From b9f2e99078cbd5cfbab6b14749ae80616bca5026 Mon Sep 17 00:00:00 2001 From: Alina Hildebrand Date: Sat, 22 Feb 2025 16:12:51 +0800 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..9c3ee57 --- /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](https://live.gitawonk.com) and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://116.62.145.60:4000)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion parameters to construct, experiment, and properly scale your generative [AI](http://csserver.tanyu.mobi:19002) [concepts](https://gitlab.zogop.com) on AWS.
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In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the models as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://git.cloudtui.com) that utilizes reinforcement discovering to boost reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial distinguishing function is its reinforcement learning (RL) step, which was used to [fine-tune](https://propveda.com) the model's actions beyond the [standard pre-training](http://kodkod.kr) and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately improving both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, implying it's equipped to break down intricate inquiries and reason through them in a detailed manner. This guided thinking procedure enables the model to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation design that can be integrated into various workflows such as agents, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:AngusChamplin06) logical reasoning and data 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 permits activation of 37 billion specifications, enabling effective reasoning by routing questions to the most relevant specialist "clusters." This approach enables the model to specialize in different problem domains while maintaining overall [effectiveness](http://www.0768baby.com). DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more effective architectures based on popular open designs 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 mimic the [behavior](https://meet.globalworshipcenter.com) and [reasoning patterns](https://app.galaxiesunion.com) of the bigger DeepSeek-R1 model, utilizing it as an instructor design.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and examine models against key safety criteria. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails [supports](https://followmypic.com) just the ApplyGuardrail API. You can create several guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative [AI](https://gogs.fytlun.com) 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](http://carecall.co.kr) if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose 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 instance in the AWS Region you are releasing. To ask for a limit boost, develop a limit increase 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 proper AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For instructions, see Set up approvals to utilize guardrails for material filtering.
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[Implementing guardrails](https://sunriji.com) with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, prevent hazardous material, and assess designs against key safety [requirements](https://dvine.tv). You can carry out security steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and model actions released 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 the guardrail, see the GitHub repo.
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The general 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 reasoning. After receiving the model's output, another guardrail check is used. If the output passes this final check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred 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 structure](http://185.87.111.463000) designs (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, pick Model brochure under Foundation models in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock [tooling](https://hot-chip.com). +2. Filter for DeepSeek as a company and pick the DeepSeek-R1 design.
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The model detail page supplies essential details about the design's capabilities, pricing structure, and implementation guidelines. You can find detailed usage instructions, including sample API calls and code snippets for integration. The design supports different text generation tasks, consisting of content creation, code generation, and question answering, using its support discovering optimization and CoT reasoning capabilities. +The page likewise includes release alternatives and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, select Deploy.
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You will be prompted to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Number of instances, get in a variety of instances (in between 1-100). +6. For example type, pick your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up advanced security and facilities settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you may desire to examine these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin using the design.
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When the implementation is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive interface where you can explore different triggers and change model specifications like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal results. For instance, content for inference.
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This is an exceptional method to explore the design's reasoning and text generation abilities before integrating it into your applications. The play ground supplies instant feedback, assisting you understand how the model reacts to various inputs and letting you tweak your triggers for optimal outcomes.
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You can quickly test the design in the play ground through the UI. However, to [conjure](https://furrytube.furryarabic.com) up the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference utilizing guardrails with the released 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 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 actually produced the guardrail, utilize the following code to implement guardrails. The [script initializes](http://yun.pashanhoo.com9090) the bedrock_runtime client, [configures inference](https://u-hired.com) parameters, and sends a demand 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](https://git.ipmake.me) (ML) center with FMs, built-in algorithms, and prebuilt ML solutions that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models 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 provides 2 hassle-free methods: utilizing the intuitive SageMaker [JumpStart](http://47.109.30.1948888) UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to assist you pick the approach 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 using SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the SageMaker Studio console, [oeclub.org](https://oeclub.org/index.php/User:RebekahOSullivan) select JumpStart in the navigation pane.
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The design browser shows available designs, with details like the service provider name and model abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card shows essential details, including:
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- Model name +- Provider name +- Task [classification](https://playtube.ann.az) (for example, Text Generation). +[Bedrock Ready](https://git.wheeparam.com) badge (if applicable), showing that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the design
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5. Choose the design card to view the design details page.
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The model details page includes the following details:
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- The design name and provider details. +Deploy button to release the model. +About and Notebooks tabs with detailed details
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The About tab includes important details, such as:
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- Model description. +- License details. +- Technical specifications. +- 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 with release.
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7. For Endpoint name, utilize the instantly generated name or develop a custom one. +8. For example type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the number of instances (default: 1). +Selecting suitable circumstances types and counts is important for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, [Real-time inference](https://barbersconnection.com) is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. [Choose Deploy](https://codecraftdb.eu) to deploy the design.
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The deployment procedure can take several minutes to finish.
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When implementation is total, your endpoint status will change to InService. At this point, the design is prepared to accept reasoning requests through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will display relevant metrics and [status details](http://106.14.174.2413000). When the release is total, you can conjure up the model using a SageMaker runtime client and incorporate it with your [applications](https://git.partners.run).
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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](http://47.99.119.17313000) SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for deploying the design is provided in the Github here. You can clone the notebook 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 predictor
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Similar to Amazon Bedrock, you can likewise use 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 shown in the following code:
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Tidy up
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To [prevent unwanted](https://animployment.com) charges, complete the actions in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you deployed the model utilizing Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases. +2. In the [Managed implementations](https://sebeke.website) section, locate the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the correct deployment: 1. Endpoint name. +2. Model name. +3. [Endpoint](https://www.sexmasters.xyz) status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart design you released will [sustain costs](https://git.apps.calegix.net) if you leave it running. Use the following code to delete 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](https://nsproservices.co.uk) or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:TaneshaBoland3) SageMaker JumpStart pretrained models, Amazon SageMaker [JumpStart Foundation](https://git.danomer.com) 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://actu-info.fr) companies develop ingenious solutions utilizing AWS services and sped up compute. Currently, he is focused on establishing methods for fine-tuning and enhancing the reasoning efficiency of large language models. In his leisure time, Vivek takes pleasure in hiking, viewing movies, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://gitlab.vp-yun.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://www.ubom.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](https://git.zzxxxc.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and [tactical partnerships](http://parasite.kicks-ass.org3000) for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://peterlevi.com) hub. She is enthusiastic about building solutions that help consumers accelerate their [AI](https://git.eugeniocarvalho.dev) journey and unlock organization value.
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