From 9c11f3db46971fd3d58ecccbc4ef40c6ad67c487 Mon Sep 17 00:00:00 2001 From: Erika Bustard Date: Thu, 13 Feb 2025 10:18:02 +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..6a178fb --- /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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://178.44.118.232)'s first-generation frontier design, DeepSeek-R1, [wavedream.wiki](https://wavedream.wiki/index.php/User:Natalie6866) along with the distilled versions varying from 1.5 to 70 billion specifications to build, experiment, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) and responsibly scale your generative [AI](https://aubameyangclub.com) [concepts](https://103.1.12.176) on AWS.
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In this post, we [demonstrate](http://wrs.spdns.eu) how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled versions 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) developed by DeepSeek [AI](http://47.107.132.138:3000) that utilizes support [discovering](https://vieclam.tuoitrethaibinh.vn) to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key identifying feature is its reinforcement knowing (RL) action, which was utilized to improve the model's reactions beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually improving both relevance and [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:AnnabelleV59) clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, implying it's geared up to break down complex questions and reason through them in a detailed way. This assisted reasoning procedure allows the model to produce more accurate, transparent, and detailed answers. This design integrates [RL-based](http://47.75.109.82) fine-tuning with CoT abilities, aiming to create structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has captured the industry's attention as a [flexible](https://jobedges.com) text-generation model that can be incorporated into numerous workflows such as representatives, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:ElmaAfford18621) sensible reasoning and information interpretation jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion specifications, making it possible for effective inference by routing queries to the most pertinent expert "clusters." This method enables the design to specialize in different problem domains while maintaining overall performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 model to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more effective models to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher design.
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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 suggest releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and evaluate models against key safety criteria. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to different use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](http://forum.kirmizigulyazilim.com) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you need access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm 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 releasing. To ask for a limitation boost, create a limit increase request and connect to your account group.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and [Gain Access](http://barungogi.com) To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Establish approvals to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, avoid hazardous material, and [examine designs](https://brightworks.com.sg) against key security criteria. You can implement security procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and model reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a [guardrail](https://git.haowumc.com) using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
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The basic circulation includes the following actions: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the check, it's sent out to the design for reasoning. After getting the design's output, another guardrail check is applied. If the output passes this final check, [links.gtanet.com.br](https://links.gtanet.com.br/sharidarr65) it's [returned](https://albion-albd.online) as the final 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 occurred at the input or output stage. The examples showcased in the following sections show 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 designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the [InvokeModel API](https://janhelp.co.in) to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and choose the DeepSeek-R1 design.
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The design detail page supplies important details about the [model's](http://lty.co.kr) capabilities, rates structure, and implementation standards. You can discover detailed use instructions, consisting of sample API calls and code snippets for integration. The model supports numerous text generation tasks, consisting of content production, code generation, and question answering, using its reinforcement finding out optimization and CoT reasoning abilities. +The page likewise includes deployment choices and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, pick Deploy.
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You will be triggered to set up the implementation details for DeepSeek-R1. The design 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 number of instances (between 1-100). +6. For example type, pick your circumstances type. For optimal efficiency with DeepSeek-R1, a [GPU-based circumstances](https://internship.af) type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production releases, you may want to examine 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 release is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive interface where you can experiment with various triggers and adjust model parameters like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For instance, material for reasoning.
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This is an exceptional method to explore the model's thinking and text generation capabilities before incorporating it into your applications. The play area offers instant feedback, helping you understand how the model reacts to different inputs and letting you fine-tune your prompts for [optimum](https://121gamers.com) results.
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You can rapidly evaluate the design in the play area through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you need 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 demonstrates how to carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a [guardrail](https://mhealth-consulting.eu) using the Amazon Bedrock console or the API. For the example code to produce 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 customer, sets up inference parameters, and sends out a request to [produce text](https://social.oneworldonesai.com) based upon a user timely.
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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 ML services 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 deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides two hassle-free methods: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you choose the approach that finest fits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to deploy DeepSeek-R1 using 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 produce a domain. +3. On the SageMaker Studio console, pick [JumpStart](https://talentocentroamerica.com) in the navigation pane.
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The design web browser shows available designs, with details like the service provider name and design capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card shows key details, consisting of:
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[- Model](https://bibi-kai.com) name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if applicable), indicating that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the design card to see the design details page.
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The design details page consists of the following details:
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- The model name and service provider details. +Deploy button to deploy the model. +About and Notebooks tabs with [detailed](http://150.158.93.1453000) 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 evaluate the design details and license terms to validate compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, use the immediately produced name or [wavedream.wiki](https://wavedream.wiki/index.php/User:VeroniqueBernhar) produce a custom-made one. +8. For [Instance type](https://jobs.campus-party.org) ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the variety of instances (default: 1). +Selecting appropriate circumstances types and counts is crucial for expense and performance 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 [precision](http://carpediem.so30000). For this design, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to deploy the design.
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The implementation procedure can take several minutes to complete.
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When release is complete, your endpoint status will change to InService. At this point, the model is prepared to accept reasoning requests through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will show pertinent metrics and status [details](http://8.222.247.203000). When the deployment is complete, you can conjure up the design using a [SageMaker runtime](https://901radio.com) customer and integrate it with your [applications](https://sosmed.almarifah.id).
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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 require 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 release and use DeepSeek-R1 for inference programmatically. The code for deploying the model is offered in the Github here. You can clone the notebook and [ratemywifey.com](https://ratemywifey.com/author/ollieholtze/) run 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 [utilize](http://git.dashitech.com) 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 [revealed](https://code.paperxp.com) in the following code:
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Clean up
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To avoid undesirable charges, finish the actions in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you [released](http://git.thinkpbx.com) the design using Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations. +2. In the Managed implementations 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 deleting the correct 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 released will sustain expenses 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 model 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 models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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://www.yourtalentvisa.com) business construct innovative solutions using AWS services and accelerated calculate. Currently, he is focused on developing strategies for fine-tuning and enhancing the inference performance of big language models. In his [totally](http://www.carnevalecommunity.it) free time, Vivek takes pleasure in hiking, seeing motion pictures, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://112.48.22.196:3000) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://47.99.37.63:8099) 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://www.nenboy.com:29283) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://maarifatv.ng) hub. She is passionate about constructing solutions that assist clients accelerate their [AI](https://europlus.us) journey and unlock business worth.
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