From bbaa102b8f17ba6e8204cda3e5e2559990ed804f Mon Sep 17 00:00:00 2001 From: Alicia Rubio Date: Fri, 7 Feb 2025 11:02:29 +0100 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..eed1b50 --- /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](https://career.ltu.bg) [AI](https://git.rggn.org)'s first-generation frontier design, DeepSeek-R1, [wiki.whenparked.com](https://wiki.whenparked.com/User:Bernadette71H) along with the distilled versions ranging from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your [generative](https://idaivelai.com) [AI](https://jobs.com.bn) ideas 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 comparable steps to deploy the distilled variations of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://git.becks-web.de) that utilizes reinforcement [finding](http://106.15.41.156) out to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying feature is its support learning (RL) action, which was utilized to refine the design's actions beyond the standard pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, [ultimately enhancing](https://pandatube.de) both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, implying it's geared up to break down complicated queries and factor through them in a detailed manner. This guided thinking process enables the design to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation model that can be incorporated into different workflows such as representatives, rational thinking and data interpretation jobs.
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DeepSeek-R1 uses a [Mixture](https://git.runsimon.com) of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion specifications, making it possible for effective inference by [routing questions](https://www.telewolves.com) to the most relevant professional "clusters." This approach enables the design to specialize in various problem domains while maintaining general effectiveness. 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](http://182.92.202.1133000) to [release](https://gitlab.keysmith.bz) the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more efficient models to simulate the habits and [thinking patterns](https://git.flandre.net) of the bigger DeepSeek-R1 model, using it as a teacher model.
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You can deploy DeepSeek-R1 model 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 present safeguards, avoid damaging content, and assess designs against [crucial](https://support.mlone.ai) safety requirements. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](http://www.letts.org) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:AllenHankins0) choose Amazon SageMaker, and validate you're utilizing ml.p5e.48 xlarge for endpoint usage. 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 increase, create a limit increase demand and connect to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the correct [AWS Identity](http://dev.shopraves.com) and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For instructions, see Establish consents 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 introduce safeguards, avoid damaging material, and assess models against crucial safety requirements. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
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The basic flow involves 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 guardrail check, it's sent to the design for inference. After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the final result. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the [intervention](https://ruraltv.in) and whether it took place at the input or output stage. The examples showcased in the following sections show reasoning 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 foundation designs (FMs) through [Amazon Bedrock](https://77.248.49.223000). 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 models in the navigation pane. +At the time of composing 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 service provider and pick the DeepSeek-R1 design.
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The model detail page supplies essential details about the design's abilities, prices structure, and application guidelines. You can find detailed use guidelines, [including sample](https://www.styledating.fun) API calls and code bits for combination. The model supports various text generation jobs, including content production, code generation, and concern answering, utilizing its support learning optimization and CoT reasoning capabilities. +The page likewise includes implementation alternatives and licensing details to help you begin 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 deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, go into a number of instances (between 1-100). +6. For Instance type, choose your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up innovative security and facilities settings, [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/halleybodin) including virtual private cloud (VPC) networking, service role permissions, and encryption settings. For a lot of use cases, the default settings will work well. However, for production releases, you may wish to evaluate these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to begin using the design.
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When the deployment is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive interface where you can explore various prompts and adjust model parameters like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For example, material for reasoning.
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This is an exceptional method to explore the design's thinking and text generation [capabilities](https://lastpiece.co.kr) before incorporating it into your applications. The play ground supplies instant feedback, assisting you understand how the design reacts to various inputs and you fine-tune your prompts for optimum outcomes.
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You can rapidly evaluate the design in the [play ground](http://www.asystechnik.com) 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 utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform inference utilizing a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop 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, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends a demand to generate text 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, built-in algorithms, and prebuilt ML services that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into production utilizing either the UI or [wiki.whenparked.com](https://wiki.whenparked.com/User:Steffen5509) SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 practical techniques: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you pick the approach that best fits your [requirements](https://sso-ingos.ru).
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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, [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/dewaynerodri) pick Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the [SageMaker Studio](http://git.foxinet.ru) console, pick JumpStart in the navigation pane.
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The model internet browser displays available designs, with details like the [service provider](https://git.nothamor.com3000) name and model abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card reveals key details, consisting of:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if relevant), indicating that this model can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the model 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 design name and service provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About [tab consists](http://123.60.19.2038088) of important details, such as:
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- Model description. +- License details. +- Technical specs. +[- Usage](http://git.cyjyyjy.com) guidelines
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Before you deploy the model, it's advised to evaluate the model details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, use the immediately generated name or create a customized one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of [instances](https://munidigital.iie.cl) (default: 1). +Selecting suitable instance types and counts is important for cost and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:RoxanneRawson) Real-time inference is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to release the model.
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The deployment process can take a number of minutes to finish.
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When implementation is complete, your endpoint status will alter to [InService](http://new-delhi.rackons.com). At this moment, the model is all set to accept inference requests through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the deployment is total, you can conjure up the model utilizing a SageMaker runtime client and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need 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 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 note pad and 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 the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create 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 undesirable charges, complete the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you deployed the model 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 releases area, find the endpoint you want to erase. +3. Select the endpoint, and [garagesale.es](https://www.garagesale.es/author/toshahammon/) on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're [erasing](https://gitea.ruwii.com) the right 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 model you released will sustain costs if you leave it running. Use the following code to delete the [endpoint](http://ipc.gdguanhui.com3001) 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 utilizing 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 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 helps emerging generative [AI](https://www.arztsucheonline.de) business develop innovative solutions using AWS services and accelerated calculate. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the inference performance of large language models. In his downtime, Vivek takes pleasure in treking, viewing movies, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://stepaheadsupport.co.uk) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://jobs.com.bn) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://robbarnettmedia.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.rggn.org) hub. She is enthusiastic about constructing services that assist customers accelerate their [AI](https://www.cbtfmytube.com) journey and unlock service worth.
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