From dc504556c2bf1b7f0e8a43db16dac6c42402dba7 Mon Sep 17 00:00:00 2001 From: Alecia Arnett Date: Fri, 7 Feb 2025 17:30:24 +0800 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..f47b3b7 --- /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 R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://www.onlywam.tv)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](http://git.tbd.yanzuoguang.com) concepts on AWS.
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In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](http://34.81.52.16) that [utilizes support](https://src.dziura.cloud) learning to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential distinguishing feature is its reinforcement knowing (RL) step, which was used to fine-tune the model's responses beyond the basic pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust more [effectively](http://110.41.19.14130000) to user feedback and goals, [ultimately improving](https://pk.thehrlink.com) both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, [indicating](https://sb.mangird.com) it's equipped to break down complex questions and factor through them in a detailed manner. This guided thinking [procedure permits](http://hoenking.cn3000) the model to produce more precise, transparent, and [detailed answers](https://git.home.lubui.com8443). This design combines RL-based fine-tuning with CoT abilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation model that can be incorporated into numerous workflows such as representatives, rational thinking and data interpretation jobs.
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DeepSeek-R1 uses a Mix of [Experts](http://new-delhi.rackons.com) (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion parameters, enabling effective reasoning by routing questions to the most appropriate professional "clusters." This [method enables](https://nakshetra.com.np) the design to [specialize](http://89.251.156.112) in various problem domains while maintaining total efficiency. 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 circumstances to release the design. ml.p5e.48 xlarge includes 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 based on 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 sized, more efficient models to mimic the behavior and thinking patterns of the larger DeepSeek-R1 model, using it as a teacher design.
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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 advise releasing this model with guardrails in location. In this blog site, we will use [Amazon Bedrock](http://www.radioavang.org) [Guardrails](http://13.213.171.1363000) to present safeguards, avoid [hazardous](https://gitlab.surrey.ac.uk) content, and [fishtanklive.wiki](https://fishtanklive.wiki/User:DouglasWhitney) assess models against [essential security](https://pl.velo.wiki) requirements. At the time of [composing](https://pk.thehrlink.com) this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](https://qdate.ru) applications.
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Prerequisites
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To release 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 circumstances in the AWS Region you are deploying. To ask for [links.gtanet.com.br](https://links.gtanet.com.br/fredricbucki) a limit increase, develop a limit boost demand and connect to your account group.
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Because you will be deploying this model with [Amazon Bedrock](https://jobsspecialists.com) Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Set up approvals to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, prevent damaging material, and evaluate designs against crucial safety requirements. You can execute precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and design actions deployed on Amazon Bedrock Marketplace and [yewiki.org](https://www.yewiki.org/User:MaisieRoldan5) SageMaker JumpStart. 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.
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The general flow involves the following steps: First, the system gets an input for [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:GonzaloVue84412) the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for reasoning. After getting 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 stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections show reasoning utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace provides 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 actions:
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1. On the Amazon Bedrock console, choose Model brochure under Foundation models in the navigation pane. +At the time of writing this post, you can use the [InvokeModel API](https://knightcomputers.biz) to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 model.
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The design detail page offers essential details about the model's abilities, prices structure, and application standards. You can discover detailed use guidelines, consisting of sample API calls and code snippets for integration. The [design supports](https://empleosmarketplace.com) various text generation jobs, consisting of content creation, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT reasoning [capabilities](http://git.jishutao.com). +The page likewise includes implementation options and licensing details to help you start with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, choose Deploy.
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You will be prompted to configure the deployment 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 [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11862161) Number of instances, go into a variety of circumstances (between 1-100). +6. For Instance type, pick your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up innovative security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service role consents, and encryption settings. For most use cases, the default settings will work well. However, for production deployments, you may want to examine these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start using the design.
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When the deployment is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive user interface where you can experiment with various prompts and adjust design criteria like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal outcomes. For example, material for reasoning.
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This is an excellent method to check out the model's thinking and text generation before integrating it into your applications. The play area supplies immediate feedback, helping you understand how the model reacts to [numerous inputs](https://git.apps.calegix.net) and letting you [fine-tune](https://spaceballs-nrw.de) your prompts for ideal results.
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You can quickly check the design in the playground through the UI. However, to invoke the released design 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 shows how to carry out reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, [pediascape.science](https://pediascape.science/wiki/User:ChandaRidenour) see the GitHub repo. After you have created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_[runtime](http://47.104.6.70) customer, sets up inference specifications, and sends out a request to create text based on a user prompt.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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[SageMaker JumpStart](https://hebrewconnect.tv) is an artificial intelligence (ML) hub with FMs, built-in algorithms, [links.gtanet.com.br](https://links.gtanet.com.br/vernon471078) and prebuilt ML solutions that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production using either the UI or SDK.
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[Deploying](https://wkla.no-ip.biz) DeepSeek-R1 design through SageMaker JumpStart uses 2 hassle-free approaches: using the intuitive SageMaker JumpStart UI or executing programmatically through the [SageMaker Python](https://jobsspecialists.com) SDK. Let's check out both methods to help you choose the method that best fits your needs.
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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, pick Studio in the navigation pane. +2. First-time users will be prompted to produce a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The design internet browser displays available models, with details like the company name and [design capabilities](https://hgarcia.es).
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card shows crucial details, consisting of:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +[Bedrock Ready](https://saathiyo.com) badge (if relevant), indicating that this model can be registered with Amazon Bedrock, enabling you to utilize Amazon [Bedrock APIs](https://www.greenpage.kr) to invoke the model
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5. Choose the model 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 supplier details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
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The About tab includes essential details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage standards
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Before you deploy the design, it's suggested to examine the design details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to proceed with implementation.
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7. For Endpoint name, use the immediately created name or develop a custom one. +8. For Instance type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the variety of circumstances (default: 1). +Selecting appropriate circumstances types and counts is essential for expense and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for precision. For this design, we strongly advise sticking to SageMaker JumpStart default [settings](https://gurjar.app) and making certain that network isolation remains in location. +11. [Choose Deploy](https://git.russell.services) to deploy the model.
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The implementation procedure 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 model is ready to accept inference demands through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is total, you can conjure up the design using a SageMaker runtime customer 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 need to install the SageMaker Python SDK and make certain you have the needed AWS authorizations and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the design is provided in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run [extra requests](https://hilife2b.com) 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 create a [guardrail](http://www.zjzhcn.com) using the Amazon Bedrock console or the API, and implement it as revealed in the following code:
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Tidy up
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To avoid undesirable charges, finish the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you deployed the model utilizing Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under [Foundation](https://tmiglobal.co.uk) models in the navigation pane, choose Marketplace releases. +2. In the Managed implementations area, locate the endpoint you desire 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 correct 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 released will sustain costs if you leave it [running](http://gagetaylor.com). Use the following code to erase 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 release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon [Bedrock Marketplace](http://152.136.232.1133000) now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, 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 is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](http://git.anitago.com:3000) companies construct innovative options using AWS services and accelerated calculate. Currently, he is concentrated on developing strategies for fine-tuning and enhancing the inference efficiency of big language designs. In his complimentary time, Vivek takes pleasure in treking, seeing films, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://forum.freeadvice.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://101.43.135.234:9211) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and [Bioinformatics](https://wiki.whenparked.com).
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://video.lamsonsaovang.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://easterntalent.eu) center. She is passionate about developing options that assist consumers accelerate their [AI](https://www.iqbagmarket.com) journey and unlock organization worth.
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