From 4d318d72053d0a8801e211b3d2837b27e05278f9 Mon Sep 17 00:00:00 2001 From: barbaracantero Date: Thu, 27 Feb 2025 01:12:29 +0000 Subject: [PATCH] Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-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..46c6919 --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, we are delighted to announce that [DeepSeek](http://120.79.27.2323000) 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://rubius-qa-course.northeurope.cloudapp.azure.com)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative [AI](https://www.friend007.com) 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 comparable steps to release the [distilled versions](https://twitemedia.com) of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://jollyday.club) that uses reinforcement finding out to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3[-Base foundation](http://122.51.17.902000). A key identifying function is its reinforcement learning (RL) action, which was used to refine the design's reactions beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually improving both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, suggesting it's equipped to break down intricate questions and factor through them in a detailed manner. This assisted thinking procedure permits the model to produce more precise, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:KarolynShanahan) transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has captured the market's attention as a flexible text-generation design that can be incorporated into various workflows such as agents, [rational reasoning](https://app.theremoteinternship.com) and data interpretation jobs.
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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 criteria, enabling effective inference by routing questions to the most relevant professional "clusters." This approach permits the design to focus on different issue domains while maintaining total performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the [thinking capabilities](https://syndromez.ai) of the main R1 design to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, [disgaeawiki.info](https://disgaeawiki.info/index.php/User:CharityGunderson) 14B, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1105855) and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective models to the habits and thinking patterns of the larger DeepSeek-R1 model, utilizing it as a teacher design.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and assess designs against crucial security 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 several guardrails tailored to different use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](https://git.programming.dev) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you need access to an ml.p5e [circumstances](http://nas.killf.info9966). To check if you have quotas for P5e, open the Service Quotas console and [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2774581) under AWS Services, select Amazon SageMaker, and verify you're utilizing 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 request a limit boost, produce a [limit increase](http://154.209.4.103001) demand and reach out to your account team.
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Because you will be [deploying](https://idaivelai.com) this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For instructions, see Establish approvals to use guardrails for [material filtering](http://kodkod.kr).
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to present safeguards, avoid damaging material, and assess models against crucial security requirements. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock [ApplyGuardrail](https://grailinsurance.co.ke) API. This enables you to use guardrails to assess user inputs and design reactions released on Amazon Bedrock [Marketplace](https://geniusactionblueprint.com) and SageMaker JumpStart. You can produce a [guardrail](http://120.78.74.943000) using the [Amazon Bedrock](http://nas.killf.info9966) [console](http://8.137.54.2139000) 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 design. This input is then processed through the [ApplyGuardrail API](https://git.yingcaibx.com). If the [input passes](https://git.yingcaibx.com) the guardrail check, it's sent out to the design for reasoning. After getting the design's output, another [guardrail check](https://gitea.cronin.one) is used. If the [output passes](https://sea-crew.ru) 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 suggesting the nature of the [intervention](https://samisg.eu8443) and whether it took place at the input or output phase. The examples showcased in the following sections show inference utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon [Bedrock Marketplace](http://caxapok.space) offers 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, choose Model catalog under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to invoke the model. It does not support Converse APIs and other [Amazon Bedrock](https://thesecurityexchange.com) tooling. +2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 design.
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The design detail page provides vital details about the design's abilities, pricing structure, and execution standards. You can discover detailed usage guidelines, including sample API calls and code snippets for [combination](https://gitlabdemo.zhongliangong.com). The design supports various text generation jobs, consisting of content production, code generation, and concern answering, using its support learning optimization and CoT reasoning abilities. +The page also consists of deployment alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, pick Deploy.
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You will be triggered to configure the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, enter a variety of circumstances (in between 1-100). +6. For Instance type, pick your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can [configure innovative](https://followingbook.com) security and facilities settings, including virtual personal cloud (VPC) networking, service function authorizations, and encryption settings. For many use cases, the default settings will work well. However, for production deployments, you may want to review 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 implementation is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive interface where you can explore different prompts and adjust design parameters like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For instance, content for inference.
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This is an outstanding method to explore the model's thinking and text generation capabilities before incorporating it into your applications. The playground supplies immediate feedback, assisting you comprehend how the [model reacts](https://tiptopface.com) to various inputs and letting you tweak your triggers for [optimal](https://mzceo.net) results.
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You can [rapidly evaluate](http://ods.ranker.pub) the model in the play area 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 using guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to carry out 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 produce the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up [reasoning](http://grainfather.co.uk) criteria, and sends a demand 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](https://gitea.cronin.one) (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can release with simply 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 using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 practical techniques: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you select the method that best fits 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 utilizing SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. [First-time](https://phones2gadgets.co.uk) users will be triggered to [develop](https://voggisper.com) a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model internet browser shows available models, with details like the provider name and design abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card shows key details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if relevant), [suggesting](https://git.dev.advichcloud.com) that this model can be registered with Amazon Bedrock, permitting you to utilize Amazon [Bedrock](http://101.200.241.63000) APIs to conjure up the model
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5. Choose the design 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 provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab includes crucial 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 examine the model details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, use the instantly created name or create a custom one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the variety of circumstances (default: 1). +Selecting suitable instance types and counts is crucial for expense and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency. +10. Review all setups for accuracy. 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 release the model.
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The release process can take several minutes to complete.
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When deployment is complete, your endpoint status will alter to InService. At this moment, the model is ready to accept inference requests through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is complete, you can conjure up the design utilizing a SageMaker runtime customer and integrate it with your [applications](https://cvbankye.com).
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker [Python SDK](https://rocksoff.org) and make certain you have the needed AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run extra requests 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing 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 avoid unwanted charges, complete the actions in this area 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 actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace deployments. +2. In the Managed deployments 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 erasing 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 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 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock [Marketplace](https://git.xinstitute.org.cn) now to get started. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, [Amazon SageMaker](http://upleta.rackons.com) 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](https://happylife1004.co.kr) for Inference at AWS. He helps emerging generative [AI](https://guyanajob.com) business develop ingenious options utilizing AWS services and [wiki.myamens.com](http://wiki.myamens.com/index.php/User:RoyceE3132) sped up [calculate](https://jobs.sudburychamber.ca). Currently, he is concentrated on developing methods for fine-tuning and enhancing the reasoning performance of large language models. In his downtime, Vivek enjoys hiking, watching movies, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://bizad.io) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://git.joystreamstats.live) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](http://git.acdts.top:3000) 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 [AI](https://git.ipmake.me) hub. She is enthusiastic about developing solutions that help clients accelerate their [AI](https://bizad.io) journey and unlock service value.
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