From 479f42e0445174f20dae2c299f446b67aa757713 Mon Sep 17 00:00:00 2001 From: addieodriscoll Date: Mon, 7 Apr 2025 22:03:29 +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..7481394 --- /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 are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://udyogseba.com)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion criteria to construct, experiment, and properly scale your generative [AI](https://demo.titikkata.id) 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 as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](https://git.xutils.co) that uses reinforcement finding out to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key distinguishing function is its support knowing (RL) step, which was used to improve the design's reactions beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately improving both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, indicating it's equipped to break down complex queries and factor through them in a detailed manner. This assisted reasoning procedure allows the design to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation design that can be incorporated into various workflows such as representatives, logical thinking and information analysis tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows [activation](https://gitea.scalz.cloud) of 37 billion parameters, enabling efficient reasoning by routing inquiries to the most appropriate specialist "clusters." This method allows the design to focus on various issue domains while maintaining total efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. 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 thinking [abilities](https://gitea.robertops.com) of the main R1 design to more [efficient architectures](https://yaseen.tv) based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more efficient models to simulate the habits and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher model.
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You can [release](https://git.ddswd.de) DeepSeek-R1 model either through SageMaker JumpStart or [Bedrock Marketplace](http://funnydollar.ru). Because DeepSeek-R1 is an emerging model, we recommend deploying this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and evaluate designs against key security criteria. At the time of [composing](https://bikapsul.com) this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, enhancing user [experiences](https://haloentertainmentnetwork.com) and [oeclub.org](https://oeclub.org/index.php/User:MerryGaw187772) standardizing safety controls throughout your [generative](http://121.37.208.1923000) [AI](https://linked.aub.edu.lb) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and validate you're using 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 a limitation increase, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:MitziCarandini3) create a limit increase request and connect to your [account team](https://gitlab.informicus.ru).
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Because you will be deploying this design with [Amazon Bedrock](https://alumni.myra.ac.in) Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. 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 introduce safeguards, prevent harmful material, and evaluate designs against essential safety requirements. You can implement safety procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and model 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 produce the guardrail, see the GitHub repo.
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The basic circulation involves the following steps: First, the system receives an input for the model. 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 receiving the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. 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 phase. The examples showcased in the following sections show inference 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, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:NorbertoGuess) 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, select Model brochure under Foundation models in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.
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The model detail page provides essential details about the design's abilities, pricing structure, and execution guidelines. You can find detailed usage instructions, including sample API calls and code bits for integration. The model supports different text generation tasks, [consisting](https://git.agent-based.cn) of content production, code generation, and question answering, using its support learning optimization and CoT thinking abilities. +The page also includes release alternatives and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, select Deploy.
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You will be prompted to configure the release 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, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:Curtis3026) get in a variety of instances (between 1-100). +6. For Instance type, select your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can set up innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service function approvals, and encryption settings. For most use cases, the default settings will work well. However, for production implementations, you might wish to review these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start using the design.
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When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive interface where you can explore different prompts and adjust model criteria like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For instance, content for inference.
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This is an outstanding way to explore the design's reasoning and text generation abilities before incorporating it into your applications. The playground supplies instant feedback, helping you [comprehend](https://mediawiki1334.00web.net) how the model responds to numerous inputs and letting you fine-tune your prompts for optimal outcomes.
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You can rapidly check the design in the play area through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the [endpoint](https://www.earnwithmj.com) 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 perform inference using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock [console](https://repo.farce.de) or the API. For the example code to develop the guardrail, see the [GitHub repo](https://thestylehitch.com). After you have developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference criteria, and sends a request to produce text based on a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML services that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 [hassle-free](http://122.51.6.973000) methods: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the [SageMaker Python](https://chancefinders.com) SDK. Let's check out both [methods](https://www.genbecle.com) to help you choose the approach that finest suits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the [SageMaker](http://51.15.222.43) console, select Studio in the navigation pane. +2. First-time users will be triggered to [produce](http://122.51.230.863000) a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design internet browser shows available models, with details like the service provider name and design capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card reveals key details, consisting of:
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- Model name +- [Provider](https://demo.theme-sky.com) name +- Task [classification](http://stackhub.co.kr) (for example, Text Generation). +Bedrock Ready badge (if suitable), [indicating](https://abcdsuppermarket.com) that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the design
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5. Choose the model card to see the design details page.
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The design details page consists of the following details:
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- The model name and supplier details. +Deploy button to release the model. +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 specs. +- Usage guidelines
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Before you release the design, it's advised to evaluate the [design details](https://git.gday.express) and license terms to confirm compatibility with your use case.
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6. Choose Deploy to proceed with implementation.
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7. For Endpoint name, use the automatically generated name or create a custom-made one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial [instance](https://chumcity.xyz) count, get in the variety of circumstances (default: 1). +Selecting proper circumstances types and counts is crucial for expense and performance optimization. Monitor your release 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 setups for accuracy. For this model, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to release the model.
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The deployment procedure can take several minutes to finish.
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When implementation is total, your endpoint status will change to InService. At this moment, the model is prepared to accept reasoning [requests](https://git.youxiner.com) through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is complete, you can invoke the design utilizing a [SageMaker runtime](https://www.trueposter.com) 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 begun with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS consents and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the design is provided in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run additional requests against the predictor:
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Implement guardrails and run reasoning with your [SageMaker JumpStart](https://demo.playtubescript.com) predictor
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Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
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Tidy up
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To avoid undesirable charges, finish the actions 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 design utilizing Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases. +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 design you released will sustain costs if you leave it running. Use the following code to delete the [endpoint](https://wiki.cemu.info) if you desire to stop sustaining charges. For more details, see Delete Endpoints and [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:RalfLuna002) 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 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 Getting going 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](https://arthurwiki.com) business build innovative services utilizing AWS services and accelerated calculate. Currently, he is concentrated on developing techniques for fine-tuning and optimizing the reasoning performance of large language models. In his spare time, Vivek takes pleasure in hiking, seeing movies, and attempting different [cuisines](http://39.108.93.0).
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Niithiyn Vijeaswaran is a Generative [AI](http://203.171.20.94:3000) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://wino.org.pl) 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](https://lovelynarratives.com) dealing with [generative](http://47.114.82.1623000) [AI](http://103.242.56.35:10080) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.eisenwiener.com) center. She is passionate about building solutions that assist clients accelerate their [AI](http://huaang6688.gnway.cc:3000) journey and unlock business value.
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