commit 6d9d742a52e38824d5a36445cb99fcd4d112983c Author: glindaatkin69 Date: Sat Apr 5 16:39:33 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..19e2db9 --- /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 reveal that DeepSeek R1 distilled Llama and [Qwen models](https://git.fhlz.top) are available through Amazon Bedrock Marketplace and [Amazon SageMaker](https://pk.thehrlink.com) JumpStart. With this launch, you can now deploy DeepSeek [AI](https://git.selfmade.ninja)['s first-generation](https://git.iws.uni-stuttgart.de) frontier design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your [generative](http://116.203.108.1653000) [AI](https://starleta.xyz) concepts on AWS.
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In this post, we show how to get begun with DeepSeek-R1 on [Amazon Bedrock](http://154.64.253.773000) Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled versions of the designs also.
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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://projobfind.com) that uses reinforcement learning to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating function is its support learning (RL) action, which was utilized to refine the design's actions beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, eventually enhancing both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, suggesting it's geared up to break down complex questions and reason through them in a detailed manner. This directed reasoning process allows the design to produce more precise, transparent, [it-viking.ch](http://it-viking.ch/index.php/User:JasonHeavener88) and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has recorded the industry's attention as a flexible text-generation design that can be incorporated into different workflows such as agents, sensible reasoning and information interpretation tasks.
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DeepSeek-R1 uses a Mixture of [Experts](https://git.skyviewfund.com) (MoE) architecture and is 671 billion [parameters](http://lifethelife.com) in size. The MoE architecture allows activation of 37 billion specifications, enabling effective inference by [routing inquiries](https://impactosocial.unicef.es) to the most appropriate expert "clusters." This technique permits the design to concentrate on different problem 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](https://han2.kr) an ml.p5e.48 xlarge instance to deploy 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 thinking capabilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more effective models to simulate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor model.
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You can [release](https://blog.giveup.vip) 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 utilize Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and examine models against key security requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, [enhancing](https://oros-git.regione.puglia.it) user experiences and standardizing security controls across your generative [AI](http://47.90.83.132:3000) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and [confirm](http://182.92.169.2223000) 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 boost, create a limit boost request 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 appropriate AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For instructions, see Set up permissions to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, avoid hazardous content, and [gratisafhalen.be](https://gratisafhalen.be/author/melanie01q4/) assess designs against crucial security requirements. You can implement safety steps for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to examine user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console 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 actions: First, the system receives 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 design for reasoning. After receiving the design's output, another guardrail check is used. If the output passes this last check, it's returned as the last outcome. However, if either the input or output is stepped in by the guardrail, a is returned showing the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas show 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 foundation 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 under Foundation models in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and pick the DeepSeek-R1 design.
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The design detail page offers necessary details about the design's abilities, pricing structure, and [disgaeawiki.info](https://disgaeawiki.info/index.php/User:TYKEarl029660062) implementation standards. You can find detailed usage instructions, including sample API calls and code snippets for combination. The design supports various text generation jobs, including material production, code generation, and question answering, utilizing its support discovering optimization and CoT thinking capabilities. +The page also includes implementation choices and licensing details to help you get begun with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
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You will be prompted 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 instances, enter a variety of circumstances (in between 1-100). +6. For [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:AlizaSmallwood) Instance type, choose your circumstances type. For [optimal performance](https://hektips.com) with DeepSeek-R1, a [GPU-based](http://116.203.108.1653000) [instance type](https://zeroth.one) like ml.p5e.48 xlarge is advised. +Optionally, you can set up advanced security and facilities settings, including virtual personal cloud (VPC) networking, service function permissions, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you might wish to review these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the release is total, you can evaluate 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 try out different triggers and change design specifications like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum outcomes. For instance, material for inference.
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This is an exceptional way to explore the design's reasoning and text generation abilities before integrating it into your applications. The play ground supplies instant feedback, assisting you comprehend how the design reacts to various inputs and letting you [fine-tune](https://www.jobsition.com) your prompts for optimum outcomes.
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You can rapidly test the model in the play area 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 shows how to perform reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up [inference](https://gitlab.freedesktop.org) specifications, and sends out a demand to generate text 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, integrated algorithms, and [prebuilt](https://croart.net) ML [solutions](https://git.tx.pl) that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses two convenient techniques: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to help you pick the approach that best matches 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 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the [SageMaker Studio](https://www.pickmemo.com) console, select JumpStart in the navigation pane.
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The model web browser shows available designs, with details like the provider name and model capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card shows key details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if suitable), suggesting that this design can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the design
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5. Choose the model card to view the model details page.
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The design details page includes the following details:
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- The design name and company details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About [tab consists](https://followmypic.com) of crucial details, such as:
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- Model description. +- License [details](https://luckyway7.com). +- Technical specs. +[- Usage](https://swahilihome.tv) standards
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Before you deploy the model, it's advised to evaluate 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](https://code.smolnet.org) name, use the instantly generated name or produce a custom-made one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the number of instances (default: 1). +Selecting proper circumstances types and counts is vital for cost and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:CathyLouis0372) precision. For this model, we highly suggest adhering to SageMaker JumpStart default settings and making certain that [network isolation](https://allcallpro.com) remains in place. +11. Choose Deploy to release the model.
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The deployment process can take several minutes to complete.
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When release is complete, your endpoint status will change to InService. At this moment, the model is all set to accept inference requests through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is complete, you can conjure up the model utilizing a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 using the [SageMaker Python](https://www.etymologiewebsite.nl) SDK
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To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to deploy and [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:SheilaDaws73394) utilize DeepSeek-R1 for inference programmatically. The code for [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:LucileMcElhaney) deploying the design is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra requests against the predictor:
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[Implement guardrails](http://112.125.122.2143000) 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 create a guardrail using the Amazon Bedrock console or the API, and implement it as [displayed](http://gitlab.andorsoft.ad) in the following code:
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Tidy up
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To avoid undesirable charges, finish the actions 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, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the [navigation](https://celticfansclub.com) pane, choose Marketplace releases. +2. In the Managed deployments area, locate the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the right deployment: 1. [Endpoint](https://www.airemploy.co.uk) 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 delete the endpoint if you want 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 deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock [tooling](https://gogs.xinziying.com) with Amazon SageMaker JumpStart models, [SageMaker JumpStart](http://81.70.93.2033000) 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](https://legatobooks.com) for Inference at AWS. He helps emerging generative [AI](https://younghopestaffing.com) business build ingenious solutions utilizing AWS services and accelerated compute. Currently, he is focused on developing techniques for fine-tuning and enhancing the inference performance of large language models. In his complimentary time, Vivek takes pleasure in treking, watching movies, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://warleaks.net) Specialist Solutions [Architect](http://www.hnyqy.net3000) with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://manpoweradvisors.com) 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](http://hjl.me) with the [Third-Party Model](http://git.1473.cn) Science team at AWS.
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Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://phdjobday.eu) center. She is enthusiastic about constructing solutions that assist customers accelerate their [AI](http://ccrr.ru) journey and unlock organization value.
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