From 740f6e4fd9d96626d1b4ee2916ba0981982442ac Mon Sep 17 00:00:00 2001 From: Chantal Kellaway Date: Sat, 8 Feb 2025 22:18:44 +0800 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..1f2d926 --- /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 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 release DeepSeek [AI](http://118.89.58.19:3000)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative [AI](http://artpia.net) ideas on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled variations of the models also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) developed by [DeepSeek](https://lius.familyds.org3000) [AI](http://47.108.69.33:10888) that utilizes support finding out to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial distinguishing feature is its support learning (RL) step, which was used to [fine-tune](https://gitea.freshbrewed.science) the model's actions beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, [implying](http://125.43.68.2263001) it's geared up to break down intricate inquiries and factor through them in a detailed way. This directed thinking procedure enables the model to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation model that can be incorporated into numerous workflows such as agents, logical thinking and information interpretation tasks.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits [activation](https://munidigital.iie.cl) of 37 billion parameters, [allowing efficient](https://git.dsvision.net) inference by routing questions to the most appropriate specialist "clusters." This method enables the design to focus on various issue domains while maintaining overall performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 model to more efficient architectures based upon popular open designs 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 to mimic the behavior and thinking patterns of the larger DeepSeek-R1 design, utilizing it as an instructor model.
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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 suggest [deploying](https://giftconnect.in) this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent harmful content, and [evaluate designs](https://gitcq.cyberinner.com) against essential security requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the [ApplyGuardrail API](https://git.rggn.org). You can create multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative [AI](https://gogs.zhongzhongtech.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, [wiki-tb-service.com](http://wiki-tb-service.com/index.php?title=Benutzer:Milla01Z3855169) you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas [console](https://gitea.createk.pe) and under AWS Services, pick Amazon SageMaker, and validate you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To request a limit increase, create a limit increase demand and reach out to your account group.
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Because you will be deploying this design 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 guidelines, see Establish approvals 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 hazardous content, and examine [designs](https://tygerspace.com) against key security requirements. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a [guardrail](http://isarch.co.kr) using the Amazon Bedrock console or the API. For the example code to create the guardrail, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:Bettina5096) see the GitHub repo.
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The general [circulation](https://gitea.potatox.net) includes the following steps: 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 inference. After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the last 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 phase. The examples showcased in the following areas show reasoning using this API.
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Deploy DeepSeek-R1 in [Amazon Bedrock](https://www.wtfbellingham.com) Marketplace
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Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized structure 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, pick Model brochure under Foundation designs in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 model.
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The model detail page supplies vital details about the design's abilities, pricing structure, and implementation guidelines. You can discover detailed use directions, including sample API calls and code bits for combination. The [model supports](https://git.the.mk) different text generation jobs, including content production, code generation, and question answering, using its reinforcement finding out optimization and CoT reasoning capabilities. +The page likewise [consists](https://www.ggram.run) of release options and licensing details to help you get started with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, choose Deploy.
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You will be prompted to configure 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, go into a variety of instances (between 1-100). +6. For example type, choose your instance type. For ideal efficiency with DeepSeek-R1, a [GPU-based circumstances](https://10-4truckrecruiting.com) type like ml.p5e.48 xlarge is advised. +Optionally, you can configure advanced security and facilities settings, including virtual personal cloud (VPC) networking, service role authorizations, and encryption settings. For the majority of use cases, the default settings will work well. However, for production releases, you may wish to examine these settings to line up with your organization's security and compliance requirements. +7. [Choose Deploy](https://gitlab.bzzndata.cn) to begin using the design.
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When the release is complete, you can evaluate 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 explore various prompts and adjust model criteria like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For instance, material for reasoning.
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This is an exceptional way to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The playground offers immediate feedback, assisting you comprehend how the [design reacts](https://zamhi.net) to various inputs and [letting](https://jobz0.com) you tweak your triggers for optimal outcomes.
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You can quickly evaluate the design in the play area through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing 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](https://cbfacilitiesmanagement.ie). After you have produced the guardrail, [utilize](https://git.elder-geek.net) the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up [reasoning](https://gitea.thuispc.dynu.net) criteria, [fishtanklive.wiki](https://fishtanklive.wiki/User:KentonR156) and sends a demand to produce 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, built-in algorithms, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:KarolynShanahan) and prebuilt ML options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://executiverecruitmentltd.co.uk) models to your usage case, with your data, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 hassle-free techniques: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both [methods](https://cielexpertise.ma) 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 release DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design web browser displays available models, 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 design [card reveals](https://lius.familyds.org3000) key details, consisting of:
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- Model name +- Provider name +- Task category (for example, Text Generation). +[Bedrock Ready](https://www.ggram.run) badge (if applicable), indicating that this design can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the design
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5. Choose the model card to view the design details page.
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The [design details](https://gitea.potatox.net) page consists of the following details:
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- The model name and supplier details. +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
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The About tab includes important details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage guidelines
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Before you deploy the design, it's advised to examine the design details and license terms to validate compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, use the automatically produced name or create a customized one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the variety of circumstances (default: 1). +Selecting proper circumstances types and counts is essential for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this design, we strongly recommend sticking 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 release process can take a number of minutes to complete.
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When implementation is total, your endpoint status will alter to InService. At this moment, the model is prepared to accept reasoning requests through the [endpoint](https://bihiring.com). You can keep track of the release development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is total, you can invoke 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 get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the needed AWS permissions and environment setup. The following is a [detailed](http://internetjo.iwinv.net) code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for deploying 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 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
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Clean up
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To avoid undesirable charges, complete the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the design 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 [releases](https://demo.theme-sky.com). +2. In the Managed releases area, locate the [endpoint](https://47.100.42.7510443) you wish to erase. +3. Select the endpoint, and [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:KarissaGleason) on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting 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 design you released will sustain expenses 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 started. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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](http://49.235.101.2443001) generative [AI](https://wiki.cemu.info) business develop innovative solutions utilizing AWS services and sped up compute. Currently, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:TraceyPrell3) he is concentrated on developing methods for fine-tuning and enhancing the reasoning efficiency of large language designs. In his downtime, Vivek enjoys hiking, watching motion pictures, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://git.smartenergi.org) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://samman-co.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://medifore.co.jp) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and [strategic collaborations](https://pingpe.net) for Amazon SageMaker JumpStart, [SageMaker's](https://gitlab-zdmp.platform.zdmp.eu) artificial intelligence and generative [AI](http://dibodating.com) hub. She is enthusiastic about building options that assist clients accelerate their [AI](https://smarthr.hk) journey and unlock company worth.
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