From 7bfaf258434d0a99b1b0a3cdba5f2d5e389995e8 Mon Sep 17 00:00:00 2001 From: Alma Geer Date: Wed, 12 Mar 2025 11:51:40 +0100 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..6356d73 --- /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](http://40.73.118.158)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions [varying](http://www.isexsex.com) from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](http://47.109.153.57:3000) concepts on AWS.
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In this post, we show how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions 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 model (LLM) established by DeepSeek [AI](http://anggrek.aplikasi.web.id:3000) that uses reinforcement discovering to [enhance](https://golz.tv) reasoning abilities through a [multi-stage training](http://120.79.27.2323000) process from a DeepSeek-V3-Base structure. A crucial differentiating feature is its reinforcement knowing (RL) action, which was utilized to [fine-tune](https://pierre-humblot.com) the model's reactions beyond the basic [pre-training](https://heli.today) and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, indicating it's geared up to break down intricate queries and factor through them in a detailed manner. This guided thinking process permits the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured responses while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually captured the market's attention as a versatile text-generation model that can be incorporated into different workflows such as representatives, rational reasoning and data interpretation jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion criteria, making it possible for effective reasoning by routing queries to the most pertinent expert "clusters." This approach permits the design to concentrate on different issue domains while maintaining overall effectiveness. 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 circumstances to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective models to imitate the behavior and thinking patterns of the bigger DeepSeek-R1 design, using it as an instructor design.
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You can [release](https://smaphofilm.com) DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and examine models against crucial security criteria. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](http://163.228.224.105:3000) 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 examine if you have quotas for P5e, open the Service Quotas [console](https://macphersonwiki.mywikis.wiki) and under AWS Services, pick 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 request a limit boost, [develop](https://yourecruitplace.com.au) a limit boost demand and reach out to your account team.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock [Guardrails](https://jobs.askpyramid.com). For instructions, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:PeggyHocking58) see Establish approvals to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, prevent damaging material, and evaluate models against key security criteria. You can execute safety steps for the DeepSeek-R1 design using the Amazon Bedrock [ApplyGuardrail](https://sabiile.com) API. This enables you to apply guardrails to [examine](http://87.98.157.123000) user inputs and model responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
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The basic circulation includes the following steps: 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 design for inference. After getting the model's output, another guardrail check is applied. If the [output passes](https://gitea.evo-labs.org) this final 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 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 gives you access to over 100 popular, emerging, and specialized structure designs (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, select Model catalog under Foundation designs in the navigation pane. +At the time of writing 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 service provider and choose the DeepSeek-R1 design.
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The model detail page offers important details about the design's capabilities, pricing structure, and implementation standards. You can discover detailed use guidelines, consisting of sample API calls and code snippets for integration. The model supports various tasks, consisting of material creation, code generation, and question answering, utilizing its support finding out optimization and CoT thinking capabilities. +The page likewise consists of implementation options and licensing details to help you get begun with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, choose Deploy.
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You will be prompted to configure the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, get in a number of instances (between 1-100). +6. For Instance type, pick 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 infrastructure settings, including virtual private cloud (VPC) networking, service role approvals, and file encryption settings. For many use cases, the default settings will work well. However, for production deployments, you may wish to evaluate these settings to align with your [company's security](https://mediawiki.hcah.in) and compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the release is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive interface where you can explore different prompts and change model criteria like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For instance, content for inference.
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This is an exceptional way to check out the design's thinking and text generation capabilities before incorporating it into your applications. The [playground](https://xhandler.com) offers immediate feedback, assisting you understand how the model responds to various inputs and letting you tweak your prompts for ideal results.
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You can quickly evaluate the design in the [play ground](http://124.222.7.1803000) through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference using guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to perform inference using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock [console](https://equipifieds.com) or the API. For the example code to produce the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, [configures reasoning](http://expertsay.blog) parameters, and sends a demand to generate text based 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) center with FMs, integrated algorithms, and [prebuilt](http://www.xn--he5bi2aboq18a.com) ML options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient approaches: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both methods to help you pick the method that best matches your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release 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 create a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model [browser displays](https://gogs.yaoxiangedu.com) available models, with details like the company name and model capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card reveals key details, consisting of:
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[- Model](http://110.41.143.1288081) name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if appropriate), showing that this design 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 see the model details page.
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The design details page consists of the following details:
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- The model name and provider details. +Deploy button to deploy the design. +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 specs. +- Usage standards
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Before you deploy the design, it's recommended to examine the design 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, use the automatically generated name or develop a customized one. +8. For example [type ΒΈ](https://techport.io) pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the variety 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 inference is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to deploy the model.
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The implementation process can take a number of minutes to finish.
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When deployment is complete, your endpoint status will alter to [InService](https://phoebe.roshka.com). At this point, the design is ready to accept reasoning requests through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is complete, you can invoke 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 get begun with DeepSeek-R1 utilizing the [SageMaker Python](https://lastpiece.co.kr) SDK, you will need to set up the SageMaker Python SDK and make certain you have the needed AWS approvals 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 releasing the design is offered in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra 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, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:
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Clean up
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To avoid undesirable charges, complete the steps in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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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 releases area, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate 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 costs if you leave it running. 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 release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, [SageMaker](https://git.k8sutv.it.ntnu.no) JumpStart pretrained models, Amazon SageMaker JumpStart [Foundation](https://gogs.es-lab.de) 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 Architect for Inference at AWS. He helps emerging generative [AI](http://shenjj.xyz:3000) companies construct ingenious services using AWS services and sped up calculate. Currently, he is focused on [establishing strategies](https://gitea.taimedimg.com) for fine-tuning and optimizing the inference efficiency of big [language models](https://precise.co.za). In his leisure time, Vivek takes pleasure in treking, seeing movies, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://wiki.iurium.cz) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://stay22.kr) [accelerators](http://gsrl.uk) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect working on generative [AI](http://52.23.128.62:3000) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://gitea.shoulin.net) center. She is passionate about building solutions that help consumers accelerate their [AI](https://firstamendment.tv) journey and unlock business value.
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