commit b6831ad230a11dedacdc5d4ef40f4f093d143c1d Author: matthewhamblet Date: Sat May 31 17:29:19 2025 +0800 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..b19468e --- /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 announce that DeepSeek R1 [distilled Llama](http://code.bitahub.com) and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://www.ignitionadvertising.com)'s first-generation frontier design, DeepSeek-R1, along with the [distilled variations](https://git.selfmade.ninja) ranging from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](https://zurimeet.com) concepts on AWS.
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In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps 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 big language design (LLM) established by DeepSeek [AI](http://123.60.103.97:3000) that utilizes reinforcement discovering to enhance reasoning capabilities through a multi-stage training [process](https://www.vadio.com) from a DeepSeek-V3-Base foundation. A crucial distinguishing function is its support knowing (RL) action, which was utilized to fine-tune the design's actions beyond the standard pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually improving both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, implying it's equipped to break down complex inquiries and factor through them in a detailed manner. This guided thinking process [permits](http://www.zeil.kr) the model to produce more accurate, transparent, and detailed responses. This design integrates RL-based [fine-tuning](https://www.trabahopilipinas.com) with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation design that can be incorporated into various workflows such as agents, logical reasoning and information interpretation jobs.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion parameters, [allowing efficient](https://crossdark.net) reasoning by routing queries to the most relevant expert "clusters." This method permits the model to focus on various problem domains while maintaining general 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 deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning capabilities 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 describes a procedure of training smaller, more effective models to imitate the habits and [reasoning patterns](https://psuconnect.in) of the larger DeepSeek-R1 design, using it as a teacher design.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this design with guardrails in location. In this blog, [wavedream.wiki](https://wavedream.wiki/index.php/User:CedricElston) we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and examine designs against essential safety requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative [AI](https://hlatube.com) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you need 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 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 deploying. To ask for a [limitation](http://chkkv.cn3000) increase, [develop](https://sunrise.hireyo.com) a limit boost demand and connect to your account team.
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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) approvals to use Amazon Bedrock Guardrails. For instructions, see Establish approvals to use 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 hazardous content, and examine designs against essential security requirements. You can execute security steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to examine user inputs and design actions deployed 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 circulation includes the following actions: First, the system gets an input for the design. This input is then [processed](https://www.assistantcareer.com) through the [ApplyGuardrail API](http://video.firstkick.live). If the input passes the guardrail check, it's sent to the design for inference. After receiving the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections demonstrate inference using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon [Bedrock Marketplace](http://g-friend.co.kr) 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 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 conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 design.
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The design detail page provides vital details about the design's capabilities, rates structure, and application guidelines. You can discover detailed use directions, including sample API calls and code snippets for combination. The model supports numerous text generation tasks, including content creation, code generation, and question answering, utilizing its reinforcement learning optimization and CoT reasoning capabilities. +The page also includes release options and licensing details to help you get begun with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, select Deploy.
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You will be prompted to set up the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 [alphanumeric](http://120.46.37.2433000) characters). +5. For Variety of instances, go into a variety of circumstances (in between 1-100). +6. For example type, select your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can configure advanced security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you might desire to review these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the deployment 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 explore various triggers and change design parameters like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum outcomes. For example, content for inference.
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This is an outstanding method to check out the design's thinking and text generation abilities before integrating it into your [applications](https://lifestagescs.com). The play area supplies immediate feedback, assisting you understand how the model reacts to various inputs and letting you tweak your prompts for optimal results.
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You can rapidly evaluate the design in the play ground through the UI. However, to conjure up the deployed model programmatically with any [Amazon Bedrock](https://gitlab.minet.net) APIs, you require to get the endpoint ARN.
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Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference using a [released](https://www.noagagu.kr) DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up inference criteria, and sends out 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 that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and release 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](http://43.138.236.39000) JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both approaches to help you select the method that best suits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 using 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 produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The [model internet](https://hitechjobs.me) browser displays available designs, with details like the service provider name and model capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card shows key details, including:
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- Model name +- Provider name +- Task [category](https://aravis.dev) (for example, Text Generation). +Bedrock Ready badge (if applicable), showing that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon [Bedrock](https://gitea.umrbotech.com) APIs to invoke the design
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5. Choose the design card to see the design details page.
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The model details page includes the following details:
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- The design name and [company details](https://www.egomiliinteriors.com.ng). +Deploy button to deploy the model. +About and Notebooks tabs with [detailed](https://www.indianpharmajobs.in) details
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The About tab consists of crucial details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage standards
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Before you release the model, it's advised to review the design details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, utilize the immediately generated name or [develop](https://superblock.kr) a custom-made one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of circumstances (default: 1). +Selecting proper instance types and counts is crucial for expense and performance optimization. [Monitor](https://gitea.umrbotech.com) your [release](http://8.137.89.263000) to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the model.
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The deployment procedure can take a number of minutes to finish.
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When implementation is total, your endpoint status will change to InService. At this moment, the model is ready to accept reasoning requests through the endpoint. You can monitor the implementation development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is complete, you can invoke the model using a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the needed AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the design is offered in the Github here. You can clone the note pad and run from [SageMaker Studio](https://code.in-planet.net).
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You can run extra requests against the predictor:
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Implement guardrails and run inference with your [SageMaker JumpStart](https://gamingjobs360.com) predictor
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Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and [implement](https://geniusactionblueprint.com) it as displayed in the following code:
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Clean up
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To prevent undesirable charges, complete the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you released the model using 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 implementations area, locate the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1337555) select Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate 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 model you deployed will [sustain costs](https://bakery.muf-fin.tech) 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 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 begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, 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 helps emerging generative [AI](http://84.247.150.84:3000) companies construct ingenious options utilizing AWS services and sped up compute. Currently, he is [concentrated](https://www.letsauth.net9999) on developing strategies for fine-tuning and optimizing the inference performance of large language models. In his downtime, Vivek delights in treking, enjoying films, and trying various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://paknoukri.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His [location](https://www.netrecruit.al) of focus is AWS [AI](https://www.honkaistarrail.wiki) [accelerators](http://47.244.232.783000) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](http://chotaikhoan.me) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:SusanneDodson) tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.synz.io) center. She is enthusiastic about developing services that help customers accelerate their [AI](https://truejob.co) journey and unlock business worth.
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