From 1922d8f3bf4c106d90acaf86a6bca761d1e1d1cf Mon Sep 17 00:00:00 2001 From: lowellglasheen Date: Fri, 7 Feb 2025 10:18:29 +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..61a4da1 --- /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 models](https://findgovtsjob.com) are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://124.129.32.66:3000)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative [AI](https://pakkalljob.com) [concepts](https://betalk.in.th) 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 versions of the models also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://edtech.wiki) that utilizes support [learning](https://www.keeloke.com) to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial differentiating function is its support learning (RL) step, which was used to fine-tune the model's reactions beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, meaning it's equipped to break down complex queries and reason through them in a detailed way. This directed thinking [process](https://www.koumii.com) enables the model to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:LucasFtu80211) aiming to create structured reactions while [focusing](http://114.55.2.296010) on [interpretability](https://heli.today) and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually [captured](http://enhr.com.tr) the [industry's attention](https://www.jobs-f.com) as a versatile text-generation model that can be incorporated into different workflows such as representatives, logical thinking and [larsaluarna.se](http://www.larsaluarna.se/index.php/User:AlineCox0079049) information analysis tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion criteria, enabling efficient inference by routing queries to the most relevant specialist "clusters." This approach allows the design to focus on different issue domains while maintaining overall efficiency. DeepSeek-R1 requires a minimum of 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 features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking [abilities](https://git.zzxxxc.com) of the main R1 design to more efficient architectures based on popular open designs 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 imitate the behavior and reasoning patterns of the larger DeepSeek-R1 design, using it as a teacher design.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and evaluate models against essential security requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, [Bedrock Guardrails](https://www.highpriceddatinguk.com) supports just the ApplyGuardrail API. You can create numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative [AI](http://artpia.net) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and validate 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 releasing. To ask for a limit increase, produce a limitation increase request and 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 right AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Establish consents to utilize guardrails for content filtering.
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[Implementing guardrails](http://124.70.58.2093000) with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, avoid harmful content, and assess designs against crucial safety [requirements](https://ugit.app). You can carry out safety procedures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock [console](https://inspiredcollectors.com) 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 actions: 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, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:CynthiaCrombie) it's sent to the design for inference. After receiving the model's output, another guardrail check is used. If the output passes this last check, it's returned as the last [outcome](http://dgzyt.xyz3000). However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output stage. 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 gives you access to over 100 popular, emerging, and specialized structure designs (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, choose Model brochure under Foundation models in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [company](https://iklanbaris.id) and select the DeepSeek-R1 design.
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The design detail page provides important details about the model's capabilities, pricing structure, and implementation guidelines. You can find detailed use instructions, including sample API calls and code snippets for combination. The design supports different text generation tasks, consisting of content creation, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT reasoning abilities. +The page also consists of release choices and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, choose Deploy.
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You will be triggered to configure the implementation 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 Variety of circumstances, get in a number of instances (in between 1-100). +6. For example type, [pediascape.science](https://pediascape.science/wiki/User:ChandaRidenour) select your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up advanced security and facilities settings, including virtual personal cloud (VPC) networking, service role approvals, and encryption settings. For [pipewiki.org](https://pipewiki.org/wiki/index.php/User:MilanCastro087) most utilize cases, the default settings will work well. However, for production implementations, you may want to [examine](http://www.carnevalecommunity.it) these settings to align with your [organization's security](https://livesports808.biz) and compliance requirements. +7. Choose Deploy to start using the model.
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When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can explore various prompts and adjust model criteria like temperature level and maximum length. +When using 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 excellent way to explore the model's thinking and text generation capabilities before incorporating it into your [applications](https://thankguard.com). The play ground supplies immediate feedback, assisting you understand how the [model reacts](http://udyogservices.com) to various inputs and letting you fine-tune your prompts for ideal outcomes.
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You can rapidly evaluate the model in the play area 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 perform inference using a [deployed](https://owow.chat) DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually developed the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning 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) center with FMs, built-in algorithms, and prebuilt ML solutions that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 hassle-free techniques: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you select the method that best matches your [requirements](http://whai.space3000).
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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 triggered to develop a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The design browser displays available designs, with details like the service provider name and design abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card shows crucial details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if suitable), suggesting that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the design card to view the model 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 deploy 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 requirements. +- Usage standards
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Before you release the design, it's advised to evaluate the model details and license terms to verify 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](http://shammahglobalplacements.com) the immediately generated name or develop a customized one. +8. For Instance type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of instances (default: 1). +Selecting proper 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 picked by default. This is enhanced for sustained traffic and low latency. +10. Review all [configurations](https://dev.clikviewstorage.com) for accuracy. For this model, we highly recommend sticking to SageMaker JumpStart [default settings](https://iinnsource.com) and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the design.
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The implementation process can take a number of minutes to complete.
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When [release](https://23.23.66.84) is complete, your endpoint status will alter to InService. At this point, the model is all set to accept inference demands through the endpoint. You can keep track of the release development on the SageMaker console [Endpoints](https://gogs.koljastrohm-games.com) page, which will display relevant metrics and status details. When the deployment is complete, you can conjure up the model utilizing a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 [utilizing](https://2ubii.com) the SageMaker Python SDK
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To get started 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 consents and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for [reasoning programmatically](http://gsrl.uk). The code for deploying the model is offered in the Github here. You can clone the notebook and range from [SageMaker Studio](https://slovenskymedved.sk).
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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](https://bocaiw.in.net) predictor
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Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and [implement](http://git.ningdatech.com) it as revealed in the following code:
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Clean up
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To avoid undesirable charges, complete the actions in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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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 designs in the navigation pane, pick Marketplace deployments. +2. In the Managed releases section, locate the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, [choose Delete](https://empleos.contatech.org). +4. Verify the endpoint details to make certain you're deleting the proper deployment: 1. [Endpoint](http://hoteltechnovalley.com) 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 deployed will sustain costs if you leave it running. Use the following code to delete 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 explored how you can access and deploy the DeepSeek-R1 model using and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:IvyCano5125640) 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 helps emerging generative [AI](http://www.amrstudio.cn:33000) business construct innovative solutions using AWS services and accelerated calculate. Currently, he is focused on developing methods for fine-tuning and optimizing the inference efficiency of big language models. In his downtime, Vivek enjoys treking, enjoying movies, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.lokfuehrer-jobs.de) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His [location](https://gogs.macrotellect.com) of focus is AWS [AI](https://git.fanwikis.org) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://ouptel.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](http://udyogservices.com) center. She is passionate about building services that help customers accelerate their [AI](https://www.assistantcareer.com) journey and unlock service worth.
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