From 0253d09c7a28f4d572713971d1834622a97d431d Mon Sep 17 00:00:00 2001 From: Adela Harbison Date: Thu, 20 Feb 2025 03:19:12 +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..e616d07 --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are delighted to reveal that DeepSeek R1 [distilled Llama](http://101.34.211.1723000) and Qwen designs are available through [Amazon Bedrock](https://www.jobsition.com) Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://release.rupeetracker.in)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](http://git.fmode.cn:3000) concepts on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the designs 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](https://doop.africa) that uses support learning to improve thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential differentiating feature is its reinforcement learning (RL) action, which was used to refine the model's responses beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately improving both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, suggesting it's equipped to break down complicated questions and reason through them in a detailed manner. This guided reasoning procedure allows the design to produce more precise, transparent, and detailed responses. This design integrates RL-based [fine-tuning](http://47.101.46.1243000) with CoT abilities, aiming to create structured actions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has recorded the market's attention as a versatile text-generation model that can be incorporated into various workflows such as representatives, sensible thinking and information 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 permits activation of 37 billion parameters, allowing efficient reasoning by routing inquiries to the most pertinent professional "clusters." This technique allows the model to specialize in different issue domains while maintaining general efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to [release](https://www.celest-interim.fr) the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 [GPUs supplying](https://gitea.v-box.cn) 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 models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more efficient designs to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 design, using it as a teacher model.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and evaluate models against essential safety requirements. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](https://git.creeperrush.fun) 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 under AWS Services, [choose Amazon](http://103.242.56.3510080) SageMaker, and verify 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 deploying. To request a limit boost, produce 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) [permissions](http://xn--ok0b74gbuofpaf7p.com) to use Amazon Bedrock Guardrails. For instructions, see [Establish authorizations](https://gitea.gai-co.com) 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 present safeguards, prevent harmful content, and examine models against crucial safety criteria. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and model responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce 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](https://bence.net) includes the following actions: 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 out to the design for reasoning. After receiving the model's output, another guardrail check is used. If the output passes this final check, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) 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 took place at the input or [output stage](https://baitshepegi.co.za). The examples showcased in the following areas show [reasoning utilizing](https://git.unicom.studio) 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](https://job.firm.in) models (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, select [Model brochure](http://119.23.72.7) under Foundation models in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and select the DeepSeek-R1 model.
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The design detail page offers necessary details about the model's abilities, prices structure, and implementation standards. You can find detailed usage directions, including sample API calls and code bits for [integration](https://git.caraus.tech). The model supports different text generation tasks, including material creation, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:MatthewOconner5) code generation, and concern answering, using its support learning optimization and CoT thinking abilities. +The page also includes implementation choices and licensing details to assist you get started 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 implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Number of instances, get in a number of instances (in between 1-100). +6. For example type, select your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service function permissions, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production releases, you may want to evaluate these settings to align with your company's security and [it-viking.ch](http://it-viking.ch/index.php/User:ShannanMullen43) compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the release is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play area to access an interactive interface where you can explore different triggers and change design specifications 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 instance, content for inference.
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This is an exceptional way to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The play area offers instant feedback, assisting you understand how the design reacts to different inputs and letting you tweak your triggers for optimal results.
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You can quickly check the design in the playground through the UI. However, to invoke the released model 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 demonstrates how to carry out reasoning utilizing a [deployed](https://www.genbecle.com) DeepSeek-R1 model through Amazon Bedrock [utilizing](http://194.87.97.823000) the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends out a demand to generate text 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) 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 information, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient methods: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SDK. Let's explore both techniques to assist you choose the approach that best fits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be triggered to produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model browser displays available designs, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2746667) with details like the supplier name and design capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card shows key details, consisting of:
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- Model name +- Provider name +- [Task classification](https://git.mintmuse.com) (for example, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this design can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model
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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 deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of essential details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage guidelines
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Before you release the model, it's advised to examine the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, use the instantly created name or create a custom one. +8. For Instance type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of circumstances (default: 1). +Selecting proper circumstances types and counts is important for cost and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this design, we highly recommend sticking to SageMaker JumpStart default settings and [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1335129) making certain that network isolation remains in place. +11. Choose Deploy to release the model.
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The implementation procedure can take a number of minutes to complete.
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When deployment is total, your endpoint status will alter to InService. At this point, the design is all set to accept inference demands through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will show [pertinent](https://www.cittamondoagency.it) metrics and status details. When the [deployment](https://filuv.bnkode.com) is complete, you can invoke the design using a [SageMaker runtime](https://gogs.xinziying.com) client and integrate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that demonstrates how to release and use 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 extra 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:
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Tidy up
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To avoid undesirable charges, complete the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you [released](https://gogs.les-refugies.fr) the design using Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations. +2. In the Managed deployments area, find 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 proper 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 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 release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For [garagesale.es](https://www.garagesale.es/author/roscoehavel/) more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, 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](https://mhealth-consulting.eu) at AWS. He assists emerging generative [AI](http://rackons.com) business build ingenious services using AWS services and accelerated compute. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the inference efficiency of large language models. In his spare time, Vivek takes pleasure in hiking, seeing motion pictures, and trying different foods.
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[Niithiyn Vijeaswaran](https://sudanre.com) is a Generative [AI](http://www.zjzhcn.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://194.87.97.82:3000) 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 dealing with [generative](https://bandbtextile.de) [AI](http://47.103.112.133) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and [tactical partnerships](https://gogs.les-refugies.fr) for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://dev.fleeped.com) center. She is enthusiastic about building solutions that assist clients accelerate their [AI](http://180.76.133.253:16300) journey and unlock company worth.
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