commit f563eaa1863960b4bc75434acec9aab1e6df1196 Author: simanorcross01 Date: Sun Feb 9 02:51:00 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..2e8d722 --- /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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://www.iratechsolutions.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative [AI](https://mount-olive.com) 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 steps to release the distilled versions of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) [developed](http://www.cl1024.online) by DeepSeek [AI](http://git2.guwu121.com) that uses reinforcement finding out to boost thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential distinguishing feature is its support learning (RL) action, which was used to refine the design's actions beyond the basic pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately improving both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's equipped to break down complex inquiries and reason through them in a detailed way. This assisted reasoning process permits the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based [fine-tuning](http://121.196.13.116) with CoT abilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has recorded the market's attention as a versatile text-generation design that can be integrated into various workflows such as agents, logical thinking and information analysis tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion specifications, enabling effective inference by routing queries to the most relevant expert "clusters." This approach enables the model to focus on various problem domains while [maintaining](https://filmcrib.io) overall efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 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 on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and [wavedream.wiki](https://wavedream.wiki/index.php/User:MargieMakin668) 70B). Distillation describes a process of training smaller sized, more effective models to mimic the habits and thinking patterns of the larger DeepSeek-R1 design, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1073855) using it as a teacher model.
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You can [release](https://gitea.qianking.xyz3443) DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we [advise deploying](http://rootbranch.co.za7891) this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and assess models against crucial safety requirements. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, user experiences and standardizing security [controls](https://talentrendezvous.com) throughout your generative [AI](https://www.greenpage.kr) 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 inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select 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 releasing. To request a limitation boost, [produce](http://git.cyjyyjy.com) a limitation increase demand and reach out to your account group.
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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 use Amazon Bedrock Guardrails. For guidelines, see Set up authorizations to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, avoid harmful material, and examine designs against essential security criteria. You can execute safety procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to [examine](https://gitea.oio.cat) user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
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The general flow involves the following actions: First, the system gets 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 reasoning. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the last result. However, if either the input or output is intervened by the guardrail, a message is [returned](http://compass-framework.com3000) showing the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following sections demonstrate reasoning utilizing 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, pick Model catalog under Foundation models in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to [conjure](https://juventusfansclub.com) up the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and choose the DeepSeek-R1 model.
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The design detail page offers essential details about the design's abilities, prices structure, and application standards. You can find detailed usage guidelines, including sample API calls and code bits for integration. The model supports various text generation tasks, including material creation, code generation, and question answering, utilizing its support finding out optimization and CoT reasoning capabilities. +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, choose Deploy.
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You will be triggered to [configure](https://jobsinethiopia.net) the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, go into a number of instances (between 1-100). +6. For Instance type, choose your instance type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure innovative security and facilities settings, including virtual private cloud (VPC) networking, service function consents, and encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you may wish to examine these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the release is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive interface where you can try out various triggers and change model specifications like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal outcomes. For example, material for inference.
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This is an excellent method to explore the design's thinking and text generation capabilities before integrating it into your applications. The play area supplies instant feedback, helping you understand how the model responds to numerous inputs and letting you fine-tune your prompts for ideal results.
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You can rapidly test the model in the play area through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, [yewiki.org](https://www.yewiki.org/User:JefferyGoudie23) you require to get the endpoint ARN.
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Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning utilizing a deployed DeepSeek-R1 model 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 produce the guardrail, see the GitHub repo. After you have produced the guardrail, utilize the following code to execute guardrails. The script [initializes](https://gitea-working.testrail-staging.com) the bedrock_runtime customer, sets up inference criteria, and sends a demand [garagesale.es](https://www.garagesale.es/author/marcyschwar/) to [produce text](http://122.51.46.213) 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) hub with FMs, built-in algorithms, and prebuilt ML services that you can deploy with just a few clicks. With [SageMaker](http://gitlab.andorsoft.ad) JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses two [practical](https://git.kairoscope.net) approaches: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you pick the method that best matches your needs.
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Deploy DeepSeek-R1 through [SageMaker JumpStart](http://47.119.27.838003) UI
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Complete the following steps to release 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 prompted to create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design internet browser shows available designs, with details like the company name and design capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each [design card](https://lifestagescs.com) shows essential details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +[Bedrock Ready](https://git.jerl.dev) badge (if relevant), suggesting that this model can be registered with Amazon Bedrock, allowing you to [utilize Amazon](http://39.101.179.1066440) Bedrock APIs to conjure up the design
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5. Choose the model card to see the model details page.
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The design details page consists of the following details:
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- The model 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 specifications. +- Usage standards
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Before you release the design, it's advised to review the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to [proceed](http://www.mitt-slide.com) with deployment.
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7. For Endpoint name, use the immediately produced name or create a custom-made one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the variety of circumstances (default: 1). +Selecting suitable 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 is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for [accuracy](http://artpia.net). For this model, we strongly suggest adhering 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 complete.
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When deployment is complete, your endpoint status will change to InService. At this moment, the design is prepared to [accept reasoning](http://soho.ooi.kr) requests through the endpoint. You can monitor the implementation development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation is complete, you can [conjure](https://www.sc57.wang) up the design using a SageMaker runtime client and integrate it with your [applications](https://gratisafhalen.be).
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the necessary AWS authorizations and [environment](https://git.wsyg.mx) setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for [releasing](http://gitlab.andorsoft.ad) the design is [supplied](https://jobwings.in) 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 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 develop a guardrail using the Amazon Bedrock console or the API, and execute it as shown in the following code:
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Clean up
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To prevent undesirable charges, finish the [actions](https://mobidesign.us) in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the design using [Amazon Bedrock](https://internship.af) Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations. +2. In the Managed releases section, find the endpoint you want to erase. +3. Select the endpoint, and on the [Actions](https://gitlab.wah.ph) menu, [choose Delete](https://spreek.me). +4. Verify the endpoint details to make certain you're erasing the proper release: 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 erase the endpoint if you desire 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 deploy the DeepSeek-R1 design using 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 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](https://git.purplepanda.cc) business develop ingenious services using AWS services and [accelerated calculate](https://alapcari.com). Currently, he is focused on establishing methods for fine-tuning and enhancing the inference efficiency of big language designs. In his spare time, Vivek delights in hiking, seeing motion pictures, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://xn--114-2k0oi50d.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://2workinoz.com.au) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer [technology](https://www.rotaryjobmarket.com) and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](https://demo.theme-sky.com) with the Third-Party Model Science group 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://mtmnetwork.co.kr) center. She is enthusiastic about constructing solutions that assist clients accelerate their [AI](https://mulaybusiness.com) journey and unlock business worth.
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