commit 43deee59361d78875f464f18dc31d92740c46ff7 Author: hanneloretyx52 Date: Thu Apr 10 11:10:00 2025 +0800 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..da0c412 --- /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 release DeepSeek [AI](https://paksarkarijob.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](http://183.221.101.89:3000) ideas on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled variations of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](https://calamitylane.com) that uses reinforcement learning to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial distinguishing function is its support knowing (RL) action, which was used to improve the model's responses beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and objectives, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:Jerrell1351) ultimately improving both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, suggesting it's geared up to break down complex queries and reason through them in a detailed manner. This directed thinking process enables the design to produce more precise, transparent, and [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:GarrettMcConnel) detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has captured the market's attention as a flexible text-generation model that can be incorporated into numerous workflows such as agents, sensible reasoning and data interpretation jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion specifications, enabling efficient inference by routing questions to the most appropriate specialist "clusters." This technique allows the model to focus on various problem domains while maintaining overall performance. 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 instance to [release](https://gitea.phywyj.dynv6.net) the design. ml.p5e.48 xlarge comes with 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 model to more efficient architectures based on popular open designs 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 imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, using it as a teacher design.
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You can [release](https://demo.theme-sky.com) DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an [emerging](https://gitea.gumirov.xyz) design, we [advise deploying](https://snapfyn.com) this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) prevent harmful content, and assess designs against key security criteria. At the time of composing this blog, for DeepSeek-R1 releases on [SageMaker JumpStart](https://git.collincahill.dev) and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative [AI](https://www.virfans.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint use. 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, produce a limitation boost 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 right AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) guidelines, see Set up consents to use guardrails for content filtering.
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Implementing guardrails with the [ApplyGuardrail](https://musicplayer.hu) API
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Amazon Bedrock [Guardrails permits](http://jobjungle.co.za) you to introduce safeguards, prevent harmful content, and evaluate designs against key safety criteria. You can carry out for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce 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 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 reasoning. 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 intervened by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections [demonstrate reasoning](https://friendspo.com) using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon [Bedrock Marketplace](https://gitlab.dituhui.com) 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 composing this post, you can utilize the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and select the DeepSeek-R1 design.
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The model detail page offers essential details about the design's abilities, rates structure, and application guidelines. You can find detailed use instructions, including sample [API calls](http://8.137.103.2213000) and code bits for combination. The model supports different text generation jobs, including material development, code generation, and concern answering, using its reinforcement finding out optimization and CoT thinking capabilities. +The page also consists of deployment choices and licensing details to assist you get begun with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, choose Deploy.
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You will be prompted to set up the release details for DeepSeek-R1. The model 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 circumstances (in between 1-100). +6. For [Instance](http://112.74.93.6622234) type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function permissions, and encryption settings. For many use cases, the default settings will work well. However, for production deployments, you may wish to evaluate these settings to align with your company's security and [compliance requirements](http://43.138.57.2023000). +7. Choose Deploy to start utilizing the model.
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When the implementation is complete, you can evaluate 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 change model specifications like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For instance, content for reasoning.
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This is an exceptional way to check out the design's reasoning and text generation abilities before integrating it into your applications. The playground supplies immediate feedback, assisting you understand how the model reacts to different inputs and letting you tweak your prompts for optimum results.
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You can rapidly evaluate the model in the play ground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning using guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create 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 actually produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up inference specifications, and sends a request to create text 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) hub with FMs, integrated algorithms, and prebuilt ML solutions that you can release with just 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 model through [SageMaker JumpStart](http://gbtk.com) offers two convenient techniques: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to help you choose the method that finest fits 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 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, choose JumpStart in the navigation pane.
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The model internet browser displays available designs, with details like the supplier name and design capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card reveals crucial details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if applicable), [suggesting](http://118.89.58.193000) that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to see the model details page.
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The design details page includes the following details:
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- The design name and company details. +Deploy button to release 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 requirements. +- Usage standards
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Before you deploy the model, it's recommended to review the [design details](https://www.worlddiary.co) and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to [proceed](http://git.dgtis.com) with deployment.
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7. For Endpoint name, use the automatically generated name or create a customized one. +8. For Instance type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of instances (default: 1). +Selecting appropriate instance types and counts is important for cost and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low [latency](https://studentvolunteers.us). +10. Review all configurations for accuracy. For [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:AlejandrinaGard) this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the model.
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The deployment process can take several minutes to finish.
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When release is total, your endpoint status will change to InService. At this moment, the design is ready to accept inference requests through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and [status details](http://ccrr.ru). When the release is complete, you can conjure up the model utilizing a SageMaker runtime client and integrate it with your [applications](https://funitube.com).
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get started with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS permissions and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is [supplied](https://social.acadri.org) in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional demands 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 create a guardrail utilizing the Amazon [Bedrock console](http://dgzyt.xyz3000) or the API, and implement it as shown in the following code:
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Tidy up
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To prevent undesirable charges, finish the actions in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the design 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, choose Marketplace deployments. +2. In the Managed implementations section, find the endpoint you want to delete. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate 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](https://weeddirectory.com) design you deployed will sustain costs if you leave it [running](http://gitlabhwy.kmlckj.com). 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 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](http://anggrek.aplikasi.web.id3000) or Amazon Bedrock [Marketplace](https://jobs.but.co.id) now to start. For more details, refer to Use [Amazon Bedrock](http://8.136.197.2303000) tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.
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About the Authors
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[Vivek Gangasani](https://fotobinge.pincandies.com) is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://git.cbcl7.com) companies build innovative solutions using AWS services and sped up calculate. Currently, he is concentrated on [developing techniques](http://forum.infonzplus.net) for fine-tuning and optimizing the reasoning performance of large language designs. In his spare time, Vivek delights in treking, watching movies, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.sewosoft.de) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://gitlab.thesunflowerlab.com) 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 dealing with generative [AI](https://www.athleticzoneforum.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](https://gitea.nongnghiepso.com) [AI](https://publiccharters.org) hub. She is enthusiastic about developing services that help clients accelerate their [AI](https://git.li-yo.ts.net) journey and unlock organization value.
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