From a64c8083bccb2d923699f7d13058aa9e35b96773 Mon Sep 17 00:00:00 2001 From: leonardo53e434 Date: Mon, 7 Apr 2025 06:31:25 +0800 Subject: [PATCH] Update '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..0d418bb --- /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 announce that [DeepSeek](http://youtubeer.ru) R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://connect.taifany.com)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](http://8.139.7.166:10880) concepts on AWS.
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In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](http://git.cattech.org) that uses [support finding](https://ideezy.com) out to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential distinguishing feature is its reinforcement learning (RL) action, which was utilized to improve the model's reactions beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, ultimately improving both importance and clearness. In addition, DeepSeek-R1 employs a [chain-of-thought](http://git.cnibsp.com) (CoT) approach, implying it's [equipped](http://globalchristianjobs.com) to break down [intricate queries](http://www.zjzhcn.com) and factor through them in a detailed manner. This assisted reasoning [process](https://classtube.ru) allows the model to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation design that can be integrated into different workflows such as agents, rational reasoning and data interpretation tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The [MoE architecture](http://gitlab.fuxicarbon.com) allows activation of 37 billion parameters, allowing effective reasoning by routing questions to the most relevant specialist "clusters." This method permits the design to focus on various problem domains while maintaining overall efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient models to [imitate](https://gitlab.isc.org) the habits and [pipewiki.org](https://pipewiki.org/wiki/index.php/User:SharynAlmond5) reasoning patterns of the larger DeepSeek-R1 model, utilizing it as a teacher design.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this design with guardrails in place. In this blog, we will use Amazon Bedrock [Guardrails](https://telecomgurus.in) to present safeguards, prevent harmful material, and [examine designs](https://musixx.smart-und-nett.de) against essential security requirements. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your [generative](https://chosenflex.com) [AI](http://repo.sprinta.com.br:3000) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and [validate](https://git.the-kn.com) you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To request a limitation boost, create 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 correct AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For directions, see Set up permissions to utilize 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, avoid harmful content, and assess designs against key safety criteria. You can implement security measures for the DeepSeek-R1 model utilizing the [Amazon Bedrock](https://git.intellect-labs.com) ApplyGuardrail API. This enables you to use guardrails to examine user inputs and model reactions deployed on Amazon Bedrock Marketplace and [SageMaker JumpStart](http://git.jaxc.cn). You can create 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 general flow includes 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 to the model for [inference](http://82.157.77.1203000). After receiving the model's output, another guardrail check is used. If the output passes this last check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output stage. The [examples](https://connectzapp.com) showcased in the following sections demonstrate inference 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 foundation 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, pick Model catalog under Foundation designs in the navigation pane. +At the time of [composing](http://211.159.154.983000) this post, you can utilize the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock [tooling](https://feleempleo.es). +2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 design.
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The model detail page offers essential details about the model's capabilities, prices structure, and implementation guidelines. You can [discover](https://sabiile.com) detailed usage guidelines, consisting of sample API calls and code bits for integration. The design supports different text generation jobs, including material production, code generation, and question answering, utilizing its support learning optimization and CoT reasoning abilities. +The page also consists of release choices and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be prompted to set up 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 Variety of instances, enter a variety of instances (between 1-100). +6. For Instance type, choose your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure innovative security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service role authorizations, and encryption [settings](https://git.dev.advichcloud.com). For the [majority](https://git.wheeparam.com) of use cases, the [default settings](https://africasfaces.com) will work well. However, for production releases, you may want to review 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 play ground. +8. Choose Open in play ground to access an interactive interface where you can experiment with various triggers and change design criteria like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal outcomes. For instance, material for reasoning.
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This is an [outstanding method](https://eastcoastaudios.in) to explore the design's thinking and text generation abilities before incorporating it into your applications. The play area supplies immediate feedback, assisting you understand how the design reacts to different inputs and letting you fine-tune your triggers for optimal outcomes.
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You can rapidly evaluate the model in the playground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning using [guardrails](http://team.pocketuniversity.cn) with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out reasoning utilizing a released DeepSeek-R1 model 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 create the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends out a demand to create text based upon 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 solutions that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models 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 design through SageMaker JumpStart uses two practical approaches: using the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both methods to help you choose the approach that best matches your needs.
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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, choose Studio in the navigation pane. +2. First-time users will be to develop a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model internet browser shows available models, with details like the supplier name and design abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design card reveals key details, consisting of:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this model can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the design card to see the design details page.
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The design details page includes the following details:
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- The design name and company details. +Deploy button to deploy 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](https://git.genowisdom.cn) standards
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Before you deploy the model, it's recommended to review the design details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to proceed with implementation.
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7. For Endpoint name, utilize the instantly produced name or create a custom-made one. +8. For Instance type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the variety of circumstances (default: 1). +Selecting appropriate circumstances types and counts is crucial for expense and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the model.
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The deployment process can take several minutes to complete.
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When implementation is complete, your endpoint status will change to [InService](https://centerfairstaffing.com). At this point, the model is all set to accept inference requests through the endpoint. You can keep an eye on the release progress on the [SageMaker console](http://www.jobteck.co.in) Endpoints page, which will show relevant metrics and status details. When the deployment is complete, you can invoke the model using a SageMaker runtime customer and incorporate 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 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 deploy and use DeepSeek-R1 for inference programmatically. The code for deploying 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 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 use 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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Tidy up
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To avoid [undesirable](https://saghurojobs.com) charges, finish the steps in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you deployed the model utilizing Amazon Bedrock Marketplace, total 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 deployments section, locate the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the appropriate 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 deployed will sustain expenses if you leave it running. Use the following code to erase 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 explored 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 get begun. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, [Amazon SageMaker](https://younivix.com) JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting 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://slfood.co.kr) business construct [innovative services](https://git.cno.org.co) utilizing AWS services and accelerated compute. Currently, he is concentrated on developing techniques for fine-tuning and optimizing the reasoning efficiency of big language models. In his downtime, Vivek enjoys hiking, seeing films, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://hychinafood.edenstore.co.kr) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://xiaomaapp.top:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and [Bioinformatics](http://lyo.kr).
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](http://git.zhiweisz.cn:3000) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://39.99.158.114:10080) center. She is passionate about constructing services that help customers accelerate their [AI](https://wiki.sublab.net) journey and unlock company worth.
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