commit 14cf20a2f70c3af084e7e298c5b1e9265b814553 Author: isla6102675402 Date: Thu Apr 3 17:58:12 2025 +0000 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..f5d0d9a --- /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](http://gitea.smartscf.cn8000) R1 distilled Llama and Qwen models are available through Amazon [Bedrock Marketplace](http://122.112.209.52) and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://hub.tkgamestudios.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](http://www.yfgame.store) [concepts](https://openedu.com) on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the designs as well.
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[Overview](https://www.lokfuehrer-jobs.de) of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) developed by [DeepSeek](https://bakery.muf-fin.tech) [AI](http://christiancampnic.com) that utilizes support discovering to improve thinking [abilities](http://git.estoneinfo.com) through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing function is its reinforcement knowing (RL) step, which was used to fine-tune the design's actions beyond the standard pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, eventually boosting both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, suggesting it's geared up to break down intricate inquiries and factor through them in a detailed way. This directed reasoning procedure enables the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT abilities, aiming to [produce structured](https://gitlab.rail-holding.lt) responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation design that can be incorporated into various workflows such as agents, logical reasoning and data analysis tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, allowing efficient reasoning by routing queries to the most pertinent expert "clusters." This [technique enables](https://git.jerrita.cn) the design to focus on different problem [domains](https://www.rhcapital.cl) while maintaining general effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 [xlarge instance](http://118.190.175.1083000) to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 model to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more efficient models to [simulate](https://gitlab.syncad.com) the habits and [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:EfrainWawn65) thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor design.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and examine models against essential security requirements. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](https://gitlab.healthcare-inc.com) applications.
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Prerequisites
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To [release](https://aceme.ink) the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limit increase, produce a [limitation increase](https://wiki.solsombra-abdl.com) demand and [connect](http://220.134.104.928088) to your [account](https://xnxxsex.in) group.
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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) approvals to utilize Amazon Bedrock Guardrails. For guidelines, see Establish approvals to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to [introduce](http://47.114.187.1113000) safeguards, prevent hazardous material, and examine designs against crucial safety criteria. You can carry out security steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and model reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For [surgiteams.com](https://surgiteams.com/index.php/User:AdolfoGaertner6) the example code to produce 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 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 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 demonstrate inference 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](https://media.izandu.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 catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize 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 select the DeepSeek-R1 model.
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The model detail page offers important details about the design's capabilities, pricing structure, and implementation guidelines. You can discover detailed use guidelines, consisting of sample API calls and code snippets for combination. The model supports different text generation jobs, consisting of content development, code generation, and question answering, [utilizing](https://www.sparrowjob.com) its reinforcement learning optimization and CoT thinking capabilities. +The page likewise includes release alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, pick Deploy.
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You will be prompted to set up the [implementation details](https://owangee.com) 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, go into a [variety](https://sunrise.hireyo.com) of instances (in between 1-100). +6. For example type, pick your instance type. For optimal efficiency with DeepSeek-R1, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1344139) a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure advanced security and facilities settings, consisting of [virtual private](https://video.clicktruths.com) cloud (VPC) networking, service function authorizations, and encryption settings. For the majority of utilize cases, the default settings will work well. However, for production releases, you might wish to evaluate these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the release is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive user interface where you can experiment with various triggers and change design criteria like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum results. For instance, material for reasoning.
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This is an excellent way to explore the [design's reasoning](https://raida-bw.com) and text generation abilities before incorporating it into your applications. The playground offers instant feedback, assisting you understand how the design responds to various inputs and letting you fine-tune your triggers for optimum results.
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You can rapidly test the design in the playground through the UI. However, to conjure up 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 carry out reasoning using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock [console](http://42.192.130.833000) or the API. For the example code to produce the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, configures reasoning parameters, and sends 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 ML solutions that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses two practical approaches: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to help you choose the method that finest fits your [requirements](https://www.ayuujk.com).
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model web browser displays available models, with details like the service provider name and design capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card reveals crucial details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if appropriate), showing that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the design card to view the design details page.
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The model details 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 essential details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage guidelines
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Before you deploy the design, it's recommended to examine the model details and license terms to verify compatibility with your use case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, use the immediately created name or create a custom-made one. +8. For example type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of instances (default: 1). +Selecting suitable circumstances types and counts is crucial for cost and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for precision. For this design, we strongly suggest [adhering](http://bammada.co.kr) to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to release the design.
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The implementation process can take numerous minutes to finish.
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When release is complete, your endpoint status will change to InService. At this point, the model is ready to accept inference requests through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is total, you can invoke the model using a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 [utilizing](https://firemuzik.com) the SageMaker Python SDK
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To begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the [SageMaker Python](https://gitea.linkensphere.com) SDK and make certain you have the required AWS consents and . The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the model is offered 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 inference 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 create a guardrail using the Amazon Bedrock console or the API, and implement it as [displayed](http://duberfly.com) in the following code:
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Clean up
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To avoid unwanted charges, complete the steps in this area to clean up your resources.
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Delete the Amazon Bedrock [Marketplace](https://gitea-working.testrail-staging.com) release
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If you deployed the model utilizing Amazon Bedrock Marketplace, [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:SBNMarty65594) total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments. +2. In the Managed implementations section, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, [choose Delete](https://git.ivran.ru). +4. Verify the endpoint details to make certain you're deleting the correct implementation: 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 expenses 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](https://git.torrents-csv.com) and Resources.
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Conclusion
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In this post, we [explored](http://lnsbr-tech.com) how you can access and deploy the DeepSeek-R1 model utilizing 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 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](https://jobsantigua.com) is a Lead Specialist Solutions [Architect](http://1.12.246.183000) for Inference at AWS. He helps emerging generative [AI](https://hesdeadjim.org) [companies construct](https://gitlab01.avagroup.ru) innovative solutions using AWS services and accelerated calculate. Currently, he is focused on developing methods for fine-tuning and enhancing the inference performance of big language designs. In his downtime, Vivek delights in treking, seeing movies, and attempting various foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://www.zjzhcn.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://www.raverecruiter.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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[Jonathan Evans](http://203.171.20.943000) is a Professional Solutions Architect dealing with generative [AI](http://112.126.100.134:3000) 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 intelligence and generative [AI](http://www.yfgame.store) hub. She is enthusiastic about constructing solutions that help clients accelerate their [AI](http://52.23.128.62:3000) journey and [unlock company](https://sfren.social) value.
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