commit 69ced7dd8c476836300fa4e3ba27b0e637b60482 Author: eloisatrudeau Date: Sun Apr 6 10:42:38 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..751daad --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://addify.ae)'s first-generation frontier design, DeepSeek-R1, in addition to the [distilled versions](https://wegoemploi.com) varying from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative [AI](https://www.yanyikele.com) ideas on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock [Marketplace](http://59.37.167.938091) and SageMaker JumpStart. You can follow similar steps to release the distilled variations of the designs 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://video.chops.com) that uses reinforcement finding out to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential differentiating function is its reinforcement knowing (RL) step, which was used to refine the model's actions beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, ultimately boosting both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, implying it's equipped to break down complex questions and reason through them in a detailed way. This guided reasoning [process](https://dubaijobzone.com) enables the design to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually caught the market's attention as a versatile text-generation model that can be integrated into numerous workflows such as representatives, logical reasoning and data analysis 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 parameters, enabling effective reasoning by [routing inquiries](http://tpgm7.com) to the most pertinent expert "clusters." This technique allows the model to focus on various issue domains while maintaining total effectiveness. 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 [release](https://social.japrime.id) the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design to more effective architectures based on 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](https://www.ubom.com) to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher design.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this design with [guardrails](https://men7ty.com) in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and assess designs against essential 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 multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, enhancing user [experiences](https://git.szrcai.ru) and standardizing safety controls throughout your generative [AI](https://gallery.wideworldvideo.com) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you require 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 SageMaker, and [validate](http://81.68.246.1736680) you're using ml.p5e.48 xlarge for endpoint usage. 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 increase, produce a limit increase request and connect to your account team.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For directions, see Establish authorizations to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to present safeguards, prevent damaging material, and evaluate models against key safety criteria. You can implement safety steps for the DeepSeek-R1 [model utilizing](http://47.104.65.21419206) the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and model actions deployed 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 develop the guardrail, see the GitHub repo.
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The general flow 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 guardrail check, it's sent to the design for reasoning. After receiving the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the final outcome. 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 sections demonstrate reasoning utilizing 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 foundation 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, select Model brochure under Foundation models in the navigation pane. +At the time of this post, you can utilize the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 design.
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The design detail page provides necessary details about the design's capabilities, prices structure, and execution standards. You can discover detailed use guidelines, consisting of sample API calls and code snippets for combination. The model supports various text generation tasks, including content creation, code generation, and question answering, using its reinforcement discovering optimization and CoT thinking abilities. +The page also includes deployment options and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, choose Deploy.
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You will be triggered to set up the deployment 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 Number of instances, enter a number of instances (between 1-100). +6. For example type, pick your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure advanced security and infrastructure settings, [consisting](https://gitea.scalz.cloud) of virtual personal cloud (VPC) networking, service function consents, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production releases, you might wish to evaluate these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the implementation is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive interface where you can try out various prompts and adjust design specifications like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For example, content for inference.
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This is an excellent way to check out the design's thinking and [garagesale.es](https://www.garagesale.es/author/shennaburch/) text generation capabilities before integrating it into your applications. The play ground supplies immediate feedback, helping you comprehend how the design responds to various inputs and letting you tweak your triggers for ideal results.
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You can quickly test the model in the play ground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference utilizing guardrails with the deployed 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 produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the [GitHub repo](https://git.bubblesthebunny.com). After you have developed the guardrail, utilize the following code to execute guardrails. The script [initializes](http://tanpoposc.com) the bedrock_runtime client, configures inference specifications, and sends out a request to create text based on a user prompt.
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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 [solutions](https://www.celest-interim.fr) that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses two convenient techniques: using the intuitive SageMaker [JumpStart](https://www.ubom.com) UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to help you choose the method that best matches your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to release 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 triggered to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model browser displays available designs, with details like the supplier name and model abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card shows key details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if appropriate), suggesting that this model can be signed up with Amazon Bedrock, [enabling](https://rna.link) you to utilize Amazon [Bedrock APIs](https://gitea.ws.adacts.com) to invoke the design
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5. Choose the design card to view the design details page.
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The design details page includes the following details:
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- The design name and provider details. +[Deploy button](http://codaip.co.kr) to release 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 requirements. +- Usage standards
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Before you deploy the model, it's suggested to review the [design details](https://bug-bounty.firwal.com) and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, use the immediately created name or produce a custom-made one. +8. For Instance type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of instances (default: 1). +Selecting suitable instance types and counts is essential for expense and performance optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time reasoning is selected by [default](https://gitlab.oc3.ru). This is optimized 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 release the design.
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The deployment process can take several minutes to complete.
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When implementation is complete, your endpoint status will alter to InService. At this moment, the model is all set to accept inference demands through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is total, you can invoke the model using a SageMaker runtime customer and incorporate 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 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that [demonstrates](https://www.runsimon.com) how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for [releasing](http://media.nudigi.id) the model 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 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console 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, complete the actions in this area to clean up your [resources](https://www.ynxbd.cn8888).
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Delete the Amazon Bedrock Marketplace release
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If you released the model utilizing 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 deployments. +2. In the Managed releases section, locate the endpoint you desire 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 appropriate 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 design you released will sustain costs 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 checked out how you can access and deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker [JumpStart](http://git.jihengcc.cn) designs, SageMaker JumpStart pretrained models, Amazon SageMaker [JumpStart](https://canadasimple.com) 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 helps emerging generative [AI](https://orka.org.rs) companies develop ingenious solutions using AWS services and accelerated compute. Currently, he is [concentrated](https://videopromotor.com) on developing strategies for fine-tuning and enhancing the reasoning performance of big language designs. In his leisure time, Vivek delights in hiking, viewing movies, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://8.222.216.184:3000) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://git.tbd.yanzuoguang.com) 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](http://223.68.171.150:8004) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](https://gitea.scalz.cloud) [AI](http://211.159.154.98:3000) center. She is passionate about constructing solutions that assist clients accelerate their [AI](https://bdenc.com) journey and unlock company worth.
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