commit 5e50ec5000ca92b5d6d35bb1aa51910317e04a64 Author: evonne21413144 Date: Thu Feb 13 07:22:00 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..1204945 --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://git.torrents-csv.com)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion parameters to develop, experiment, and [properly scale](http://git.jaxc.cn) your generative [AI](http://playtube.ythomas.fr) concepts 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 comparable steps to deploy the distilled versions of the models as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](http://jerl.zone:3000) that utilizes reinforcement discovering to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key differentiating function is its support learning (RL) step, which was used to improve the model's reactions beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually boosting both importance and clearness. In addition, DeepSeek-R1 utilizes a [chain-of-thought](https://choosy.cc) (CoT) approach, implying it's equipped to break down [complex inquiries](https://raovatonline.org) and factor through them in a detailed way. This assisted reasoning process allows the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as agents, logical thinking and information interpretation tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion criteria, allowing efficient reasoning by routing queries to the most pertinent expert "clusters." This technique enables the design to focus on various problem domains while maintaining general 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 model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of [GPU memory](http://okna-samara.com.ru).
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DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 design to more [efficient architectures](http://www.litehome.top) based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more efficient models to imitate the behavior and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor design.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we [recommend deploying](https://circassianweb.com) this model with [guardrails](https://theindietube.com) in location. In this blog site, we will utilize Amazon Bedrock Guardrails to [introduce](https://reckoningz.com) safeguards, avoid hazardous material, and assess models against [essential security](http://bolling-afb.rackons.com) criteria. 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 several guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative [AI](https://www.cbl.aero) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, 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 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 releasing this design with [Amazon Bedrock](http://wiki-tb-service.com) Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For directions, see Set up authorizations to utilize guardrails for material [filtering](http://47.120.16.1378889).
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to present safeguards, avoid damaging material, and examine models against key safety requirements. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing 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, [surgiteams.com](https://surgiteams.com/index.php/User:ToneyGosse71) 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 inference. After receiving the model'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 indicating](https://dhivideo.com) the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following sections show reasoning utilizing 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, total the following actions:
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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 utilize the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 model.
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The model detail page offers important details about the model's capabilities, pricing structure, and implementation guidelines. You can find detailed usage directions, consisting of sample API calls and code snippets for combination. The design supports different text generation jobs, including content creation, code generation, and question answering, using its support discovering optimization and CoT thinking capabilities. +The page likewise consists of implementation alternatives and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be prompted to set up the implementation 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 [Variety](https://iamzoyah.com) of circumstances, go into a number of instances (between 1-100). +6. For example type, pick 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 sophisticated security and infrastructure settings, including virtual private cloud (VPC) networking, service role approvals, and encryption settings. For most use cases, the default settings will work well. However, for [production](http://120.92.38.24410880) deployments, you might desire to review these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to [start utilizing](https://gitea.jessy-lebrun.fr) the design.
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When the implementation is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive user interface where you can try out various triggers and adjust model specifications like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal results. For example, content for inference.
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This is an [excellent method](https://2flab.com) to check out the model's reasoning and text generation capabilities before integrating it into your applications. The playground supplies instant feedback, helping you comprehend how the [design reacts](http://144.123.43.1382023) to various inputs and letting you fine-tune your prompts for optimal results.
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You can rapidly check the model 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 reasoning using guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock [console](https://git.tanxhub.com) or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually developed the guardrail, [utilize](https://scienetic.de) the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, and sends out a demand to create text based upon a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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[SageMaker JumpStart](http://121.37.208.1923000) is an artificial intelligence (ML) center with FMs, [integrated](https://v-jobs.net) algorithms, and [prebuilt](http://111.9.47.10510244) ML [solutions](https://git.clicknpush.ca) that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained [designs](https://fromkorea.kr) to your usage 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 convenient methods: using the intuitive SageMaker [JumpStart UI](https://www.gotonaukri.com) or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you select the approach that best fits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions 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 prompted to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design internet browser displays available designs, with details like the provider name and design abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card shows essential details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if suitable), showing that this model can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to view the model details page.
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The model details page includes the following details:
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- The model 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 includes crucial details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage standards
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Before you release the model, it's advised to review the model details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, use the immediately produced name or develop a customized one. +8. For example type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial [instance](https://repos.ubtob.net) count, enter the number of circumstances (default: 1). +Selecting proper instance types and counts is crucial 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 enhanced for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to release the model.
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The implementation procedure can take numerous minutes to finish.
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When deployment is complete, your endpoint status will alter to InService. At this point, the model is all set to accept reasoning demands through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the deployment is complete, you can invoke the design using a [SageMaker runtime](https://gogs.2dz.fi) customer and [incorporate](https://luckyway7.com) 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 need to install the [SageMaker Python](https://www.atlantistechnical.com) 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 use DeepSeek-R1 for inference programmatically. The code for deploying the model is supplied in the Github here. You can clone the notebook and range 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, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:ColinStoddard) you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a [guardrail utilizing](http://thinking.zicp.io3000) the Amazon Bedrock console or the API, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:GLXKatrice) and execute it as revealed in the following code:
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Tidy up
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To avoid undesirable charges, complete the actions in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the design utilizing Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace releases. +2. In the Managed releases section, find the [endpoint](https://nukestuff.co.uk) you want to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the right 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 design you released will sustain expenses if you leave it running. Use the following code to delete the endpoint if you want 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 explored how you can access and [wiki.myamens.com](http://wiki.myamens.com/index.php/User:MarylynEsmond) deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with [Amazon SageMaker](https://talentocentroamerica.com) JumpStart designs, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:DaleneCollins99) SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart 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 assists emerging generative [AI](https://setiathome.berkeley.edu) business construct ingenious services utilizing AWS services and sped up [calculate](https://gitea.scalz.cloud). Currently, he is concentrated on developing strategies for fine-tuning and enhancing the of large language models. In his spare time, Vivek delights in hiking, viewing films, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://kyeongsan.co.kr) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://www.boutique.maxisujets.net) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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[Jonathan Evans](http://150.158.93.1453000) is a Specialist Solutions Architect dealing with generative [AI](http://101.200.220.49:8001) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/bagjanine969) and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.jobassembly.com) hub. She is passionate about constructing solutions that help [customers accelerate](http://8.130.72.6318081) their [AI](https://wisewayrecruitment.com) journey and unlock company worth.
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