commit b2d52bce3c05272eff2162317848c1b4f34baf56 Author: nicholrule168 Date: Sat May 31 11:33:23 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..83776f6 --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, we are excited to reveal 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](https://streaming.expedientevirtual.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion specifications to develop, experiment, and properly scale your [generative](https://git.alexavr.ru) [AI](http://www.tuzh.top:3000) ideas on AWS.
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In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the [distilled versions](https://moyatcareers.co.ke) of the models too.
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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://www.yourtalentvisa.com) that uses support discovering to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating function is its reinforcement knowing (RL) action, which was used to refine the design's reactions beyond the standard pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and goals, ultimately enhancing both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, implying it's equipped to break down complex inquiries and reason through them in a detailed way. This [guided reasoning](http://47.92.149.1533000) process allows the model to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has [captured](https://storymaps.nhmc.uoc.gr) the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as agents, sensible reasoning and data analysis jobs.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, making it possible for [efficient reasoning](https://tricityfriends.com) by routing questions to the most appropriate professional "clusters." This technique allows the design to focus on different [issue domains](https://git.guaranteedstruggle.host) while maintaining overall 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 release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking 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 sized, more efficient designs to imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as an [instructor design](https://git.i2edu.net).
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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](http://repo.bpo.technology) this model with guardrails in place. In this blog, we will [utilize Amazon](http://gitea.ucarmesin.de) Bedrock Guardrails to introduce safeguards, prevent harmful content, and evaluate models against essential security criteria. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://gitea.malloc.hackerbots.net) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limit boost, create a limit increase 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 appropriate AWS Identity and [Gain Access](http://1.12.246.183000) To Management (IAM) consents to use Amazon Bedrock Guardrails. For guidelines, see Establish permissions to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, prevent harmful material, and assess designs against essential safety requirements. You can execute safety procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock [ApplyGuardrail](https://21fun.app) API. This permits you to apply guardrails to [examine](http://124.223.222.613000) user inputs and model actions 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 develop the guardrail, see the GitHub repo.
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The general flow includes 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 reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the result. 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 stage. The examples showcased in the following sections show reasoning using 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 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, choose [Model brochure](http://gitpfg.pinfangw.com) under Foundation models in the navigation pane. +At the time of writing this post, you can use the [InvokeModel API](https://localjobpost.com) to invoke the model. It doesn't support Converse APIs and other [Amazon Bedrock](https://posthaos.ru) tooling. +2. Filter for DeepSeek as a company and select the DeepSeek-R1 design.
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The design detail page offers important details about the design's abilities, rates structure, and application standards. You can find detailed usage instructions, [including sample](https://aubameyangclub.com) API calls and [code bits](https://repo.komhumana.org) for combination. The design supports various text generation jobs, consisting of material creation, code generation, and concern answering, using its support finding out optimization and CoT reasoning capabilities. +The page also consists of deployment alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, choose Deploy.
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You will be triggered to configure the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, get in a variety of circumstances (between 1-100). +6. For example type, choose your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure advanced security and infrastructure settings, including virtual personal cloud (VPC) networking, service [function](http://114.115.218.2309005) approvals, and file encryption settings. For most use cases, the default settings will work well. However, 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 start using the design.
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When the release is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play area to access an interactive interface where you can explore different prompts and change model parameters like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum results. For instance, material for inference.
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This is an outstanding way to check out the design's reasoning and text generation abilities before integrating it into your applications. The play area provides instant feedback, assisting you understand how the design responds to different inputs and letting you tweak your triggers for optimum results.
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You can quickly evaluate the design in the playground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail using 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 execute guardrails. The script initializes the bedrock_runtime customer, configures reasoning parameters, and sends out a request to produce 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 services that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs 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 design through SageMaker JumpStart provides two hassle-free methods: [utilizing](https://magnusrecruitment.com.au) the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you select the method that finest fits 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 develop a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model internet browser displays available designs, with details like the provider name and model abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card reveals essential details, including:
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[- Model](https://www.dynamicjobs.eu) name +- Provider name +- Task classification (for [oeclub.org](https://oeclub.org/index.php/User:ChangBraun16811) example, Text Generation). +Bedrock Ready badge (if appropriate), indicating that this model can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to [conjure](https://git.perrocarril.com) up the model
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5. Choose the model card to view the model details page.
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The design details page consists of the following details:
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- The design name and service provider details. +Deploy button to deploy the model. +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 description. +- License details. +- Technical requirements. +standards
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Before you release the model, it's recommended to evaluate the [model details](https://www.vadio.com) 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 instantly created name or produce a custom one. +8. For example type ΒΈ select an [instance type](http://git.baobaot.com) (default: ml.p5e.48 xlarge). +9. For [Initial circumstances](https://jobs.but.co.id) count, go into the variety of instances (default: 1). +Selecting proper instance types and counts is important for cost and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for [surgiteams.com](https://surgiteams.com/index.php/User:NoahSchonell) sustained traffic and [low latency](https://crossdark.net). +10. Review all setups for accuracy. For this model, we highly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the model.
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The deployment process can take numerous minutes to finish.
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When deployment is complete, your endpoint status will alter to InService. At this moment, the design is prepared to accept inference requests through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the [release](https://www.hijob.ca) is total, 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 need to set up the SageMaker Python SDK and make certain you have the necessary AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is offered in the Github here. You can clone the note pad and range from [SageMaker Studio](http://117.50.220.1918418).
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You can run extra 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 [utilize](http://47.107.80.2363000) the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
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Clean up
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To avoid unwanted charges, complete 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 [model utilizing](https://aaalabourhire.com) Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the [navigation](http://47.244.232.783000) pane, [select Marketplace](https://redebrasil.app) releases. +2. In the Managed deployments section, find the [endpoint](http://8.140.205.1543000) you want to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate implementation: 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 released will sustain costs 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](http://www.my.vw.ru) out how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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](https://video.propounded.com) at AWS. He assists emerging generative [AI](https://gitlab.surrey.ac.uk) business develop ingenious [options utilizing](https://ravadasolutions.com) AWS services and accelerated compute. Currently, he is concentrated on developing strategies for [fine-tuning](https://career.webhelp.pk) and optimizing the reasoning efficiency of big [language](https://avajustinmedianetwork.com) models. In his downtime, Vivek delights in treking, seeing films, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://git.home.lubui.com:8443) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://www.vidconnect.cyou) 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](http://repo.z1.mastarjeta.net) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://carepositive.com) hub. She is enthusiastic about developing solutions that help consumers [accelerate](https://projectblueberryserver.com) their [AI](http://jobs.freightbrokerbootcamp.com) journey and unlock company value.
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