commit 7b3fd352fb072cf614064639942421587c9f4e85 Author: shellywinneke Date: Mon Apr 7 20:29:02 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..a57d7bc --- /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 announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon [Bedrock Marketplace](http://101.43.135.2349211) and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://kition.mhl.tuc.gr)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](http://121.40.209.82:3000) concepts on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock [Marketplace](https://playtube.ann.az) and SageMaker JumpStart. You can follow comparable actions to deploy the distilled variations of the models 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](http://lty.co.kr) that utilizes reinforcement learning to enhance thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial identifying function is its reinforcement learning (RL) action, which was used to improve the design's actions beyond the standard pre-training and tweak procedure. By including RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately improving both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's geared up to break down intricate questions and factor through them in a detailed manner. This guided reasoning procedure permits the design to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user [interaction](https://git.alexavr.ru). With its wide-ranging capabilities DeepSeek-R1 has caught the market's attention as a versatile text-generation model that can be incorporated into various workflows such as representatives, rational thinking and information interpretation jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion parameters, enabling effective inference by routing queries to the most pertinent specialist "clusters." This method enables the model to focus on different issue domains while maintaining total efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:GertieAllum31) reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective models to imitate the behavior and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as an instructor design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this model with guardrails in place. In this blog, we will [utilize Amazon](https://git.penwing.org) Bedrock Guardrails to present safeguards, prevent damaging material, and examine designs against essential security criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](https://healthcarejob.cz) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you need access to an ml.p5e [instance](https://git.blinkpay.vn). To if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and validate 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 ask for a limit boost, produce a limitation boost demand and reach out to your account group.
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Because you will be releasing this model with [Amazon Bedrock](https://neoshop365.com) Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Establish approvals to utilize guardrails for [material filtering](https://gitea.tmartens.dev).
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, avoid harmful material, and evaluate models against crucial safety requirements. You can implement precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce 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 circulation involves the following actions: First, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:Darla0047511291) 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 design for inference. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the outcome. 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 stage. The examples showcased in the following sections show [reasoning](https://in.fhiky.com) using 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, total the following actions:
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1. On the [Amazon Bedrock](http://git.thinkpbx.com) console, choose Model catalog under Foundation models in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to [conjure](https://gitea.v-box.cn) up the design. It does not support Converse APIs and [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2684771) other [Amazon Bedrock](https://deadlocked.wiki) tooling. +2. Filter for DeepSeek as a company and pick the DeepSeek-R1 model.
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The model detail page provides vital details about the design's abilities, pricing structure, and execution standards. You can discover detailed use guidelines, consisting of sample API calls and [code snippets](https://talentrendezvous.com) for integration. The model supports numerous text generation jobs, including content creation, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT reasoning abilities. +The page also consists of deployment choices and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, choose Deploy.
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You will be prompted to set up the [implementation details](https://cariere.depozitulmax.ro) for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, go into a variety of instances (between 1-100). +6. For example type, select your circumstances 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 personal cloud (VPC) networking, service role permissions, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production deployments, you might wish to review these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the implementation is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive user interface where you can try out different prompts and change model parameters like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For example, content for inference.
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This is an outstanding method to explore the design's thinking and text generation capabilities before integrating it into your applications. The playground supplies immediate feedback, helping you comprehend how the design reacts to numerous inputs and letting you fine-tune your prompts for optimum results.
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You can rapidly evaluate the design in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run [reasoning](http://tv.houseslands.com) utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference using 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 developed the guardrail, utilize the following code to carry out [guardrails](https://gogs.adamivarsson.com). The script initializes the bedrock_runtime client, sets up inference criteria, and sends a request to create [text based](http://devhub.dost.gov.ph) on 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, built-in algorithms, and prebuilt ML services that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through [SageMaker JumpStart](https://gitlab.alpinelinux.org) offers 2 practical approaches: using the instinctive SageMaker JumpStart UI or [implementing programmatically](https://xajhuang.com3100) through the [SageMaker Python](https://healthcarejob.cz) SDK. Let's explore both approaches to help you select the technique that best matches your needs.
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Deploy DeepSeek-R1 through [SageMaker JumpStart](https://git.dev-store.xyz) UI
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Complete the following steps to [release](http://hmkjgit.huamar.com) 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 develop a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model internet browser shows available models, with details like the supplier name and model capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card shows crucial details, consisting of:
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- Model name +- [Provider](https://pompeo.com) name +- Task category (for example, Text Generation). +Bedrock Ready badge (if relevant), suggesting that this design can be signed up with Amazon Bedrock, allowing you to use 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 design details page consists of the following details:
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- The model name and company details. +Deploy button to release 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 deploy the model, it's recommended to review the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the automatically generated name or produce a custom-made one. +8. For example type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the variety of instances (default: 1). +Selecting appropriate circumstances types and counts is essential for expense and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is [enhanced](http://47.113.125.2033000) for [sustained traffic](https://git.esc-plus.com) and low latency. +10. Review all setups for accuracy. For this model, we strongly suggest sticking to SageMaker JumpStart [default settings](https://git.pandaminer.com) and making certain that network seclusion remains in location. +11. Choose Deploy to release the design.
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The deployment process can take numerous minutes to complete.
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When release is total, your endpoint status will alter to InService. At this moment, the model is prepared to accept inference demands through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is total, you can conjure up the design utilizing a SageMaker runtime client and [disgaeawiki.info](https://disgaeawiki.info/index.php/User:MorrisSherrill8) integrate it with your [applications](http://git.szmicode.com3000).
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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 require to install the SageMaker Python SDK and make certain you have the needed AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the design is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run additional 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, [it-viking.ch](http://it-viking.ch/index.php/User:OsvaldoHildebran) you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the [Amazon Bedrock](https://git.dev.hoho.org) console or the API, and implement it as revealed in the following code:
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Clean up
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To prevent unwanted charges, finish the actions in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you deployed the design utilizing Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace deployments. +2. In the Managed releases area, find the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the correct 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 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](https://uspublicsafetyjobs.com) and Resources.
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Conclusion
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In this post, we explored how you can access and release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, refer to Use [Amazon Bedrock](http://gitlab.ds-s.cn30000) tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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://somo.global) companies construct innovative solutions using AWS services and accelerated calculate. Currently, he is focused on [establishing techniques](https://iamzoyah.com) for fine-tuning and enhancing the reasoning efficiency of big [language](https://sossdate.com) models. In his spare time, Vivek takes pleasure in hiking, viewing films, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.paknaukris.pro) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://www.radioavang.org) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://careers.midware.in) with the Third-Party Model Science group 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](https://gitea.evo-labs.org) hub. She is enthusiastic about building solutions that help consumers accelerate their [AI](http://tktko.com:3000) journey and unlock organization worth.
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