From 20d1bf601bea8021d600aaeece9af8449a88de04 Mon Sep 17 00:00:00 2001 From: andreasilvers7 Date: Fri, 7 Feb 2025 09:52:53 +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..13e9650 --- /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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://vitricongty.com)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](https://sahabatcasn.com) concepts 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 deploy the [distilled versions](http://e-kou.jp) of the models as well.
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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://47.108.161.78:3000) that uses support finding out to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial distinguishing function is its support knowing (RL) step, which was used to refine the design's reactions beyond the basic pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, meaning it's geared up to break down complicated queries and reason through them in a detailed way. This directed reasoning process permits the model to [produce](https://followmypic.com) more accurate, transparent, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1073113) and detailed answers. This design integrates RL-based [fine-tuning](https://lms.digi4equality.eu) with CoT abilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually recorded the market's attention as a [versatile](https://www.openstreetmap.org) [text-generation design](http://47.101.139.60) that can be integrated into various workflows such as representatives, logical reasoning and data interpretation tasks.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion specifications, making it possible for effective inference by routing queries to the most pertinent expert "clusters." This technique permits the design to specialize in various problem domains while maintaining general effectiveness. 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 release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 model to more [effective architectures](http://106.55.61.1283000) based upon popular open [designs](http://1.117.194.11510080) like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient models to imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor model.
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You can deploy DeepSeek-R1 design either through [SageMaker JumpStart](http://getthejob.ma) or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid damaging 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 several guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](https://shiapedia.1god.org) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and [kousokuwiki.org](http://kousokuwiki.org/wiki/%E5%88%A9%E7%94%A8%E8%80%85:AnkeStarnes867) under AWS Services, select Amazon SageMaker, and verify 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, create a limit increase demand 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 appropriate AWS Identity and [disgaeawiki.info](https://disgaeawiki.info/index.php/User:KelleG0472) Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For instructions, see Establish authorizations to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to introduce safeguards, prevent harmful content, and evaluate designs against key safety requirements. You can execute safety measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and design actions [deployed](http://gitlab.ileadgame.net) on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock [console](http://getthejob.ma) or the API. For the example code to create the guardrail, see the [GitHub repo](https://code.miraclezhb.com).
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The basic circulation includes the following actions: First, the system [receives](http://www.boot-gebraucht.de) 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 reasoning. After getting the model's output, another [guardrail check](https://elsalvador4ktv.com) is used. If the output passes this last check, it's returned as the result. 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 stage. The examples showcased in the following areas show reasoning using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon [Bedrock Marketplace](http://39.108.86.523000) provides you access to over 100 popular, emerging, and specialized foundation models (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 brochure under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to conjure up the model. It does not support Converse APIs and [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 design.
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The model detail page supplies vital [details](https://git.jerl.dev) about the model's capabilities, rates structure, and application guidelines. You can discover detailed use directions, consisting of sample API calls and code bits for integration. The design supports various text generation tasks, consisting of material creation, code generation, and [concern](https://kommunalwiki.boell.de) answering, utilizing its reinforcement finding out optimization and CoT thinking abilities. +The page also consists of implementation alternatives and licensing details to help you start with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, choose 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, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, get in a number of circumstances (between 1-100). +6. For Instance type, pick your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up [sophisticated security](https://basedwa.re) and facilities settings, including virtual private cloud (VPC) networking, service function consents, and encryption settings. For many use cases, the default settings will work well. However, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1073364) for production implementations, you may wish to examine these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start using the design.
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When the deployment is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can explore various prompts and adjust model criteria like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal results. For instance, material for reasoning.
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This is an exceptional way to check out the design's thinking and text generation abilities before incorporating it into your applications. The play ground offers immediate feedback, helping you understand how the model reacts to different inputs and letting you tweak your triggers for optimal results.
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You can rapidly check the model in the play area through the UI. However, to invoke the [released design](https://www.jobzpakistan.info) programmatically with any Amazon Bedrock APIs, you need 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 shows how to perform reasoning utilizing a released DeepSeek-R1 design through Amazon Bedrock using 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 produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends out a request to [produce text](http://repo.bpo.technology) 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, built-in algorithms, and prebuilt ML options that you can deploy with simply a couple of clicks. With SageMaker JumpStart, [wavedream.wiki](https://wavedream.wiki/index.php/User:ClaireSparling1) you can tailor pre-trained designs to your usage case, with your information, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 practical techniques: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to help you choose the technique that best matches your [requirements](https://wiki.vifm.info).
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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, choose Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design web browser shows available models, with details like the provider name and [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:ColinStoddard) design capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card shows essential details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if suitable), showing that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the design
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5. Choose the model card to see the model details page.
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The model 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 details
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The About tab consists of important details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage standards
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Before you release the design, it's suggested to review the design details and license terms to confirm 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 automatically generated name or develop a custom one. +8. For example type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the variety of circumstances (default: 1). +Selecting suitable circumstances types and counts is important for expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, [Real-time reasoning](https://savico.com.br) is selected by default. This is enhanced for [sustained traffic](https://www.sportpassionhub.com) and low latency. +10. Review all setups for accuracy. For this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to release the design.
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The deployment process can take a number of minutes to finish.
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When implementation is total, your endpoint status will change to InService. At this moment, the model is prepared to accept inference demands through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the [implementation](https://sing.ibible.hk) is total, you can conjure up the model 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 SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is supplied in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run additional 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 likewise 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 shown in the following code:
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Clean up
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To prevent unwanted charges, finish the actions in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the model utilizing Amazon Bedrock Marketplace, total the following steps:
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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, find the [endpoint](https://www.webthemes.ca) you wish to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're erasing the proper deployment: 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 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 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead [Specialist Solutions](https://tjoobloom.com) Architect for Inference at AWS. He helps emerging generative [AI](https://localjobpost.com) [business construct](https://solegeekz.com) ingenious services using [AWS services](http://parasite.kicks-ass.org3000) and accelerated compute. Currently, he is concentrated on establishing strategies for fine-tuning and enhancing the reasoning efficiency of big language models. In his [leisure](https://embargo.energy) time, Vivek enjoys hiking, viewing movies, and various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://houseimmo.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://git.ascarion.org) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional [Solutions Architect](https://fydate.com) dealing with generative [AI](https://www.freeadzforum.com) with the Third-Party Model Science group 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://findschools.worldofdentistry.org) hub. She is passionate about constructing options that help clients accelerate their [AI](https://app.joy-match.com) journey and unlock service worth.
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