commit dc79d0227484108f82eb85f8dc206b2095728cab Author: cassandrachile Date: Fri Apr 11 07:53:19 2025 +0000 Update '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..1bb4f4f --- /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 and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://play.future.al)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative [AI](http://47.100.72.85:3000) [concepts](http://121.28.134.382039) on AWS.
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In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled variations of the models also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](https://investsolutions.org.uk) that utilizes support finding out to enhance reasoning [abilities](https://jobedges.com) through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial differentiating feature is its support learning (RL) action, which was [utilized](http://forum.rcsubmarine.ru) to refine the model's actions beyond the standard pre-training and tweak procedure. By including RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, eventually enhancing both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, meaning it's equipped to break down complex queries and factor through them in a detailed manner. This directed reasoning procedure permits the model to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually captured the industry's attention as a flexible text-generation model that can be integrated into [numerous workflows](https://www.fionapremium.com) such as agents, rational thinking and information interpretation jobs.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion criteria, making it possible for efficient inference by routing questions to the most appropriate expert "clusters." This method permits the design to concentrate on various issue domains while maintaining general [effectiveness](https://116.203.22.201). DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge comes with 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 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, more effective models to simulate the behavior and thinking patterns of the larger DeepSeek-R1 model, [utilizing](https://xn--9m1bq6p66gu3avit39e.com) it as an instructor model.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and evaluate designs against crucial safety requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative [AI](https://git.chir.rs) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limit boost, produce a limitation boost request and reach out to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) approvals to [utilize Amazon](http://sdongha.com) Bedrock Guardrails. For directions, see Establish permissions 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 safeguards, prevent hazardous material, and evaluate models against essential security criteria. You can implement precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and model actions 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](http://git.motr-online.com) the guardrail, see the [GitHub repo](https://ayjmultiservices.com).
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The general flow includes the following steps: 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 model for inference. After receiving the model's output, another guardrail check is used. If the output passes this last 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 happened at the input or output phase. The examples showcased in the following sections show inference 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 structure models (FMs) through [Amazon Bedrock](http://194.87.97.823000). To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, select Model catalog under Foundation models in the [navigation](https://nuswar.com) pane. +At the time of composing this post, you can use the InvokeModel API to conjure up the model. 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 important details about the design's capabilities, prices structure, and implementation standards. You can discover detailed usage guidelines, including sample API calls and code bits for combination. The design supports different [text generation](https://satitmattayom.nrru.ac.th) jobs, including [material](https://hotjobsng.com) creation, code generation, and concern answering, utilizing its support discovering optimization and CoT reasoning capabilities. +The page also consists of deployment choices and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, select Deploy.
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You will be prompted to configure the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, go into a variety of circumstances (in between 1-100). +6. For example type, select your [instance type](https://geoffroy-berry.fr). For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure innovative security and infrastructure settings, including virtual private cloud (VPC) networking, service function approvals, and file encryption settings. For many use cases, the default settings will work well. However, for production implementations, you may desire to review these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start using 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 area to access an interactive interface where you can experiment with various prompts and adjust design criteria like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For instance, material for inference.
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This is an exceptional way to explore the model's thinking and text generation abilities before integrating it into your applications. The play area provides [instant](https://smartcampus-seskoal.id) feedback, helping you understand how the design reacts to numerous inputs and letting you fine-tune your triggers for ideal results.
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You can rapidly evaluate the model in the play area through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out reasoning using a released DeepSeek-R1 model 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 produce the guardrail, see the GitHub repo. After you have [produced](http://quickad.0ok0.com) the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures reasoning parameters, and sends out a request to [produce text](https://git.aionnect.com) 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, built-in algorithms, and prebuilt ML options that you can release with simply a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://www.tqmusic.cn) designs to your usage case, with your data, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 convenient methods: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you select the technique that best suits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design web browser shows available models, with details like the provider name and design capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card shows key details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if appropriate), suggesting that this model can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model
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5. Choose the design card to see 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 model. +About and Notebooks tabs with [detailed](https://talentlagoon.com) 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 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 release.
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7. For Endpoint name, utilize the instantly generated name or create a custom one. +8. For example type ΒΈ choose an instance 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 picked by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the model.
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The deployment process can take a number of minutes to finish.
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When implementation is total, your endpoint status will alter to InService. At this point, the design is prepared to accept inference demands through the endpoint. You can keep an eye on the implementation progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation is complete, you can conjure up the design using a SageMaker runtime customer and incorporate it with your applications.
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Deploy DeepSeek-R1 using 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](https://git.andrewnw.xyz) SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for inference programmatically. The code for [pipewiki.org](https://pipewiki.org/wiki/index.php/User:ShirleenBergeron) releasing the model is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional requests against the predictor:
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[Implement guardrails](https://travel-friends.net) and run inference with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your predictor. You can produce a guardrail using the Amazon Bedrock 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, complete the actions in this section to tidy 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, total the following actions:
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1. On the Amazon Bedrock console, under [Foundation models](http://makerjia.cn3000) in the navigation pane, pick Marketplace implementations. +2. In the Managed deployments section, locate the endpoint you want to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're [deleting](https://goalsshow.com) the appropriate implementation: 1. [Endpoint](https://code.dev.beejee.org) 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 deployed will sustain expenses 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://www.paknaukris.pro) 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 get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, [SageMaker JumpStart](https://salesupprocess.it) 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](https://links.gtanet.com.br) is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://git.pawott.de) companies construct innovative services using AWS services and accelerated calculate. Currently, he is focused on establishing methods for fine-tuning and enhancing the inference performance of big language models. In his leisure time, Vivek delights in treking, enjoying films, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://tagreba.org) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His [location](http://hmind.kr) of focus is AWS [AI](https://careers.mycareconcierge.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://wiki.piratenpartei.de) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](http://119.130.113.2453000) [AI](https://supardating.com) hub. She is passionate about constructing solutions that help consumers accelerate their [AI](https://git.jerl.dev) journey and [unlock service](http://motojic.com) value.
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