescarfe87 Date: Wed, 5 Mar 2025 21:35:14 +0700 Subject: [PATCH] Update '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..baaa494 --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://203.171.20.94:3000)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](http://1.117.194.115:10080) concepts on AWS.
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In this post, we demonstrate how to get begun with DeepSeek-R1 on [Amazon Bedrock](https://socipops.com) Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](https://app.joy-match.com) that utilizes reinforcement learning to enhance reasoning [capabilities](http://37.187.2.253000) through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key distinguishing feature is its support learning (RL) action, which was used to refine the model's actions beyond the standard pre-training and tweak procedure. By [integrating](https://sossdate.com) RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually boosting both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, implying it's geared up to break down complicated questions and reason through them in a detailed manner. This directed reasoning procedure 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 actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually [captured](https://git.alien.pm) the market's attention as a flexible text-generation model that can be integrated into different workflows such as representatives, sensible thinking and information 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 enables activation of 37 billion criteria, enabling efficient inference by routing queries to the most relevant specialist "clusters." This method enables the model to focus on various problem domains while maintaining total effectiveness. DeepSeek-R1 requires at least 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 features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model 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 sized, more efficient models to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 model, 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 design, we suggest releasing this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and assess designs against crucial safety requirements. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails [supports](http://111.35.141.53000) just the ApplyGuardrail API. You can create numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative [AI](https://git.uucloud.top) [applications](http://144.123.43.1382023).
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas [console](http://154.8.183.929080) and under AWS Services, [choose Amazon](https://git.gqnotes.com) SageMaker, and verify 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, produce a limit boost demand and connect to your account team.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For directions, see Set up authorizations to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, prevent harmful content, and examine designs against essential security [criteria](https://social-lancer.com). You can implement safety measures for the DeepSeek-R1 design utilizing the Amazon [Bedrock ApplyGuardrail](https://www.sintramovextrema.com.br) API. This enables you to use guardrails to examine user inputs and model reactions released on [Amazon Bedrock](http://wiki.lexserve.co.ke) Marketplace and SageMaker JumpStart. You can create 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 circulation includes 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, it's sent out to the design for reasoning. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a [message](https://empleosmarketplace.com) is returned suggesting the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections demonstrate reasoning [utilizing](http://1.14.71.1033000) this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and [specialized foundation](https://blog.giveup.vip) models (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, choose [Model brochure](https://soehoe.id) under Foundation designs in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to [conjure](http://www.tomtomtextiles.com) up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [supplier](https://wiki.asexuality.org) and select the DeepSeek-R1 model.
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The model detail page provides vital details about the design's capabilities, rates structure, and application standards. You can find detailed use directions, including sample API calls and code bits for integration. The design supports numerous text generation tasks, consisting of material production, code generation, and concern answering, using its reinforcement finding out optimization and CoT thinking [capabilities](https://geniusactionblueprint.com). +The page also consists of implementation options and licensing details to help you begin with DeepSeek-R1 in your [applications](http://120.79.75.2023000). +3. To begin using DeepSeek-R1, pick Deploy.
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You will be triggered to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, go into a number of instances (in between 1-100). +6. For example type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can configure advanced security and facilities settings, including virtual personal cloud (VPC) networking, service function permissions, and encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:LiliaBoston3284) you may wish to review these settings to align with your company's security and [compliance requirements](http://124.220.187.1423000). +7. Choose Deploy to start utilizing the design.
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When the implementation is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive user interface where you can try out different triggers and change design parameters like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For example, material for reasoning.
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This is an exceptional way to explore the design's thinking and text generation abilities before incorporating it into your [applications](https://gogs.lnart.com). The play ground provides instant feedback, assisting you comprehend how the design reacts to various inputs and letting you tweak your prompts for optimum results.
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You can rapidly test the design in the [playground](https://stepstage.fr) through the UI. However, to conjure up the deployed model 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 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 create the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up reasoning specifications, and [larsaluarna.se](http://www.larsaluarna.se/index.php/User:JaymeMeredith) sends out a [request](http://www.kotlinx.com3000) to generate 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 that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses two convenient approaches: utilizing the intuitive SageMaker UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you choose the technique that finest matches 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, 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 model internet browser shows available models, with details like the supplier name and model abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card shows key details, including:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if suitable), showing that this model can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the design 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 service provider details. +Deploy button to deploy the design. +About and [pipewiki.org](https://pipewiki.org/wiki/index.php/User:ArmandoVigna999) 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 specifications. +[- Usage](http://www.lucaiori.it) standards
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Before you deploy the design, it's advised to evaluate the model details and license terms to verify compatibility with your use case.
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6. Choose Deploy to continue with [release](http://190.117.85.588095).
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7. For Endpoint name, use the automatically produced name or produce a custom one. +8. For example type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the variety of circumstances (default: 1). +Selecting proper circumstances types and counts is crucial for expense and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this design, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to release the model.
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The deployment process can take numerous minutes to complete.
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When implementation is total, your endpoint status will alter to InService. At this moment, the design is all set to accept reasoning demands through the endpoint. You can keep an eye on the release development on the [SageMaker console](https://usa.life) Endpoints page, which will display relevant metrics and status details. When the release is complete, you can invoke the model utilizing 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 start 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](https://git.olivierboeren.nl) example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is supplied in the Github here. You can clone the note pad 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 reasoning with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a [guardrail utilizing](http://45.45.238.983000) the Amazon Bedrock console or the API, and implement it as displayed in the following code:
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Tidy up
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To avoid undesirable charges, finish the [actions](http://www.dahengsi.com30002) in this section to tidy up your resources.
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Delete the Amazon Bedrock [Marketplace](https://surmodels.com) release
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If you deployed 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 deployments area, find the [endpoint](https://proputube.com) you desire to erase. +3. Select the endpoint, and [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1345292) on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the proper deployment: 1. Endpoint name. +2. Model name. +3. [Endpoint](https://www.suyun.store) status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart design you deployed will sustain costs if you leave it [running](https://autogenie.co.uk). Use the following code to delete the [endpoint](http://63.32.145.226) if you want to stop [sustaining charges](https://wiki.contextgarden.net). For more details, see Delete Endpoints and Resources.
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Conclusion
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In this post, [wavedream.wiki](https://wavedream.wiki/index.php/User:JoseLabarre6648) we explored how you can access and release 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, refer to Use Amazon Bedrock tooling with [Amazon SageMaker](https://faptflorida.org) JumpStart models, SageMaker JumpStart pretrained designs, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1075260) Amazon SageMaker [JumpStart](http://fujino-mori.com) Foundation Models, Amazon Bedrock Marketplace, and Starting 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://tube.leadstrium.com) business construct ingenious solutions using AWS services and sped up compute. Currently, he is focused on establishing strategies for fine-tuning and enhancing the reasoning efficiency of big language models. In his spare time, Vivek takes pleasure in treking, [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=1010886) seeing films, and [attempting](https://puzzle.thedimeland.com) various foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://203.171.20.94:3000) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://gitea.ymyd.site) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://www.uaehire.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://193.123.80.202:3000) hub. She is enthusiastic about building options that assist clients accelerate their [AI](https://agapeplus.sg) journey and unlock business worth.
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