From b47079ae1b3dcbb4144e179602f63de831e94b7d Mon Sep 17 00:00:00 2001 From: raewalcott7838 Date: Mon, 17 Feb 2025 07:23:17 +0000 Subject: [PATCH] Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-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..6ff6b73 --- /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 reveal that DeepSeek R1 and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://51.68.46.170)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations [ranging](https://gitlab.edebe.com.br) from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](http://35.207.205.18:3000) 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 similar actions to release the distilled variations of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](http://carpetube.com) that utilizes support [learning](https://studentvolunteers.us) to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential identifying function is its support learning (RL) step, which was used to refine the design's responses beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, eventually enhancing both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, meaning it's geared up to break down complex questions and reason through them in a [detailed manner](http://47.75.109.82). This directed thinking procedure allows the design to produce more precise, transparent, and detailed responses. This model [integrates](https://edujobs.itpcrm.net) [RL-based fine-tuning](http://dev.icrosswalk.ru46300) with CoT capabilities, aiming to create structured reactions while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation model that can be incorporated into numerous workflows such as agents, sensible [thinking](http://jialcheerful.club3000) and data interpretation tasks.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion criteria, allowing effective [inference](http://119.45.195.10615001) by routing questions to the most pertinent professional "clusters." This approach enables the design to concentrate on different problem [domains](https://cn.wejob.info) while maintaining general performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 [GPUs offering](http://www.xn--80agdtqbchdq6j.xn--p1ai) 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more [efficient designs](http://47.103.108.263000) to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher model.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging material, and [examine models](http://gitlab.fuxicarbon.com) against key security requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails [tailored](https://wkla.no-ip.biz) to different usage cases and apply them to the DeepSeek-R1 design, improving user experiences and [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:Veola182848) standardizing security controls throughout your generative [AI](https://linkin.commoners.in) 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 check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're using 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 request a limit boost, create a limit increase request and reach out to your account group.
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Because you will be [deploying](https://ofebo.com) this design with Amazon Bedrock Guardrails, make certain you have the [correct AWS](https://bizad.io) Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For guidelines, see Establish authorizations to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, prevent hazardous material, and assess designs against essential security requirements. You can implement safety steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and design actions released on [Amazon Bedrock](http://h2kelim.com) Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
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The general flow involves the following actions: First, the system gets an input for the design. 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 receiving 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 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 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 catalog under Foundation models 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 tooling. +2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 model.
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The model detail page supplies essential details about the design's abilities, rates structure, and implementation standards. You can find detailed use guidelines, consisting of sample API calls and code snippets for integration. The design supports numerous text generation jobs, consisting of content development, code generation, and question answering, utilizing its reinforcement finding out optimization and [CoT reasoning](http://xn--jj-xu1im7bd43bzvos7a5l04n158a8xe.com) abilities. +The page likewise includes [implementation choices](http://gogs.kuaihuoyun.com3000) and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, choose Deploy.
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You will be prompted to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, go into a variety of circumstances (in between 1-100). +6. For example type, select your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can configure innovative security and facilities settings, including virtual personal cloud (VPC) networking, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:DannielleDixson) service role consents, and file encryption settings. For the majority of utilize cases, the [default settings](http://78.108.145.233000) will work well. However, for production deployments, you might desire to evaluate these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start using the design.
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When the implementation is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive user interface where you can try out different prompts and change design criteria like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For instance, content for inference.
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This is an excellent way to explore the model's thinking and text generation capabilities before integrating it into your applications. The playground offers instant feedback, helping you understand how the design reacts to numerous inputs and letting you fine-tune your prompts for [ideal outcomes](https://melanatedpeople.net).
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You can [rapidly](http://gitea.digiclib.cn801) check the model in the play area through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require 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 carry out inference utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the [Amazon Bedrock](https://spreek.me) console or the API. For the example code to create the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up inference criteria, and sends a request to generate 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) hub with FMs, integrated algorithms, and [prebuilt](https://gitea.gm56.ru) ML [services](https://familytrip.kr) that you can deploy with simply a couple of clicks. With [SageMaker](https://funitube.com) JumpStart, you can tailor pre-trained models 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 convenient methods: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you pick the method that best fits your needs.
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Deploy DeepSeek-R1 through [SageMaker JumpStart](https://karjerosdienos.vilniustech.lt) 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 triggered to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model internet browser shows available designs, with details like the supplier name and design abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card shows crucial details, consisting of:
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[- Model](https://git.muehlberg.net) name +- Provider name +- Task category (for example, Text Generation). +[Bedrock Ready](https://krazzykross.com) badge (if suitable), showing that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure 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](https://jobiaa.com) page includes the following details:
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- The model name and provider details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
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The About tab consists of crucial details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage guidelines
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Before you release the design, it's suggested to evaluate the model details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, use the automatically generated name or create a customized one. +8. For Instance type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the number of circumstances (default: 1). +Selecting appropriate instance types and counts is essential for cost and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this model, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to release the model.
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The deployment procedure can take a number of minutes to complete.
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When deployment is total, your endpoint status will change to InService. At this moment, the design is ready to accept inference requests through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is complete, you can conjure up the model utilizing a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To start with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the design is supplied in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra demands against the predictor:
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Implement [guardrails](https://musicplayer.hu) and run inference with your SageMaker JumpStart 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 utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Clean up
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To prevent undesirable charges, finish 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 deployed the model using Amazon Bedrock Marketplace, complete the following actions:
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1. On the [Amazon Bedrock](https://jobiaa.com) console, under [Foundation](https://git.dev-store.xyz) models in the navigation pane, choose Marketplace releases. +2. In the Managed releases section, locate 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 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 deployed will sustain costs if you leave it [running](http://47.120.70.168000). 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 explored 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 begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, 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](https://platform.giftedsoulsent.com) Architect for Inference at AWS. He assists emerging generative [AI](https://macphersonwiki.mywikis.wiki) companies build innovative solutions utilizing AWS services and [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:DenishaHolyfield) sped up calculate. Currently, he is focused on developing techniques for fine-tuning and enhancing the reasoning performance of large [language models](https://foke.chat). In his downtime, Vivek delights in hiking, viewing films, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://47.105.180.150:30002) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://kyeongsan.co.kr) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](http://dndplacement.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://cagit.cacode.net) hub. She is enthusiastic about building solutions that assist customers accelerate their [AI](https://apyarx.com) journey and unlock organization value.
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