From d82d10e3f7d5255434a536f1f17b5dc21c2c8393 Mon Sep 17 00:00:00 2001 From: halinarabinovi Date: Fri, 4 Apr 2025 21:41:48 +0200 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..d3daf98 --- /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](https://ransomware.design) to reveal that DeepSeek 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](http://47.92.109.230:8080)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative [AI](http://152.136.187.229) [concepts](https://propveda.com) 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 comparable actions to [release](https://git.alexavr.ru) the distilled variations of the designs too.
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[Overview](https://git.privateger.me) of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](http://git.magic-beans.cn:3000) that uses reinforcement finding out to improve reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key differentiating function is its support learning (RL) step, which was used to improve the design's responses beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust more [efficiently](https://www.e-vinil.ro) to user feedback and objectives, ultimately improving both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, [suggesting](https://praca.e-logistyka.pl) it's geared up to break down intricate inquiries and factor through them in a detailed manner. This guided reasoning process permits the design to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually caught the market's attention as a flexible text-generation model that can be integrated into different workflows such as agents, logical reasoning and information analysis jobs.
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DeepSeek-R1 utilizes a Mixture of [Experts](https://git.wisder.net) (MoE) architecture and is 671 billion criteria in size. The MoE architecture [enables](https://houseimmo.com) activation of 37 billion specifications, [allowing efficient](http://isarch.co.kr) inference by routing inquiries to the most pertinent expert "clusters." This method [permits](https://www.oradebusiness.eu) the model to focus on different problem domains while maintaining general effectiveness. 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 circumstances to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more efficient designs to mimic the behavior and reasoning patterns of the larger DeepSeek-R1 design, 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 model, we recommend deploying this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and assess models against crucial safety requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to various use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](https://git.silasvedder.xyz) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you need 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 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 deploying. To ask for a limitation boost, produce a limitation boost request and reach out to your account group.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For directions, see Set up approvals to use guardrails for [material filtering](https://southernsoulatlfm.com).
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, prevent hazardous material, and examine designs against essential safety requirements. You can carry out security measures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock [console](https://firemuzik.com) or the API. For the example code to develop the guardrail, see the GitHub repo.
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The basic flow includes the following steps: First, the system receives an input for the design. This input is then through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for reasoning. After receiving the model's output, another guardrail check is applied. If the output passes this final 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 took place at the input or output phase. The examples showcased in the following areas show reasoning 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, 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 use the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 model.
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The design detail page supplies essential details about the design's capabilities, prices structure, and implementation standards. You can find detailed use instructions, including sample API calls and code snippets for combination. The model supports numerous text generation jobs, including material production, code generation, and concern answering, utilizing its reinforcement finding out optimization and [CoT reasoning](https://gitea.elkerton.ca) abilities. +The page also consists of release alternatives and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick Deploy.
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You will be prompted to set up the release 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 circumstances, enter a number of circumstances (in between 1-100). +6. For example type, select your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up sophisticated security and facilities settings, consisting of virtual private cloud (VPC) networking, service function approvals, and file encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you may desire to review these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the release is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in playground to access an interactive interface where you can experiment with various triggers and change model criteria like temperature level and maximum length. +When utilizing 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 way to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The playground supplies instant feedback, helping you understand how the design reacts to different inputs and letting you tweak your triggers for optimum outcomes.
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You can [rapidly](http://thinkwithbookmap.com) test the model in the playground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out reasoning utilizing a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have actually produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends out a request 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, integrated algorithms, and prebuilt ML solutions that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into production utilizing either the UI or SDK.
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[Deploying](https://git.prime.cv) DeepSeek-R1 design through SageMaker JumpStart offers two [practical](https://git.komp.family) techniques: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to help you choose the approach that finest fits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following [actions](https://jvptube.net) 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, select JumpStart in the navigation pane.
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The model web browser shows available models, with details like the company name and design capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card shows key details, consisting of:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if appropriate), indicating that this model can be signed up with Amazon Bedrock, allowing you to use [Amazon Bedrock](https://www.belizetalent.com) APIs to conjure up the design
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5. Choose the design card to view the design details page.
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The design details page includes the following details:
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- The model name and company details. +Deploy button to deploy 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 requirements. +- Usage guidelines
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Before you deploy the model, it's advised to evaluate the model details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, use the automatically generated name or produce a custom-made one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the variety of instances (default: 1). +Selecting proper instance types and counts is important for expense and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, [Real-time reasoning](https://social-lancer.com) is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. For this model, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the model.
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The deployment procedure can take numerous minutes to finish.
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When release is total, your endpoint status will change to InService. At this moment, the design is prepared to accept inference demands through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will display appropriate metrics and [status details](http://profilsjob.com). When the deployment is complete, you can invoke the design utilizing a SageMaker runtime client and integrate 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 using 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 use DeepSeek-R1 for [reasoning programmatically](https://www.frigorista.org). The code for deploying the model is [offered](https://www.friend007.com) in the Github here. You can clone the note pad and run from SageMaker Studio.
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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 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 revealed in the following code:
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Tidy up
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To prevent unwanted charges, finish the actions in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you deployed the model using Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases. +2. In the Managed deployments section, find the endpoint you want to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate 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 model you released will sustain costs 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](https://parissaintgermainfansclub.com) and Resources.
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Conclusion
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In this post, we [explored](https://gst.meu.edu.jo) how you can access and deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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](https://gitlab.lizhiyuedong.com) Architect for Inference at AWS. He assists emerging generative [AI](https://gitea.dokm.xyz) [companies build](https://abcdsuppermarket.com) ingenious services utilizing AWS services and accelerated compute. Currently, he is focused on developing techniques for fine-tuning and optimizing the reasoning efficiency of large language models. In his leisure time, Vivek enjoys treking, watching movies, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://hektips.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://thesecurityexchange.com) 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 dealing with generative [AI](https://phones2gadgets.co.uk) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.silasvedder.xyz) center. She is passionate about building options that assist customers accelerate their [AI](http://60.209.125.238:20010) journey and unlock organization value.
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