commit 70d2d4964447821118b2ed6c4714865efb294498 Author: cynthiatopp427 Date: Wed Feb 19 21:19:22 2025 +0000 Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart 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..fec89c2 --- /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](http://81.68.246.1736680) R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now [deploy DeepSeek](https://pioneercampus.ac.in) [AI](https://awaz.cc)['s first-generation](https://service.aicloud.fit50443) frontier design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion parameters to construct, experiment, and properly scale your generative [AI](https://videoflixr.com) concepts on AWS.
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In this post, we show how to start with DeepSeek-R1 on [Amazon Bedrock](https://git.i2edu.net) Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy 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 [AI](https://bizad.io) that utilizes support learning to enhance reasoning abilities through a [multi-stage training](http://jobasjob.com) process from a DeepSeek-V3-Base foundation. An [essential identifying](https://stroijobs.com) feature is its reinforcement learning (RL) action, which was used to refine the model's actions beyond the standard pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, eventually enhancing both [significance](https://platform.giftedsoulsent.com) and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, meaning it's equipped to break down complicated inquiries and reason through them in a detailed manner. This directed thinking process allows the model to produce more precise, [wiki.whenparked.com](https://wiki.whenparked.com/User:JZKMireya164733) transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has captured the [industry's attention](https://gitlab.dev.cpscz.site) as a flexible text-generation model that can be incorporated into different workflows such as representatives, logical reasoning 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 parameters, enabling efficient reasoning by routing queries to the most appropriate professional "clusters." This method enables the model to focus on various problem domains while maintaining overall efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more efficient architectures based upon [popular](http://39.101.179.1066440) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective models to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 design, using it as an [instructor model](https://ubereducation.co.uk).
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an [emerging](https://freeads.cloud) model, we suggest releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid damaging content, and assess models against key security requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](https://skylockr.app) applications.
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Prerequisites
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To [release](https://pennswoodsclassifieds.com) the DeepSeek-R1 design, you require access to an ml.p5e [circumstances](https://africasfaces.com). To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and [photorum.eclat-mauve.fr](http://photorum.eclat-mauve.fr/profile.php?id=254962) confirm you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To request a limitation increase, produce a limitation boost request and [wiki.whenparked.com](https://wiki.whenparked.com/User:KathleneMelville) connect to your account team.
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Because you will be releasing 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](https://followgrown.com). For instructions, see Establish consents to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, avoid harmful content, and examine models against essential security requirements. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock [ApplyGuardrail](https://git.becks-web.de) API. This enables you to apply guardrails to evaluate user inputs and design reactions released on [Amazon Bedrock](http://fatims.org) Marketplace and SageMaker JumpStart. 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](http://gogs.kuaihuoyun.com3000).
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The general flow includes the following steps: First, the system gets an input for the design. This input is then [processed](http://test.wefanbot.com3000) through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it took place 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 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 catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and select the DeepSeek-R1 model.
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The model detail page offers vital details about the design's capabilities, pricing structure, and implementation standards. You can discover detailed use guidelines, consisting of sample API calls and code bits for integration. The model supports different text generation tasks, including material development, code generation, and question answering, utilizing its support finding out optimization and CoT reasoning capabilities. +The page likewise consists of implementation options 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 prompted to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, go into a number of instances (in between 1-100). +6. For example type, select your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up innovative security and facilities settings, consisting of virtual private cloud (VPC) networking, service role approvals, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you may wish to evaluate these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the deployment is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play area to access an interactive user interface where you can experiment with various triggers and change model specifications like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For instance, material for reasoning.
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This is an outstanding method to check out the design's reasoning and text generation capabilities before incorporating it into your applications. The [playground](https://noarjobs.info) offers immediate feedback, assisting you [comprehend](https://www.jaitun.com) how the design reacts to numerous inputs and letting you fine-tune your triggers for [optimal](http://121.28.134.382039) results.
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You can quickly check the model in the play ground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need 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 demonstrates how to carry out inference using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock [console](https://careerportals.co.za) or the API. For the example code to create the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, configures inference 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 (ML) hub with FMs, built-in algorithms, and prebuilt ML options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/chantedarbon) with your information, and release them into production utilizing either the UI or SDK.
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[Deploying](http://vivefive.sakura.ne.jp) DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient techniques: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you select the method that finest 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 utilizing SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the [navigation](https://www.opad.biz) pane.
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The design web browser shows available models, with details like the service provider name and model capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each [design card](https://www.com.listatto.ca) reveals key details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if suitable), suggesting that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the design card to view the model details page.
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The design details page includes the following details:
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- The model name and service provider details. +Deploy button to release 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](https://tiktack.socialkhaleel.com). +- License details. +- Technical specs. +- Usage standards
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Before you release the model, it's recommended to examine the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to [continue](https://customerscomm.com) with implementation.
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7. For Endpoint name, use the automatically generated name or create a custom-made 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 circumstances (default: 1). +Selecting appropriate instance types and counts is crucial for expense and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for precision. For this model, we highly advise [adhering](https://git.liubin.name) to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the design.
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The deployment procedure can take a number of minutes to complete.
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When release is complete, your endpoint status will change to InService. At this point, the design is all set to accept inference demands through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is complete, you can invoke the [model utilizing](https://calamitylane.com) a SageMaker runtime client and incorporate it with your applications.
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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 require to install the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that shows how to release 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 run from SageMaker Studio.
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You can run extra 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 produce a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Tidy up
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To avoid undesirable charges, finish the steps in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the model using Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace [deployments](https://git.goatwu.com). +2. In the Managed releases area, find the endpoint you desire 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 deployment: 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. Use the following code to delete the endpoint if you wish to stop sustaining charges. For more details, [surgiteams.com](https://surgiteams.com/index.php/User:AlexandraPuglies) 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 utilizing 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 models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker .
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About the Authors
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Vivek Gangasani is a [Lead Specialist](https://git.jordanbray.com) Solutions Architect for Inference at AWS. He helps emerging generative [AI](http://47.104.246.16:31080) business build innovative options using AWS services and accelerated calculate. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the reasoning efficiency of large language models. In his downtime, Vivek enjoys treking, watching movies, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.9iii9.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://git.vimer.top:3000) [accelerators](https://git.uzavr.ru) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://reeltalent.gr) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://eduberkah.disdikkalteng.id) hub. She is enthusiastic about constructing options that help customers accelerate their [AI](http://www.chinajobbox.com) journey and unlock organization worth.
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