From 95cb322923786c4cee750cee73e266620b9b9050 Mon Sep 17 00:00:00 2001 From: armandbeatty01 Date: Tue, 18 Feb 2025 04:50:49 +0000 Subject: [PATCH] Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart --- ...tplace 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..3677591 --- /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 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](http://www.carnevalecommunity.it)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative [AI](https://git.getmind.cn) ideas on AWS.
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In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable [actions](https://gantnews.com) to deploy the distilled versions of the models also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://hitechjobs.me) that utilizes support learning to [improve thinking](https://gitlab.tncet.com) capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key distinguishing function is its reinforcement learning (RL) step, which was utilized to fine-tune the design's reactions beyond the basic pre-training and tweak procedure. By [including](http://47.97.6.98081) RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, eventually boosting both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, indicating it's equipped to break down intricate queries and reason through them in a detailed manner. This directed thinking process enables the model to produce more precise, transparent, and detailed responses. This model integrates RL-based [fine-tuning](https://network.janenk.com) with CoT abilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually captured the market's attention as a versatile text-generation design that can be incorporated into various workflows such as representatives, sensible thinking and information analysis tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) [architecture](https://socialsnug.net) and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, enabling efficient [reasoning](https://gitea.easio-com.com) by routing questions to the most pertinent expert "clusters." This approach permits the model to concentrate on various problem domains while maintaining overall effectiveness. 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 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 thinking abilities of the main R1 model to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, [disgaeawiki.info](https://disgaeawiki.info/index.php/User:MillieTum68) more efficient models to simulate the habits and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as a [teacher model](https://dokuwiki.stream).
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this design with guardrails in place. In this blog, we will use [Amazon Bedrock](https://www.contraband.ch) Guardrails to introduce safeguards, avoid hazardous material, and examine models against essential security criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several guardrails [tailored](https://git.ashcloudsolution.com) to various usage cases and use them to the DeepSeek-R1 design, [improving](https://music.lcn.asia) user experiences and standardizing security controls across your generative [AI](http://forum.pinoo.com.tr) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To inspect 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 releasing. To request a limit boost, develop a limitation 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 appropriate [AWS Identity](https://app.hireon.cc) and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see Establish permissions to use guardrails for material filtering.
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Implementing guardrails with the [ApplyGuardrail](https://it-storm.ru3000) API
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Amazon Bedrock Guardrails enables you to present safeguards, avoid damaging content, and evaluate models against key safety requirements. You can carry out safety procedures for the DeepSeek-R1 [design utilizing](https://git.intellect-labs.com) the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and model responses 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 basic flow includes the following actions: 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 model for reasoning. After getting 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 stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections demonstrate inference utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To [gain access](https://www.kenpoguy.com) to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, select Model brochure under Foundation designs in the navigation pane. +At the time of [composing](https://www.89u89.com) this post, you can use the [InvokeModel API](http://fggn.kr) to invoke the design. It does not support Converse APIs and other [Amazon Bedrock](https://git.itbcode.com) tooling. +2. Filter for [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:Antonetta51H) DeepSeek as a service provider and choose the DeepSeek-R1 model.
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The model detail page provides important details about the design's capabilities, rates structure, and application guidelines. You can discover detailed use directions, consisting of sample API calls and code snippets for combination. The model supports numerous text generation tasks, consisting of material development, code generation, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:ColinStoddard) and question answering, using its support finding out optimization and CoT thinking abilities. +The page likewise includes implementation alternatives and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be triggered to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, [89u89.com](https://www.89u89.com/author/sole7081199/) 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 circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based [circumstances type](https://gitlab-dev.yzone01.com) like ml.p5e.48 xlarge is recommended. +Optionally, you can set up sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service role permissions, and encryption settings. For most use cases, the default settings will work well. However, for production deployments, [89u89.com](https://www.89u89.com/author/maniegillin/) you might wish to evaluate these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the deployment is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play area to access an interactive user interface where you can try out various triggers and change model criteria like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For instance, material for reasoning.
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This is an excellent method to check out the design's thinking and text generation capabilities before integrating it into your applications. The play area supplies immediate feedback, helping you comprehend how the model reacts to numerous inputs and letting you fine-tune your triggers for optimum results.
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You can quickly test the model in the play area through the UI. However, to invoke the deployed 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 demonstrates how to perform reasoning utilizing a deployed DeepSeek-R1 design 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 [developed](https://oliszerver.hu8010) the guardrail, [utilize](https://git.pyme.io) the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends out a demand to [generate text](https://careers.tu-varna.bg) based upon a user timely.
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Deploy DeepSeek-R1 with [SageMaker](https://git.googoltech.com) JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) center with FMs, [integrated](http://47.98.226.2403000) algorithms, and prebuilt ML services that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 practical methods: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to help you pick 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 deploy DeepSeek-R1 using 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 develop a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The design [browser displays](https://gitea.easio-com.com) available models, with details like the company name and .
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card reveals crucial details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if suitable), suggesting that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the [model card](https://timviec24h.com.vn) to see the design details page.
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The design details page consists of the following details:
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- The model name and supplier details. +Deploy button to deploy the design. +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 specs. +- Usage guidelines
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Before you deploy the model, it's recommended to examine the model details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, utilize the instantly generated name or develop a custom one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of instances (default: 1). +Selecting suitable circumstances types and counts is essential for expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and [low latency](https://posthaos.ru). +10. Review all configurations for precision. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that [network isolation](https://gitea.freshbrewed.science) remains in location. +11. Choose Deploy to deploy the design.
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The implementation procedure can take several minutes to finish.
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When deployment is total, your endpoint status will alter to InService. At this point, the design is prepared to accept inference requests through the [endpoint](https://selfloveaffirmations.net). You can keep track of the deployment progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the [implementation](https://geetgram.com) is total, you can invoke the model utilizing a SageMaker runtime customer and [incorporate](https://liveyard.tech4443) it with your [applications](https://friendify.sbs).
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Deploy DeepSeek-R1 using 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 required AWS permissions and [fishtanklive.wiki](https://fishtanklive.wiki/User:GiuseppeXve) 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 design is supplied in the Github here. You can clone the [notebook](https://precise.co.za) and range from [SageMaker Studio](https://takesavillage.club).
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You can run extra 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 likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as revealed in the following code:
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Tidy up
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To prevent undesirable charges, complete the actions in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you deployed the model using Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace deployments. +2. In the Managed implementations section, find the endpoint you wish 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 proper deployment: [gratisafhalen.be](https://gratisafhalen.be/author/richelleteb/) 1. Endpoint name. +2. Model name. +3. Endpoint status
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Delete the [SageMaker JumpStart](https://rrallytv.com) predictor
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The SageMaker JumpStart model you released will sustain expenses if you leave it running. Use the following code to delete the [endpoint](http://gitlab.andorsoft.ad) 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 explored how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker [JumpStart](http://124.71.134.1463000) in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, [SageMaker JumpStart](http://101.200.33.643000) 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 at AWS. He helps emerging generative [AI](https://wiki.eqoarevival.com) companies construct innovative options using AWS services and [accelerated](https://wiki.ragnaworld.net) compute. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the reasoning efficiency of large language models. In his spare time, Vivek delights in treking, viewing movies, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://gogs.yaoxiangedu.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://www.thegrainfather.com.au) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](http://63.32.145.226) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://code.qutaovip.com) hub. She is passionate about developing solutions that assist clients accelerate their [AI](http://47.109.30.194:8888) journey and unlock organization value.
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