From 1090709110002d36c5779100bab8f1f78c5e6c64 Mon Sep 17 00:00:00 2001 From: genetraugott54 Date: Thu, 3 Apr 2025 14:08:07 +0000 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..4015753 --- /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 announce that [DeepSeek](http://g-friend.co.kr) R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://dvine.tv)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative [AI](https://172.105.135.218) 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 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 design (LLM) developed by DeepSeek [AI](https://sugardaddyschile.cl) that utilizes support learning to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential differentiating function is its [reinforcement knowing](http://git.acdts.top3000) (RL) action, which was utilized to refine the model's responses beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust more successfully to user feedback and goals, eventually boosting both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it's geared up to break down complicated questions and factor through them in a detailed manner. This guided reasoning process enables the model to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation design that can be incorporated into numerous workflows such as representatives, sensible reasoning and information analysis tasks.
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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 specifications, allowing efficient reasoning by routing inquiries to the most relevant specialist "clusters." This technique allows the model to specialize in various problem domains while maintaining total performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more efficient designs to imitate the habits and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as a teacher design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock [Marketplace](https://lepostecanada.com). Because DeepSeek-R1 is an emerging design, we suggest deploying this model with guardrails in location. In this blog site, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:DeanneCarswell) we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and examine designs against crucial security criteria. At the time of [writing](https://freeworld.global) this blog, for DeepSeek-R1 [releases](https://saathiyo.com) on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](https://gitea.thuispc.dynu.net) applications.
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Prerequisites
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To [release](https://farmwoo.com) the DeepSeek-R1 design, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm 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 request a limitation increase, create a limitation boost 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) approvals to use Amazon Bedrock Guardrails. For instructions, see Set up approvals to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, prevent harmful content, and evaluate models against crucial safety criteria. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to use 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 [wiki.whenparked.com](https://wiki.whenparked.com/User:OpalGmn46347349) the example code to create the guardrail, see the GitHub repo.
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The general circulation involves the following actions: First, the system [receives](https://social.instinxtreme.com) 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 final check, it's [returned](https://www.characterlist.com) as the last result. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections show reasoning using 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 foundation 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, choose Model catalog 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 does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for [raovatonline.org](https://raovatonline.org/author/dwaynepalme/) DeepSeek as a supplier and choose the DeepSeek-R1 design.
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The model detail page offers essential details about the model's capabilities, [pricing](http://gitlab.together.social) structure, and implementation standards. You can discover detailed use directions, consisting of [sample API](http://company-bf.com) calls and code bits for integration. The model supports numerous text generation tasks, consisting of content development, code generation, and question answering, utilizing its support discovering optimization and CoT thinking abilities. +The page likewise consists of release alternatives and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, select Deploy.
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You will be prompted to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, get in a variety of circumstances (in between 1-100). +6. For example type, choose your circumstances 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 private cloud (VPC) networking, [service function](http://www.jedge.top3000) authorizations, and encryption settings. For many use cases, the default settings will work well. However, for production deployments, you might want to examine these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the deployment is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in playground to access an interactive user interface where you can explore various prompts and adjust design parameters like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For instance, content for inference.
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This is an outstanding way to check out the model's thinking and text generation abilities before integrating it into your [applications](http://fggn.kr). The playground provides immediate feedback, helping you understand how the model reacts to different inputs and letting you tweak your prompts for optimal outcomes.
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You can rapidly evaluate the design 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](https://crossroad-bj.com).
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Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and [ApplyGuardrail API](http://142.93.151.79). You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference criteria, and sends a demand to [generate text](http://47.104.60.1587777) 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, built-in algorithms, and prebuilt ML [options](https://iamzoyah.com) that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and release them into production utilizing either the UI or SDK.
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[Deploying](http://47.92.218.2153000) DeepSeek-R1 model through [SageMaker JumpStart](http://042.ne.jp) uses two convenient techniques: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you select the technique that finest suits your [requirements](http://git.jihengcc.cn).
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following [actions](http://kcinema.co.kr) to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model web browser shows available models, with details like the provider name and design capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card reveals crucial details, consisting of:
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[- Model](http://webheaydemo.co.uk) name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if suitable), indicating that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the design card to view the design details page.
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The [model details](https://frce.de) page includes the following details:
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- The model name and service provider details. +[Deploy button](http://szfinest.com6060) 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 requirements. +- Usage guidelines
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Before you deploy the model, it's suggested to review the model details and license terms to confirm 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 created name or create a custom one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the variety of circumstances (default: 1). +Selecting proper [instance types](http://sl860.com) and counts is crucial for cost and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the model.
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The release process can take a number of minutes to complete.
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When implementation is total, your endpoint status will change to InService. At this point, the design is all set to accept inference [demands](https://finitipartners.com) through the endpoint. You can monitor the implementation development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is complete, you can invoke the design using a SageMaker runtime client and incorporate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the required AWS permissions and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1334081) reasoning programmatically. The code for releasing the model is provided in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run extra 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Tidy up
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To avoid undesirable charges, finish the actions 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 design utilizing Amazon [Bedrock](https://bihiring.com) 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 deployments area, find the endpoint you wish to erase. +3. Select the endpoint, and on the [Actions](https://hrvatskinogomet.com) menu, select 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 deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you want 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 using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](https://www.lizyum.com) or Amazon Bedrock Marketplace now to get started. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.
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About the Authors
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Vivek [Gangasani](https://hypmediagh.com) is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](http://www.topverse.world:3000) business construct ingenious options using AWS services and accelerated calculate. Currently, he is focused on developing techniques for fine-tuning and optimizing the reasoning efficiency of big [language models](http://193.30.123.1883500). In his downtime, Vivek enjoys treking, seeing motion pictures, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://shareru.jp) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://203.171.20.94:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](https://www.bongmedia.tv) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.social.united-tuesday.org) center. She is passionate about building services that assist customers accelerate their [AI](https://cariere.depozitulmax.ro) [journey](https://topcareerscaribbean.com) and unlock business value.
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