commit 799747fcfd7aa416dc789033f98eebed48cd6ade Author: bettinaq723790 Date: Fri Feb 7 17:26:47 2025 +0300 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..b7ded27 --- /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 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 deploy DeepSeek [AI](https://classificados.diariodovale.com.br)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](https://cosplaybook.de) ideas 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 release the distilled variations of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](http://gogs.funcheergame.com) that utilizes reinforcement discovering to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential distinguishing feature is its [support learning](http://119.3.29.1773000) (RL) step, which was utilized to improve the model's responses beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, ultimately improving both relevance and [clarity](https://www.suyun.store). In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, meaning it's geared up to break down complex questions and factor through them in a detailed manner. This directed reasoning process allows the design to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation design that can be integrated into different workflows such as agents, sensible reasoning and data analysis tasks.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture [enables activation](https://videoflixr.com) of 37 billion specifications, allowing efficient inference by [routing questions](https://134.209.236.143) to the most pertinent expert "clusters." This approach allows the model to specialize in different problem domains while maintaining total effectiveness. DeepSeek-R1 needs a minimum of 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 model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design 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 the behavior and reasoning patterns of the bigger DeepSeek-R1 design, using 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 suggest releasing this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and evaluate models against essential security criteria. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and [wavedream.wiki](https://wavedream.wiki/index.php/User:Kristie6813) Bedrock Marketplace, [Bedrock Guardrails](https://schubach-websocket.hopto.org) supports only the ApplyGuardrail API. You can create multiple guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](http://103.254.32.77) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify 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 limit increase, develop a limitation increase demand and connect to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Set up authorizations to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to present safeguards, avoid hazardous content, and evaluate designs against essential security requirements. You can carry out precaution for the DeepSeek-R1 design using the Amazon Bedrock [ApplyGuardrail](https://customerscomm.com) API. This allows you to use guardrails to examine user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock [console](http://www.stes.tyc.edu.tw) or the API. For the example code to create the guardrail, see the GitHub repo.
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The basic flow includes the following actions: First, the system gets an input for the design. This input is then [processed](http://175.178.153.226) through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for inference. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the result. However, if either the input or output is [intervened](https://test.gamesfree.ca) by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred 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 provides you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, choose Model catalog under [Foundation models](https://wacari-git.ru) in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to conjure up the design. It doesn't [support Converse](https://gitlab.cloud.bjewaytek.com) APIs and other Amazon Bedrock tooling. +2. Filter for [DeepSeek](https://code.cypod.me) as a service provider and select the DeepSeek-R1 design.
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The design detail page provides essential details about the model's capabilities, prices structure, and execution standards. You can discover detailed use guidelines, including sample API calls and code snippets for combination. The design supports different text generation jobs, consisting of material creation, code generation, and question answering, utilizing its support discovering [optimization](http://travelandfood.ru) and CoT thinking capabilities. +The page likewise includes deployment alternatives and licensing details to help you start with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be triggered to set up the release details 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, go into a variety of circumstances (in between 1-100). +6. For Instance type, select your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can set up sophisticated security and [infrastructure](https://gitea.eggtech.net) settings, including virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For many use cases, the default settings will work well. However, for production releases, you may desire to [evaluate](https://www.huntsrecruitment.com) these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the implementation is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive user interface where you can experiment with various triggers and adjust model specifications like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum results. For example, material for inference.
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This is an outstanding way to check out the model's thinking and text generation abilities before integrating it into your applications. The play ground [supplies](http://git.jihengcc.cn) instant feedback, helping you comprehend how the [model reacts](http://gogs.dev.fudingri.com) to different inputs and letting you fine-tune your prompts for optimal outcomes.
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You can rapidly test the design in the play ground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference using guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the [Amazon Bedrock](https://coatrunway.partners) console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends out a demand to produce text based on a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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[SageMaker JumpStart](https://kaymack.careers) is an artificial intelligence (ML) center with FMs, built-in algorithms, and [prebuilt](http://123.207.206.1358048) ML services that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers two convenient techniques: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you select the technique 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](http://111.53.130.1943000) DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:RYUDarell4) choose Studio in the navigation pane. +2. First-time users will be triggered to [produce](https://tokemonkey.com) a domain. +3. On the SageMaker Studio console, [select JumpStart](http://testyourcharger.com) in the navigation pane.
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The design web browser displays available models, with details like the provider name and model capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design card shows essential details, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:ChiquitaVeilleux) consisting of:
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- Model name +- Provider name +- Task category (for [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) example, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to view the design details page.
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The design details page consists of the following details:
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- The model name and provider details. +Deploy button to release the model. +About and [Notebooks tabs](https://satitmattayom.nrru.ac.th) with detailed details
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The About tab consists of crucial details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage standards
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Before you deploy the model, it's suggested to evaluate the and license terms to [validate compatibility](https://git.jackbondpreston.me) with your usage case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, utilize the automatically produced name or produce a customized one. +8. For Instance type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of circumstances (default: 1). +Selecting suitable instance types and counts is vital for cost and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for accuracy. For this model, we highly suggest sticking to [SageMaker JumpStart](https://winf.dhsh.de) default settings and making certain that network isolation remains in location. +11. Choose Deploy to release the design.
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The release process can take several minutes to complete.
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When implementation is complete, your endpoint status will change to InService. At this point, the model is ready to accept reasoning requests through the [endpoint](https://pediascape.science). You can keep an eye on the implementation progress on the SageMaker console Endpoints page, [links.gtanet.com.br](https://links.gtanet.com.br/terilenz4996) which will show relevant metrics and status details. When the release is complete, you can conjure up the model utilizing a SageMaker runtime customer and incorporate it with your applications.
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Deploy DeepSeek-R1 utilizing 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 essential AWS permissions 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 model is supplied in the Github here. You can clone the notebook and [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:EdithJoseph92) 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock [console](https://git.youxiner.com) or [larsaluarna.se](http://www.larsaluarna.se/index.php/User:Rosalind2029) the API, and implement it as [revealed](https://dreamtube.congero.club) in the following code:
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Clean up
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To avoid unwanted charges, complete the actions in this section to tidy 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, total the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases. +2. In the [Managed deployments](https://estekhdam.in) area, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're [erasing](https://hcp.com.gt) 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 deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you wish to stop [sustaining charges](https://www.paradigmrecruitment.ca). 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 model utilizing Bedrock [Marketplace](http://175.178.153.226) and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace 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 Getting going 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://feleempleo.es) [business build](https://natgeophoto.com) innovative solutions using AWS services and accelerated compute. Currently, he is concentrated on establishing strategies for fine-tuning and optimizing the reasoning performance of big language models. In his leisure time, Vivek enjoys hiking, seeing films, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://remoterecruit.com.au) Specialist Solutions Architect with the [Third-Party Model](http://koreaeducation.co.kr) Science group at AWS. His area of focus is AWS [AI](https://git.starve.space) 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 working on generative [AI](https://noblessevip.com) with the Third-Party Model Science group at AWS.
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[Banu Nagasundaram](https://sebagai.com) leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://git.permaviat.ru) center. She is enthusiastic about developing services that assist clients accelerate their [AI](http://git.jihengcc.cn) journey and unlock business worth.
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