commit b894013d3992a8d2cb2c6e4ae29d6de7be015a6a Author: jasonedgar8742 Date: Thu Feb 20 02:12:41 2025 +0000 Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart 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..f835a69 --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://jobster.pk)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](https://www.koumii.com) ideas on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled versions of the designs 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](https://git.yinas.cn) that uses reinforcement discovering to [boost reasoning](http://60.250.156.2303000) capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential [differentiating feature](https://dev-members.writeappreviews.com) is its support knowing (RL) action, which was used to fine-tune the model's reactions beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately enhancing both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, meaning it's geared up to break down complex questions and factor through them in a detailed manner. This directed reasoning procedure enables the model to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation model that can be integrated into different workflows such as representatives, sensible reasoning and information analysis tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion [parameters](https://www.huntsrecruitment.com) in size. The MoE architecture permits activation of 37 billion criteria, making it possible for efficient reasoning by routing questions to the most relevant professional "clusters." This technique enables the design to focus on various issue domains while [maintaining](https://www.heesah.com) total efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 design to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more efficient models to simulate the habits and thinking patterns of the larger DeepSeek-R1 model, utilizing it as an instructor design.
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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 recommend releasing this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and examine models against key security criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple 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://lohashanji.com) [applications](https://git.haowumc.com).
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Prerequisites
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To deploy the DeepSeek-R1 model, 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, pick 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 circumstances in the AWS Region you are releasing. To ask for a limit boost, produce a limit increase demand and reach out to your account team.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For instructions, see Set up approvals 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 material, and evaluate models against crucial safety requirements. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and design 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 create the guardrail, see the GitHub repo.
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The general circulation 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 inference. After receiving the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following sections demonstrate reasoning using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and [specialized structure](https://travelpages.com.gh) designs (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 catalog 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 doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 design.
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The design detail page offers vital details about the model's abilities, pricing structure, and [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:SheliaDenovan) application standards. You can discover detailed use instructions, consisting of [sample API](https://newvideos.com) calls and code snippets for integration. The model supports numerous text generation tasks, including material production, code generation, and concern answering, utilizing its support finding out optimization and CoT reasoning capabilities. +The page also consists of deployment options and licensing details to assist you get started with DeepSeek-R1 in your [applications](https://iinnsource.com). +3. To start using DeepSeek-R1, select Deploy.
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You will be prompted to set up the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, get in a number of instances (between 1-100). +6. For [Instance](https://higgledy-piggledy.xyz) type, select your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function approvals, and encryption settings. For the majority of utilize cases, [89u89.com](https://www.89u89.com/author/sole7081199/) the default settings will work well. However, for production deployments, you might wish to examine these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the implementation is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play area to access an interactive user interface where you can experiment with various triggers and adjust design parameters like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For instance, material for inference.
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This is an outstanding way to explore the design's reasoning and text generation capabilities before integrating it into your applications. The play area supplies immediate feedback, assisting you comprehend how the design responds to [numerous](https://puzzle.thedimeland.com) inputs and letting you fine-tune your triggers for ideal outcomes.
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You can rapidly evaluate the design in the play area through the UI. However, to conjure up the released design programmatically with any [Amazon Bedrock](https://rsh-recruitment.nl) 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 perform reasoning using a [released](https://dandaelitetransportllc.com) DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon [Bedrock console](https://quierochance.com) or the API. For the example code to create the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends out a request to produce 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) hub with FMs, integrated algorithms, and prebuilt ML services that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and deploy them into production using either the UI or SDK.
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[Deploying](https://itconsulting.millims.com) DeepSeek-R1 design through SageMaker JumpStart provides 2 [convenient](http://121.4.70.43000) techniques: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you select the [technique](https://nuswar.com) that best suits your requirements.
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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, select Studio in the navigation pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design browser displays available designs, with [details](https://writerunblocks.com) like the service provider name and model abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card reveals essential details, including:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this design can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the [design card](https://gitlab.innive.com) 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 . +Deploy button to release the design. +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 guidelines
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Before you release the design, it's advised to evaluate the design details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, utilize the immediately generated name or develop a custom one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the variety of instances (default: 1). +Selecting appropriate circumstances types and counts is essential for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is [enhanced](https://dronio24.com) for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we highly suggest 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 deployment process can take [numerous](https://www.oradebusiness.eu) minutes to finish.
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When deployment is complete, your endpoint status will alter to InService. At this moment, the design is all set to accept reasoning demands through the endpoint. You can keep an eye on the implementation 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 begin 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 authorizations and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the design is [offered](https://carrieresecurite.fr) in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run additional 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock [console](https://www.jobexpertsindia.com) or the API, and execute it as shown in the following code:
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Tidy up
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To avoid unwanted charges, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:GalenSchoenheime) complete the steps 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 using Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments. +2. In the Managed implementations section, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate release: 1. [Endpoint](https://soundfy.ebamix.com.br) 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 deployed will sustain costs if you leave it running. Use the following code to erase the endpoint if you desire 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 checked out how you can access and [wavedream.wiki](https://wavedream.wiki/index.php/User:VeroniqueBernhar) deploy the DeepSeek-R1 model utilizing 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 designs, SageMaker JumpStart pretrained models, 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 Architect for Inference at AWS. He assists emerging generative [AI](https://www.webthemes.ca) companies develop ingenious solutions using AWS services and sped up calculate. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the inference performance of large language designs. In his leisure time, Vivek takes pleasure in hiking, seeing movies, and attempting different foods.
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Niithiyn Vijeaswaran is a [Generative](https://www.iratechsolutions.com) [AI](http://107.182.30.190:6000) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://www.jr-it-services.de:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://git.cocorolife.tw) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://onthewaytohell.com) center. She is [passionate](https://git.gilesmunn.com) about building solutions that help clients accelerate their [AI](http://kuzeydogu.ogo.org.tr) journey and unlock organization value.
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