From 07bf329a5a3f99e38ebdffab7b0f8cf0f8569bcc Mon Sep 17 00:00:00 2001 From: Alanna Haywood Date: Mon, 17 Feb 2025 15:01:22 +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..db7aa0c --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:AnnabelleV59) you can now [deploy DeepSeek](https://gitea.oo.co.rs) [AI](https://islamichistory.tv)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative [AI](http://lyo.kr) 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](http://109.195.52.923000). You can follow comparable steps to deploy the distilled variations of the designs 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://kanghexin.work:3000) that uses reinforcement learning to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial identifying feature is its support knowing (RL) action, which was utilized to improve the model's actions beyond the standard pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:BritneyVivier19) implying it's geared up to break down complicated questions and factor through them in a [detailed manner](https://youarealways.online). This guided thinking procedure permits the design to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually caught the market's attention as a versatile text-generation model that can be incorporated into various workflows such as agents, logical thinking and data analysis jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) [architecture](http://gamebizdev.ru) and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient inference by routing inquiries to the most pertinent specialist "clusters." This approach allows the model to focus on various [issue domains](https://git.pleasantprogrammer.com) while maintaining overall effectiveness. 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 model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more effective architectures based on [popular](https://mobishorts.com) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more efficient designs to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as an instructor 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 location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful content, and assess models against crucial security requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety [controls](http://parasite.kicks-ass.org3000) across your generative [AI](http://39.98.253.192:3000) applications.
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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, choose 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 a limitation boost, a limit increase request and reach out to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For instructions, see [Establish approvals](http://47.109.24.444747) to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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[Amazon Bedrock](https://bestwork.id) Guardrails enables you to introduce safeguards, avoid damaging content, and evaluate designs against crucial safety requirements. You can execute security procedures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and design actions 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 create the guardrail, see the GitHub repo.
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The basic flow includes 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 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 [outcome](https://edtech.wiki). However, if either the input or output is stepped in by the guardrail, a [message](https://miderde.de) is [returned indicating](https://writerunblocks.com) the nature of the intervention and whether it took place at the input or output stage. The [examples showcased](https://nakenterprisetv.com) in the following sections demonstrate reasoning utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon [Bedrock Marketplace](http://dchain-d.com3000) provides you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
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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 utilize the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for [DeepSeek](http://engineerring.net) as a company and pick the DeepSeek-R1 model.
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The model detail page provides important details about the design's capabilities, prices structure, and implementation guidelines. You can find detailed use directions, consisting of sample API calls and code snippets for combination. The design supports numerous text generation tasks, consisting of content production, code generation, and concern answering, using its reinforcement discovering optimization and CoT thinking abilities. +The page also consists of deployment alternatives and licensing details to help you get begun with DeepSeek-R1 in your applications. +3. To begin [utilizing](https://git.fandiyuan.com) DeepSeek-R1, select Deploy.
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You will be triggered to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Number of instances, get in a variety of circumstances (between 1-100). +6. For example type, select your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, service function approvals, and file encryption [settings](https://ssconsultancy.in). For a lot of utilize cases, the default settings will work well. However, for production deployments, you may wish to review these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start [utilizing](http://connect.lankung.com) the model.
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When the deployment is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive interface where you can experiment with different triggers and adjust model parameters like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal results. For example, content for reasoning.
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This is an [outstanding method](http://128.199.175.1529000) to explore the model's reasoning and text generation abilities before integrating it into your applications. The play ground provides instant feedback, assisting you comprehend how the design reacts to various inputs and letting you fine-tune your triggers for ideal results.
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You can rapidly check the design in the playground 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 inference utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to [execute guardrails](http://bhnrecruiter.com). The script initializes the bedrock_runtime customer, [configures inference](http://video.firstkick.live) criteria, and sends a demand to [generate text](http://1688dome.com) based upon a user prompt.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an [artificial intelligence](http://8.134.253.2218088) (ML) center with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs 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 design through SageMaker JumpStart uses two hassle-free techniques: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both methods to assist you pick the approach that finest matches your requirements.
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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, select Studio in the navigation pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model browser displays available models, with details like the provider name and design abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card reveals key details, including:
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[- Model](https://reckoningz.com) name +- Provider name +- [Task classification](http://git.zhiweisz.cn3000) (for instance, Text Generation). +Bedrock Ready badge (if suitable), showing that this model can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the design card to view the design details page.
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The model details page consists of the following details:
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- The design name and company details. +Deploy button to release the model. +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 specifications. +[- Usage](http://funnydollar.ru) guidelines
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Before you deploy the design, it's suggested to evaluate the model details and license terms to validate compatibility with your use case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, use the immediately created name or [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:EMKMichaela) produce a customized one. +8. For example type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the variety of instances (default: 1). +Selecting suitable circumstances types and counts is crucial for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is selected by [default](https://git.isatho.me). This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this design, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. [Choose Deploy](https://titikaka.unap.edu.pe) to deploy the model.
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The deployment process can take a number of minutes to complete.
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When release is total, your endpoint status will change to InService. At this point, the design is ready to [accept reasoning](https://git.j4nis05.ch) requests through the endpoint. You can keep an eye on the release development on the [SageMaker](https://git.pleasantprogrammer.com) console Endpoints page, which will display pertinent metrics and status details. When the implementation is complete, you can invoke the model utilizing a SageMaker runtime customer and [integrate](https://gamehiker.com) it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS approvals and environment setup. The following is a [detailed](http://47.100.3.2093000) code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the design is provided in the Github here. You can clone the notebook and run 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 create a guardrail using 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 prevent undesirable charges, finish the steps in this section to tidy up your resources.
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Delete the Amazon Bedrock [Marketplace](https://prime-jobs.ch) release
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If you [deployed](https://www.h0sting.org) the model using Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments. +2. In the Managed implementations section, [surgiteams.com](https://surgiteams.com/index.php/User:AlexandraPuglies) find the endpoint you want to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the proper 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 deployed 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 release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions [Architect](http://gitea.zyimm.com) for Inference at AWS. He helps emerging generative [AI](https://git.sommerschein.de) business develop ingenious solutions using AWS services and accelerated calculate. Currently, he is focused on establishing techniques for fine-tuning and optimizing the reasoning efficiency of big language models. In his spare time, Vivek enjoys treking, enjoying movies, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://truthbook.social) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://www.nenboy.com:29283) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](http://47.104.65.214:19206) with the Third-Party Model Science group at AWS.
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[Banu Nagasundaram](https://www.oscommerce.com) leads item, engineering, and [tactical collaborations](https://insta.kptain.com) for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://valueadd.kr) hub. She is passionate about developing options that assist clients accelerate their [AI](https://notitia.tv) journey and unlock business worth.
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