From f0c8989b1cd90e4112308ee60746efdd31df23e4 Mon Sep 17 00:00:00 2001 From: Amparo Nickel Date: Fri, 21 Feb 2025 17:51:37 -0500 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..972fed8 --- /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 reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://git.e365-cloud.com)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative [AI](https://www.findnaukri.pk) concepts on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the designs also.
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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://careers.webdschool.com) that uses reinforcement learning to improve thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential distinguishing function is its support learning (RL) step, which was utilized to fine-tune the design's reactions beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, ultimately boosting both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, indicating it's geared up to break down complicated inquiries and factor through them in a detailed manner. This assisted reasoning procedure permits the model to produce more accurate, transparent, and detailed answers. This [design integrates](https://gitea.egyweb.se) RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually captured the market's attention as a [flexible](https://adremcareers.com) text-generation model that can be integrated into various workflows such as representatives, sensible thinking and information interpretation tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture [permits](http://jatushome.myqnapcloud.com8090) activation of 37 billion criteria, allowing efficient reasoning by routing questions to the most appropriate specialist "clusters." This technique enables the design to concentrate on different issue domains while maintaining general 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 instance to deploy the model. 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 reasoning capabilities of the main R1 model to more [efficient architectures](http://www.shopmento.net) based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective designs to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor model.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock [Marketplace](https://wikitravel.org). Because DeepSeek-R1 is an emerging model, we advise releasing this design with guardrails in location. In this blog, we will use [Amazon Bedrock](https://younivix.com) [Guardrails](http://47.93.156.1927006) to introduce safeguards, prevent hazardous material, and assess models against key security criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, [Bedrock Guardrails](https://www.pakgovtnaukri.pk) supports just the ApplyGuardrail API. You can develop numerous guardrails [tailored](https://clinicial.co.uk) to different usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative [AI](http://47.101.131.235:3000) [applications](http://175.178.71.893000).
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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, select Amazon SageMaker, and verify you're utilizing 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 deploying. To request a [limitation](http://47.99.119.17313000) increase, develop a limit increase request and reach out to your account group.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For [yewiki.org](https://www.yewiki.org/User:WinifredHassell) instructions, see Set up authorizations to use guardrails for material filtering.
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Implementing guardrails with the [ApplyGuardrail](http://xunzhishimin.site3000) API
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Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging content, and evaluate models against crucial security criteria. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and [design responses](https://www.diltexbrands.com) 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 develop the guardrail, see the GitHub repo.
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The basic circulation [involves](https://propbuysells.com) the following steps: First, the system gets 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 model for inference. After getting the design's output, another guardrail check is used. If the [output passes](https://empleos.dilimport.com) this final check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas show reasoning utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon [Bedrock Marketplace](http://koceco.co.kr) gives you access to over 100 popular, emerging, and specialized foundation models (FMs) through [Amazon Bedrock](https://akinsemployment.ca). 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 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](https://repo.amhost.net). +2. Filter for DeepSeek as a company and choose the DeepSeek-R1 model.
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The model detail page provides essential details about the model's capabilities, prices structure, and execution standards. You can find detailed usage instructions, consisting of sample API calls and code bits for combination. The model supports numerous text [generation](https://express-work.com) tasks, including content creation, code generation, and question answering, using its reinforcement discovering optimization and CoT reasoning capabilities. +The page likewise consists of deployment options and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
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You will be triggered to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, get in a number of instances (in between 1-100). +6. For example type, select your circumstances type. For optimal efficiency with DeepSeek-R1, a [GPU-based instance](http://bhnrecruiter.com) type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service function authorizations, and encryption settings. For the majority of utilize cases, the default settings will work well. However, for [production](https://wiki.communitydata.science) releases, you might wish to examine these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start using the design.
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When the implementation is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive interface where you can explore various prompts and change design parameters like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum results. For example, material 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 play ground supplies instant feedback, helping you comprehend how the model reacts to different inputs and letting you fine-tune your triggers for optimal outcomes.
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You can rapidly check the design in the play ground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out reasoning utilizing a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock [console](https://mulaybusiness.com) or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference criteria, and sends a demand to generate 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 solutions that you can [release](http://xunzhishimin.site3000) with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production using either the UI or SDK.
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[Deploying](http://wiki.faramirfiction.com) DeepSeek-R1 design through SageMaker JumpStart uses two hassle-free methods: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's [explore](https://i10audio.com) both approaches to assist you choose the approach that finest matches your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the [navigation](http://carpetube.com) 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 shows available designs, with details like the provider name and design abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each [design card](https://git.trov.ar) reveals key details, consisting of:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if applicable), showing that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the design card to see the design details page.
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The model details page includes the following details:
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- The model name and provider details. +Deploy button 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 specs. +- Usage guidelines
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Before you the model, it's advised to examine the model details and license terms to validate compatibility with your use case.
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6. Choose Deploy to proceed with implementation.
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7. For Endpoint name, utilize the automatically produced name or produce a customized one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the variety of instances (default: 1). +Selecting proper [instance types](https://www.luckysalesinc.com) and counts is vital for expense and performance optimization. Monitor your implementation to adjust these [settings](https://git.mikecoles.us) as needed.Under Inference type, [Real-time reasoning](https://www.guidancetaxdebt.com) is picked by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that [network seclusion](https://zidra.ru) remains in place. +11. Choose Deploy to deploy the design.
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The release procedure can take several minutes to complete.
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When implementation is complete, your endpoint status will change to InService. At this point, the model is all set to accept inference requests through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will show appropriate 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.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the required AWS authorizations and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for [inference programmatically](https://www.friend007.com). The code for releasing the design is [offered](http://www.fasteap.cn3000) in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run additional 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create 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 prevent undesirable charges, finish the steps in this area to tidy up your resources.
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Delete the Amazon Bedrock [Marketplace](https://lidoo.com.br) release
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If you [deployed](http://5.34.202.1993000) the model using Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace implementations. +2. In the Managed releases area, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the correct implementation: 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 model you released will sustain expenses if you leave it [running](http://202.90.141.173000). Use the following code to erase 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 explored how you can access and deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, 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 is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://members.mcafeeinstitute.com) business build ingenious services utilizing AWS services and sped up calculate. Currently, he is focused on developing techniques for fine-tuning and optimizing the inference performance of large language designs. In his complimentary time, Vivek delights in treking, viewing films, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://47.110.52.132:3000) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://forum.elaivizh.eu) 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](http://59.56.92.34:13000) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://gitlab.interjinn.com) hub. She is enthusiastic about [building options](https://git.pyme.io) that assist customers accelerate their [AI](http://8.134.61.107:3000) journey and unlock service value.
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