From 4a0c2c87cf99b317a7690dfffc53418e390bed36 Mon Sep 17 00:00:00 2001 From: dianaferrier0 Date: Wed, 9 Apr 2025 20:46:58 +0800 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..ccc4275 --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://easterntalent.eu)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion specifications to construct, experiment, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:CharleyRudall29) and properly scale your generative [AI](https://telecomgurus.in) concepts on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the models also.
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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://182.92.196.181) that utilizes reinforcement discovering to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key differentiating feature is its reinforcement learning (RL) step, which was utilized to refine the design's responses beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, ultimately enhancing both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, implying it's geared up to break down [complex queries](http://120.79.75.2023000) and factor through them in a detailed way. This directed reasoning procedure enables the design to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation design that can be integrated into different workflows such as representatives, logical thinking and information interpretation tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion specifications, making it possible for effective reasoning by routing queries to the most relevant specialist "clusters." This approach permits the model to specialize in various [issue domains](https://git.juxiong.net) while maintaining total efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge includes 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 design to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more effective designs to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 design, using it as an instructor model.
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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 model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and assess designs against essential safety requirements. At the time of writing this blog site, for DeepSeek-R1 [deployments](https://galmudugjobs.com) on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative [AI](http://121.40.114.127:9000) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, 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](https://lms.digi4equality.eu) SageMaker, and validate you're using 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 deploying. To ask for a limit boost, produce a limit increase 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 proper AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For instructions, see Establish permissions 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 models against essential security requirements. You can execute security measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to [produce](https://manilall.com) the guardrail, see the GitHub repo.
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The basic flow [involves](https://empregos.acheigrandevix.com.br) the following actions: First, 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 to the design for inference. 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 indicating the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following areas demonstrate inference 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 structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, select 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 does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and select the DeepSeek-R1 design.
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The model detail page provides vital details about the model's capabilities, prices structure, and application standards. You can discover detailed usage instructions, consisting of [sample API](https://www.arztstellen.com) calls and code snippets for integration. The model supports numerous text generation jobs, including material production, code generation, and concern answering, utilizing its support finding out optimization and CoT thinking abilities. +The page also consists of deployment options and licensing details to help you get begun with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, [select Deploy](http://www.origtek.com2999).
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You will be prompted to configure 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 of circumstances, enter a variety of circumstances (in between 1-100). +6. For Instance type, pick your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can set up sophisticated security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role approvals, and file encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you might wish to evaluate these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the release is total, you can check DeepSeek-R1's capabilities 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 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 template for optimum outcomes. For instance, content for reasoning.
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This is an excellent method to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The playground supplies immediate feedback, assisting you understand how the model reacts to numerous inputs and letting you tweak your [triggers](http://acs-21.com) for ideal outcomes.
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You can quickly check the model in the play area through the UI. However, to [conjure](https://www.dpfremovalnottingham.com) up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce 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 actually produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference specifications, and sends a request to create [text based](https://interconnectionpeople.se) upon 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 ML options 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 release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 practical techniques: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you choose the technique that best fits 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 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, choose JumpStart in the [navigation pane](http://118.195.204.2528080).
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The model browser shows available designs, with [details](https://git.pilzinsel64.de) like the provider name and model capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card reveals essential details, consisting of:
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- Model name +[- Provider](https://www.heesah.com) name +- Task category (for example, Text Generation). +[Bedrock Ready](https://git.juxiong.net) badge (if suitable), [suggesting](https://www.groceryshopping.co.za) that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to view the design details page.
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The design details page includes the following details:
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- The model name and provider details. +Deploy button to release 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](https://jobboat.co.uk) description. +- License details. +- Technical specifications. +- Usage guidelines
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Before you release 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 continue with deployment.
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7. For Endpoint name, use the automatically generated name or develop a custom-made one. +8. For example [type ΒΈ](https://trabajosmexico.online) choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of circumstances (default: 1). +Selecting appropriate instance types and counts is essential for expense and efficiency optimization. Monitor your deployment to change these [settings](http://42.194.159.649981) as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we highly recommend adhering 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 release procedure can take a number of minutes to finish.
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When deployment is complete, your endpoint status will change to InService. At this point, the model is all set to accept inference requests through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is total, 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 get going 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 authorizations and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for [inference programmatically](http://safepine.co3000). The code for deploying the design is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.
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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](https://famenest.com) predictor
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Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker [JumpStart predictor](https://origintraffic.com). You can create a guardrail using the [Amazon Bedrock](http://optx.dscloud.me32779) console or the API, and implement it as revealed in the following code:
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Clean up
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To avoid undesirable charges, complete the actions in this area to clean up your [resources](https://git.qingbs.com).
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Delete the Amazon Bedrock [Marketplace](https://remotejobsint.com) release
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If you deployed the design utilizing Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace implementations. +2. In the Managed implementations section, locate the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, select 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 released will sustain costs 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 checked out how you can access and [release](http://git.cnibsp.com) the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker [JumpStart](https://gitlab.econtent.lu) in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, 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 Architect for Inference at AWS. He helps emerging generative [AI](https://wathelp.com) companies build ingenious services utilizing AWS services and sped up compute. Currently, he is focused on establishing techniques for fine-tuning and [optimizing](https://demo.pixelphotoscript.com) the [inference performance](https://git.lain.church) of big language designs. In his downtime, Vivek delights in treking, watching motion pictures, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.mpowerplacement.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://gogs.xinziying.com) 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](https://git.freesoftwareservers.com) with the Third-Party Model Science team at AWS.
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[Banu Nagasundaram](https://sugoi.tur.br) leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://safeway.com.bd) center. She is enthusiastic about constructing solutions that help consumers accelerate their [AI](https://my.beninwebtv.com) journey and unlock company worth.
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