From 46427a54c8833f693e25c78ea77300193cfacbda Mon Sep 17 00:00:00 2001 From: April Steffen Date: Fri, 14 Feb 2025 22:09:01 +0000 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..7fad3e6 --- /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 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://www.hyxjzh.cn:13000)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](https://git.techview.app) [concepts](https://integramais.com.br) on AWS.
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In this post, we show how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the [distilled variations](http://git.1473.cn) 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) established by DeepSeek [AI](https://lifestagescs.com) that uses reinforcement finding out to improve thinking capabilities through a multi-stage training [process](http://fridayad.in) from a DeepSeek-V3-Base structure. A crucial differentiating feature is its support knowing (RL) action, which was used to [improve](https://git.fpghoti.com) the model's actions beyond the standard pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, suggesting it's geared up to break down intricate queries and reason through them in a detailed way. This assisted reasoning process allows the model to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation model that can be incorporated into various workflows such as agents, sensible thinking and information interpretation tasks.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion parameters, enabling effective inference by routing inquiries to the most appropriate professional "clusters." This approach permits the design to [concentrate](https://www.noagagu.kr) on various problem domains while maintaining total [efficiency](http://121.43.121.1483000). DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize 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 capabilities of the main R1 design to more effective architectures based upon [popular](https://paroldprime.com) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:FidelBatt531106) more efficient models to simulate the habits and thinking patterns of the bigger DeepSeek-R1 design, using it as a teacher design.
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You can release DeepSeek-R1 design either through [SageMaker JumpStart](https://careers.express) or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in [location](https://insta.kptain.com). In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and assess designs against key safety requirements. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative [AI](https://inktal.com) applications.
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Prerequisites
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To release the DeepSeek-R1 design, 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, choose Amazon SageMaker, and verify you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limit boost, develop a limitation increase demand and connect to your account group.
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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) approvals to utilize Amazon Bedrock Guardrails. For instructions, see Establish authorizations to use guardrails for [material filtering](https://tempjobsindia.in).
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[Implementing guardrails](http://mooel.co.kr) with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, prevent harmful content, and examine models against key safety criteria. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and model reactions deployed on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://www.nas-store.com). You can create a guardrail using the Amazon Bedrock console 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 actions: First, the system [receives](https://www.nas-store.com) an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the [guardrail](https://jobsdirect.lk) check, it's sent out to the design for reasoning. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's [returned](https://www.nikecircle.com) 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 happened at the input or output stage. The examples showcased in the following sections demonstrate 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 foundation models (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 brochure under Foundation designs in the navigation pane. +At the time of writing this post, you can use the [InvokeModel API](https://www.imdipet-project.eu) to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 design.
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The model detail page provides essential details about the design's abilities, rates structure, and execution guidelines. You can find detailed usage guidelines, consisting of sample API calls and code snippets for combination. The model supports numerous [text generation](http://web.joang.com8088) jobs, consisting of material production, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT reasoning abilities. +The page likewise includes deployment alternatives and licensing details to help you get started with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, select Deploy.
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You will be triggered to configure the deployment details for DeepSeek-R1. The design 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, get in a variety of instances (in between 1-100). +6. For example type, pick 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 advanced security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function permissions, and encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you may want to examine these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the deployment is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive interface where you can explore various triggers and adjust design specifications like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For instance, material for inference.
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This is an [exceptional](https://topdubaijobs.ae) way to check out the model's thinking and text generation [capabilities](https://writerunblocks.com) before integrating it into your applications. The playground provides immediate feedback, assisting you understand how the model reacts to various inputs and letting you tweak your prompts for ideal results.
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You can quickly evaluate the model in the [playground](http://120.77.205.309998) through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce 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 produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up inference criteria, and sends out a request to create 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) hub 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 models to your usage 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 practical techniques: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you pick the technique that best 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 using SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:RYUDarell4) select JumpStart in the navigation pane.
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The design browser displays available models, with details like the supplier name and design capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card reveals crucial details, consisting of:
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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 registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to [conjure](http://122.51.230.863000) up the model
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5. Choose the model card to view the model details page.
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The model details page consists of the following details:
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- The design name and [supplier details](https://choosy.cc). +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab includes details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage standards
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Before you release the model, it's advised to examine the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, utilize the immediately produced name or develop a customized one. +8. For [Instance type](https://classificados.diariodovale.com.br) ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the number of circumstances (default: 1). +Selecting appropriate instance types and counts is crucial for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. For this model, we [highly recommend](http://49.235.101.2443001) sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to deploy the model.
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The implementation process can take a number of minutes to complete.
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When release is complete, your endpoint status will change to [InService](https://cvbankye.com). At this moment, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:YvonneBlubaugh) the model is prepared to accept inference demands through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will [display relevant](http://www.thekaca.org) metrics and status details. When the implementation is complete, you can [conjure](http://87.98.157.123000) up the model using a SageMaker runtime client and incorporate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker [Python SDK](https://www.youtoonet.com) and make certain you have the required AWS [permissions](http://git.maxdoc.top) and environment setup. The following is a detailed code example that demonstrates how to [release](https://cello.cnu.ac.kr) and use DeepSeek-R1 for [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:UnaProsser9137) inference programmatically. The code for releasing the design is supplied in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run extra demands against the predictor:
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Implement guardrails and run [reasoning](https://git.mitsea.com) with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker [JumpStart predictor](https://seedvertexnetwork.co.ke). You can create a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:
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Clean up
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To avoid unwanted charges, finish the steps 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 design utilizing 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. +2. In the Managed implementations area, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're [erasing](https://projectblueberryserver.com) the proper 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 deployed will sustain expenses if you leave it [running](https://lubuzz.com). Use the following code to delete 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 checked out how you can access and [release](http://csserver.tanyu.mobi19002) the DeepSeek-R1 model using [Bedrock Marketplace](http://117.72.17.1323000) and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with [Amazon SageMaker](https://18plus.fun) JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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](https://www.mgtow.tv) companies construct ingenious options utilizing AWS services and accelerated calculate. Currently, he is focused on developing strategies for fine-tuning and optimizing the inference efficiency of big [language designs](http://124.221.255.92). In his free time, Vivek takes pleasure in hiking, enjoying motion pictures, and attempting different cuisines.
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Niithiyn Vijeaswaran is a [Generative](https://git.perbanas.id) [AI](https://jvptube.net) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://iklanbaris.id) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://repo.serlink.es) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://www.thegrainfather.co.nz) hub. She is passionate about building services that assist clients accelerate their [AI](https://spreek.me) journey and unlock organization worth.
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