commit dd02e5dff11d3ec28ca2fb63a568dea7b56c24ab Author: ermawilfred433 Date: Fri Feb 7 02:59:50 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..e709fdb --- /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 deploy DeepSeek [AI](https://interlinkms.lk)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative [AI](https://ospitalierii.ro) concepts on AWS.
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In this post, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:AlineCox0079049) we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://myteacherspool.com) that utilizes support finding out to enhance reasoning [abilities](https://pakkalljob.com) through a multi-stage training process from a DeepSeek-V3-Base foundation. A key identifying function is its reinforcement knowing (RL) step, which was used to refine the model's reactions beyond the basic pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually improving both significance and [clarity](https://xn--939a42kg7dvqi7uo.com). In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, suggesting it's geared up to break down complex inquiries and reason through them in a detailed way. This guided reasoning process allows the design to produce more precise, transparent, and detailed answers. This [design combines](https://gigen.net) [RL-based](https://git.logicloop.io) fine-tuning with CoT abilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has captured the market's attention as a versatile text-generation model that can be integrated into different workflows such as agents, sensible thinking and data interpretation jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The [MoE architecture](http://koreaeducation.co.kr) allows activation of 37 billion specifications, enabling effective inference by routing inquiries to the most relevant specialist "clusters." This technique allows the design to concentrate on different issue domains while maintaining general performance. 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 design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 model to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:LindseyWalstab9) 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient models to imitate the behavior and thinking patterns of the bigger 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 model, we suggest releasing this design with [guardrails](http://admin.youngsang-tech.com) in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and assess designs against crucial security criteria. At the time of [composing](https://almagigster.com) this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and [Bedrock](http://www.xn--739an41crlc.kr) Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several [guardrails tailored](https://cello.cnu.ac.kr) to different use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](http://49.50.103.174) applications.
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Prerequisites
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To release the DeepSeek-R1 design, 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, choose Amazon SageMaker, and confirm 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 limit increase, develop a limit boost demand and reach out to your account team.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Establish consents to use guardrails for content [filtering](https://truthbook.social).
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, prevent harmful material, and assess designs against crucial security criteria. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon [Bedrock console](http://teamcous.com) or the API. For the example code to produce the guardrail, see the GitHub repo.
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The general circulation includes the following steps: First, the system [receives](https://git.brass.host) an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for inference. After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned showing 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 gives you access to over 100 popular, emerging, and [specialized foundation](https://voyostars.com) 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, select Model brochure under Foundation models in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.
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The design detail page supplies essential details about the model's abilities, pricing structure, and execution standards. You can discover detailed usage guidelines, consisting of sample API calls and code bits for combination. The design supports various text generation jobs, including material creation, code generation, and concern answering, [utilizing](https://freakish.life) its reinforcement discovering optimization and CoT thinking capabilities. +The page likewise consists of implementation alternatives and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, select Deploy.
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You will be [triggered](https://git.xjtustei.nteren.net) to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, go into a variety of instances (in between 1-100). +6. For [Instance](https://awaz.cc) type, pick your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function permissions, and encryption settings. For a lot of use cases, the default settings will work well. However, for production releases, you might want to review 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 release is complete, you can check DeepSeek-R1's capabilities 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 change design parameters like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal results. For example, material for inference.
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This is an outstanding method to check out the model's reasoning and text generation abilities before integrating it into your applications. The play ground offers instant feedback, assisting you understand how the [model responds](https://estekhdam.in) to numerous inputs and letting you [fine-tune](http://175.178.71.893000) your prompts for optimal results.
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You can rapidly test the model in the play ground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you need 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 demonstrates how to perform reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop 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 created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends out a [request](http://forum.pinoo.com.tr) to generate text based upon 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](http://bristol.rackons.com) ML services that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage 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 offers 2 practical approaches: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you pick the technique that finest suits your [requirements](https://www.kayserieticaretmerkezi.com).
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps 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 triggered to create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model web browser displays available designs, with details like the service provider name and design capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 . +Each model card shows essential details, consisting of:
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[- Model](https://tv.360climatechange.com) name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if suitable), showing that this model can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the design card to see the model details page.
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The model details page consists of the following details:
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- The model name and service provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of important 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 suggested to evaluate the design 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, utilize the immediately created name or produce a custom one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of circumstances (default: 1). +Selecting proper circumstances types and counts is crucial for cost and efficiency optimization. Monitor your deployment to change these [settings](http://csserver.tanyu.mobi19002) as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this model, we highly recommend sticking to SageMaker JumpStart default settings and making certain that [network isolation](http://124.222.48.2033000) remains in location. +11. Choose Deploy to deploy the model.
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The deployment process can take several minutes to complete.
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When implementation is complete, your endpoint status will alter to InService. At this point, the design is all set to accept reasoning demands through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is total, you can conjure up the design 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 get started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and [it-viking.ch](http://it-viking.ch/index.php/User:Heath0421670) make certain you have the needed AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for 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 additional requests 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 likewise 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 revealed in the following code:
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Clean up
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To avoid unwanted charges, complete the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you [deployed](https://bytes-the-dust.com) the design using Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations. +2. In the Managed implementations section, find the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the right 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 design you released will [sustain costs](https://gitea.carmon.co.kr) if you leave it running. Use the following code to erase 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 explored how you can access and [yewiki.org](https://www.yewiki.org/User:EdwinaMcintire3) release the DeepSeek-R1 design using [Bedrock Marketplace](http://120.77.213.1393389) 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 JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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.the-kn.com) companies build innovative solutions using AWS services and accelerated calculate. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning efficiency of big language models. In his complimentary time, Vivek takes pleasure in hiking, enjoying films, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.boatcareer.com) [Specialist Solutions](https://interconnectionpeople.se) Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://gitlab.truckxi.com) 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://gitlab.freedesktop.org) 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](http://31.184.254.176:8078) hub. She is enthusiastic about constructing options that assist customers accelerate their [AI](https://gitlab.steamos.cloud) journey and unlock service value.
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