commit 9b9b506e85a1a84353ff137c3497fadcad95691f Author: patrickwhiteho Date: Fri Apr 4 10:56:28 2025 +0200 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..8a1a7c2 --- /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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://abcdsuppermarket.com)'s [first-generation frontier](https://git.tbaer.de) design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion criteria to construct, experiment, and [properly scale](https://dongawith.com) your [generative](https://skillnaukri.com) [AI](https://www.yiyanmyplus.com) ideas on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the models also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](https://ideezy.com) that utilizes support learning to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential distinguishing feature is its reinforcement knowing (RL) action, which was used to fine-tune the model's reactions beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately improving both importance and [wiki.whenparked.com](https://wiki.whenparked.com/User:LourdesJuergens) clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, suggesting it's equipped to break down complex questions and reason through them in a detailed way. This directed thinking procedure enables the design to produce more accurate, transparent, and detailed answers. This [model combines](https://asw.alma.cl) RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation model that can be incorporated into numerous workflows such as representatives, rational thinking and data analysis jobs.
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DeepSeek-R1 [utilizes](https://jobs.fabumama.com) a Mix of [Experts](https://source.futriix.ru) (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion specifications, making it possible for efficient reasoning by routing questions to the most pertinent expert "clusters." This technique enables the model to concentrate on various issue domains while maintaining total effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning 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, more effective designs to simulate the habits and reasoning 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 design, we suggest releasing this design with guardrails in [location](https://www.boatcareer.com). In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and assess designs against key safety requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and [standardizing safety](https://git.googoltech.com) controls across your generative [AI](http://cjma.kr) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To check 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 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 increase, create a limit boost request and reach out to your account group.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the right [AWS Identity](https://agapeplus.sg) and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see [Establish authorizations](http://gitlab.signalbip.fr) 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 introduce safeguards, avoid harmful content, and [assess models](https://avpro.cc) against essential safety criteria. You can carry out precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or [larsaluarna.se](http://www.larsaluarna.se/index.php/User:KristineFernando) the API. For the example code to develop the guardrail, see the GitHub repo.
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The general circulation includes the following actions: First, the system [receives](https://www.vidconnect.cyou) an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for reasoning. After getting the design's output, another guardrail check is used. 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 showing the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas [demonstrate inference](https://xnxxsex.in) utilizing 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 designs (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, pick Model brochure 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 doesn't support Converse APIs and other [Amazon Bedrock](https://lets.chchat.me) tooling. +2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 model.
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The model detail page provides essential details about the model's abilities, prices structure, and application standards. You can find detailed use instructions, consisting of sample API calls and code bits for combination. The design supports numerous text generation jobs, including material development, code generation, and question answering, utilizing its support finding out optimization and CoT thinking capabilities. +The page likewise consists of deployment options and licensing details to help you get started 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 deployment details for [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:MarieEtter) DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, 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 instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure advanced security and facilities settings, including virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you may want to examine these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the implementation is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive interface where you can explore various prompts and change model criteria 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, content for reasoning.
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This is an exceptional method to check out the design's thinking and text [generation capabilities](http://webheaydemo.co.uk) before [incorporating](http://39.106.177.1608756) it into your applications. The playground offers [instant](https://play.uchur.ru) feedback, assisting you understand how the model reacts to numerous inputs and letting you your [triggers](https://git.bbh.org.in) for ideal results.
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You can rapidly test the model in the play ground through the UI. However, to conjure up the [released model](https://realestate.kctech.com.np) programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run [inference utilizing](http://129.151.171.1223000) guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning utilizing a deployed 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 developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends a request to produce text based on a user timely.
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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 solutions that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs 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 model through SageMaker JumpStart offers 2 practical techniques: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you select the method 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 deploy 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 design browser shows available models, with details like the company name and design abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card reveals essential details, including:
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- Model name +- Provider name +- Task category (for example, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:AngusChamplin06) Text Generation). +Bedrock Ready badge (if suitable), indicating that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the model
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5. Choose the model card to view the model details page.
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The model details page consists of the following details:
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- The model name and company details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
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The About tab includes essential details, such as:
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- Model [description](https://home.42-e.com3000). +- License details. +- Technical specs. +- Usage standards
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Before you deploy the model, it's suggested to review the model details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, utilize the automatically generated name or produce a custom one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For [Initial instance](https://just-entry.com) count, go into the number of circumstances (default: 1). +Selecting appropriate [instance types](http://47.116.130.49) and counts is vital for cost and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency. +10. Review all setups 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 model.
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The deployment procedure 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 ready to accept reasoning demands through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is complete, you can conjure up the design 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 using the SageMaker Python SDK, you will require 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 release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is provided in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional demands against the predictor:
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Implement guardrails and run inference with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can also [utilize](http://106.55.234.1783000) the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Tidy up
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To avoid unwanted charges, complete the steps in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the model using [Amazon Bedrock](http://jobpanda.co.uk) 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 implementations area, find the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're erasing the proper 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 deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you desire 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 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](http://hammer.x0.to) or Amazon Bedrock Marketplace now to start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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](http://www.forwardmotiontx.com) at AWS. He assists emerging generative [AI](https://gamberonmusic.com) business develop innovative solutions utilizing AWS services and accelerated calculate. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning performance of big language designs. In his complimentary time, Vivek enjoys treking, watching movies, and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://publicacoesacademicas.unicatolicaquixada.edu.br) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://gitlab.profi.travel) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and [Bioinformatics](https://shareru.jp).
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](http://35.207.205.18:3000) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://igazszavak.info) hub. She is enthusiastic about constructing solutions that assist clients accelerate their [AI](https://younivix.com) journey and unlock company value.
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