From 2dfe270092bda08f7aed24233a491e2ba0a5bcf2 Mon Sep 17 00:00:00 2001 From: Albertina Vasey Date: Thu, 27 Feb 2025 04:05:23 +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..47d1287 --- /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 distilled Llama and Qwen models are available through [Amazon Bedrock](http://51.222.156.2503000) Marketplace and Amazon SageMaker [JumpStart](http://fangding.picp.vip6060). With this launch, you can now release DeepSeek [AI](http://sehwaapparel.co.kr)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](https://biiut.com) ideas 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 versions of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](http://59.57.4.66:3000) that uses reinforcement learning to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key differentiating feature is its [reinforcement learning](http://ja7ic.dxguy.net) (RL) action, which was utilized to fine-tune the design's responses beyond the standard pre-training and [fine-tuning procedure](http://repo.magicbane.com). By including RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, eventually boosting both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, suggesting it's geared up to break down complicated queries and factor through them in a detailed manner. This assisted thinking [process permits](https://wrqbt.com) the model to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation model that can be incorporated into different workflows such as agents, sensible thinking and data interpretation jobs.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, allowing efficient [reasoning](https://corevacancies.com) by routing queries to the most appropriate [specialist](http://moyora.today) "clusters." This technique enables the design to focus on various problem domains while maintaining general efficiency. 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 instance to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of [GPU memory](https://drshirvany.ir).
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DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of [training](https://raisacanada.com) smaller sized, more efficient models to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor model.
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You can release DeepSeek-R1 design 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 utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and assess designs against key safety 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 create multiple guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative [AI](http://ja7ic.dxguy.net) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're utilizing 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 limitation boost, develop a limit increase demand and reach out to your [account](https://pyra-handheld.com) group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock [Guardrails](http://118.89.58.193000). For guidelines, see Set up authorizations to use guardrails for material 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 examine designs against key security criteria. You can implement safety steps for the DeepSeek-R1 design using the Amazon Bedrock [ApplyGuardrail](http://175.178.113.2203000) API. This allows you to use guardrails to evaluate user inputs and design actions deployed on Amazon Bedrock [Marketplace](https://mysazle.com) and SageMaker JumpStart. You can create 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 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 out to the design for inference. After getting the model's output, another guardrail check is used. If the output passes this last check, it's [returned](http://ufidahz.com.cn9015) as the result. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas demonstrate inference using 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 foundation](https://huconnect.org) 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 brochure under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 model.
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The model detail page supplies vital details about the design's capabilities, rates structure, and implementation guidelines. You can discover detailed use instructions, including sample API calls and code snippets for integration. The design supports various text generation tasks, consisting of content development, code generation, and question answering, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:FaustinoMayberry) utilizing its reinforcement learning optimization and CoT reasoning abilities. +The page also consists of deployment options and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
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You will be triggered to set up 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 Variety of circumstances, enter a number of circumstances (between 1-100). +6. For example type, pick your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure advanced security and facilities settings, including virtual private cloud (VPC) networking, service role permissions, and encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you might wish to review these settings to line up with your company's security and compliance requirements. +7. [Choose Deploy](https://live.gitawonk.com) to start using the model.
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When the implementation is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in playground to access an interactive interface where you can try out various prompts and change design criteria like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum results. For example, content for inference.
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This is an outstanding method to check out the design's thinking and text generation capabilities before incorporating it into your applications. The play ground supplies instant feedback, assisting you understand how the model reacts to numerous inputs and letting you fine-tune your triggers for ideal results.
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You can rapidly evaluate the design in the playground 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 inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have actually created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends out a demand to [generate text](https://barokafunerals.co.za) based on a user timely.
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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](https://gogs.lnart.com) that you can deploy with just a few clicks. With SageMaker JumpStart, you can [tailor pre-trained](http://www.hakyoun.co.kr) models to your use case, with your data, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 practical approaches: utilizing the user-friendly 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 fits 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 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](https://gruppl.com) a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The design browser shows available models, with details like the provider name and model abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design key details, consisting of:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +[Bedrock Ready](https://gitlab.keysmith.bz) badge (if suitable), indicating that this model can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
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5. Choose the [model card](https://code.jigmedatse.com) to see the model details page.
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The model details page consists of the following details:
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- The design name and supplier details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab includes essential details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage standards
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Before you deploy the model, 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 deployment.
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7. For Endpoint name, use the instantly produced name or create a custom-made one. +8. For example type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the variety of instances (default: 1). +Selecting proper instance types and counts is crucial for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we [highly advise](https://tricityfriends.com) 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 process can take several minutes to complete.
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When implementation is total, your endpoint status will alter to InService. At this moment, the design is all set to accept reasoning requests through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is total, you can conjure up the design utilizing a SageMaker runtime customer and incorporate 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 SDK, you will need to set up 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. The code for deploying the design is offered in the Github here. You can clone the note pad 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 inference with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using 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 avoid unwanted charges, complete the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the design using Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace deployments. +2. In the Managed implementations area, find the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the proper release: 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](http://gitlab.ds-s.cn30000) if you leave it running. Use the following code to erase the endpoint if you desire 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 deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, 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 Architect for Inference at AWS. He assists emerging generative [AI](https://friendify.sbs) business develop innovative options utilizing AWS services and sped up [calculate](https://asromafansclub.com). Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the reasoning efficiency of large language models. In his spare time, Vivek delights in hiking, enjoying movies, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://116.62.159.194) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://cameotv.cc) 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](https://gitlab.syncad.com) with the Third-Party Model Science group at AWS.
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[Banu Nagasundaram](https://bogazicitube.com.tr) leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](https://git.trov.ar) [AI](http://gogsb.soaringnova.com) center. She is passionate about constructing solutions that help customers accelerate their [AI](https://jobs.theelitejob.com) journey and unlock organization worth.
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