commit 59f561ff09507613ede3cc0d008d3ccb9f41cd29 Author: titusysa055573 Date: Fri Feb 28 10:23:25 2025 +0800 Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart 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..e796032 --- /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 announce 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](https://zamhi.net)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion specifications to build, experiment, and properly scale your generative [AI](https://git.ivabus.dev) ideas on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://ourehelp.com) that uses reinforcement finding out to boost thinking [capabilities](https://diversitycrejobs.com) through a multi-stage training process from a DeepSeek-V3-Base foundation. A key identifying function is its support learning (RL) step, which was used to improve the model's reactions beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt more [effectively](http://getthejob.ma) to user feedback and objectives, ultimately improving both relevance and [clarity](http://111.229.9.193000). In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, suggesting it's geared up to break down [intricate inquiries](https://tjoobloom.com) and factor through them in a detailed manner. This directed reasoning procedure permits the design to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to create structured actions while concentrating on interpretability and user [interaction](https://isourceprofessionals.com). With its [extensive abilities](https://www.jobexpertsindia.com) DeepSeek-R1 has actually caught the market's attention as a versatile text-generation model that can be incorporated into various workflows such as agents, sensible thinking and information analysis tasks.
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DeepSeek-R1 [utilizes](https://voyostars.com) a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion criteria, allowing effective inference by routing inquiries to the most appropriate expert "clusters." This approach permits the design to concentrate on different issue domains while [maintaining](https://demo.wowonderstudio.com) overall performance. 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 features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model 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 process of training smaller, more effective models to imitate the habits and thinking patterns of the larger DeepSeek-R1 design, using it as a teacher model.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent hazardous content, and evaluate models against key security criteria. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and [Bedrock](http://test-www.writebug.com3000) Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative [AI](https://activeaupair.no) [applications](https://git.coalitionofinvisiblecolleges.org).
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To examine 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 circumstances in the AWS Region you are deploying. To ask for a limit increase, produce a limitation boost request and connect to your account team.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and [Gain Access](http://47.100.17.114) To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For instructions, see Set up approvals to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, prevent hazardous content, and assess designs against essential security criteria. You can implement safety measures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and design reactions deployed on Amazon Bedrock [Marketplace](http://49.232.207.1133000) and SageMaker JumpStart. 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.
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The basic circulation involves the following steps: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the design for inference. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the final outcome. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output phase. 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 offers 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, select Model [brochure](http://new-delhi.rackons.com) under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 model.
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The model detail page provides important details about the design's capabilities, rates structure, and execution guidelines. You can find detailed usage instructions, including sample API calls and code snippets for combination. The model supports numerous text generation jobs, including content development, code generation, and question answering, using its support finding out [optimization](https://git.kansk-tc.ru) and CoT thinking capabilities. +The page also consists of deployment alternatives and licensing details to assist you get started with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be triggered to configure the [release details](https://www.allclanbattles.com) for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, enter a variety of circumstances (in between 1-100). +6. For example type, select your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure sophisticated security and infrastructure settings, including virtual private cloud (VPC) networking, service role authorizations, and file encryption settings. For many [utilize](https://squishmallowswiki.com) cases, the default settings will work well. However, for production releases, you might wish to examine 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 implementation is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive interface where you can experiment with various prompts and change design parameters like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum results. For instance, content for reasoning.
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This is an exceptional way to check out the design's thinking and text generation abilities before incorporating it into your [applications](http://www.colegio-sanandres.cl). The playground provides immediate feedback, helping you comprehend how the design reacts to numerous inputs and letting you fine-tune your triggers for optimum results.
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You can rapidly test the model in the [play ground](http://1.92.128.2003000) through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference utilizing guardrails with the [deployed](https://etrade.co.zw) DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning utilizing a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop 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 actually developed the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up [reasoning](https://git.yingcaibx.com) criteria, and sends 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, [yewiki.org](https://www.yewiki.org/User:DanielleEve) integrated algorithms, and prebuilt ML solutions that you can deploy with just a few 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 DeepSeek-R1 model through SageMaker JumpStart provides two convenient methods: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you choose the [technique](https://www.ifodea.com) that finest fits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the [SageMaker](http://35.207.205.183000) console, pick Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, pick JumpStart in the [navigation pane](http://47.110.248.4313000).
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The design internet browser shows available designs, with details like the supplier name and model abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card reveals essential details, consisting of:
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- Model name +- [Provider](https://estekhdam.in) name +- Task category (for example, Text Generation). +Bedrock Ready badge (if applicable), showing that this design can be signed up with Amazon Bedrock, allowing you to use [Amazon Bedrock](https://git.silasvedder.xyz) APIs to conjure up the model
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5. Choose the design card to view the design details page.
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The design [details](https://knightcomputers.biz) page consists of the following details:
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- The design name and provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab includes important details, such as:
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- Model description. +- License details. +- Technical [specifications](https://coatrunway.partners). +- Usage guidelines
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Before you deploy the model, it's suggested to review the model details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the instantly created name or produce a custom one. +8. For Instance type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the variety of instances (default: 1). +Selecting suitable circumstances types and counts is crucial for [expense](https://git.gqnotes.com) and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:CelindaLaidley6) Real-time inference is picked by default. This is optimized for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +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 is total, your endpoint status will change to InService. At this moment, the model is all set to accept inference demands through the endpoint. You can keep an eye on the implementation progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is total, you can invoke the model using a SageMaker runtime client and [incorporate](http://59.110.162.918081) it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will [require](http://gitlab.marcosurrey.de) to set up the SageMaker Python SDK and make certain you have the required AWS [approvals](https://git.cocorolife.tw) and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is offered in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run extra demands 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 utilizing the Amazon Bedrock console or the API, and implement it as revealed in the following code:
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Tidy up
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To avoid [unwanted](http://120.55.164.2343000) charges, finish the steps in this area to clean up your .
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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 models in the navigation pane, pick Marketplace implementations. +2. In the Managed implementations area, 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 erasing the right release: 1. [Endpoint](http://easyoverseasnp.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 wish to stop sustaining charges. For more details, see Delete Endpoints and [Resources](https://villahandle.com).
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Conclusion
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In this post, [kousokuwiki.org](http://kousokuwiki.org/wiki/%E5%88%A9%E7%94%A8%E8%80%85:MoniqueMerrick7) we checked out how you can access and deploy the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock [Marketplace](http://xn--ok0bw7u60ff7e69dmyw.com) now to start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, 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](https://corvestcorp.com) Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://seconddialog.com) companies construct ingenious options using AWS [services](https://collegetalks.site) and accelerated calculate. Currently, he is concentrated on developing methods for fine-tuning and optimizing the inference performance of large language designs. In his spare time, Vivek enjoys treking, viewing movies, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://elitevacancies.co.za) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://git.bubbleioa.top) 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 dealing with generative [AI](https://younivix.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, [SageMaker's artificial](http://awonaesthetic.co.kr) intelligence and generative [AI](http://43.139.10.64:3000) center. She is enthusiastic about developing options that assist consumers accelerate their [AI](https://karjerosdienos.vilniustech.lt) journey and unlock business value.
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