commit 68cd58f2a72748fcc9020ab06993023ebdb08573 Author: hermelindahoug Date: Thu Feb 20 14:37:01 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..9bf9d72 --- /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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://www.ubom.com)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](http://154.209.4.10:3001) concepts on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions 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) established by DeepSeek [AI](https://filmcrib.io) that utilizes reinforcement finding out to [improve thinking](https://git.ffho.net) capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial identifying function is its [support knowing](https://git.xinstitute.org.cn) (RL) step, which was utilized to improve the model's responses beyond the standard pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately enhancing both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, suggesting it's equipped to break down complex inquiries and factor through them in a detailed way. This assisted reasoning procedure permits the model to [produce](http://39.108.93.0) more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation model that can be [integrated](http://git.moneo.lv) into various [workflows](https://tv.360climatechange.com) such as representatives, sensible reasoning and data analysis tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion specifications, allowing efficient inference by routing questions to the most relevant specialist "clusters." This method allows the design to focus on various problem domains while maintaining total efficiency. DeepSeek-R1 needs at least 800 GB of [HBM memory](http://37.187.2.253000) in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more effective 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, [raovatonline.org](https://raovatonline.org/author/jennax25174/) more effective designs to simulate the behavior and reasoning patterns of the bigger DeepSeek-R1 design, using it as a teacher design.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or [Bedrock Marketplace](http://122.51.6.973000). Because DeepSeek-R1 is an emerging model, we suggest releasing this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid damaging content, and evaluate models against key safety requirements. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails [supports](https://git.thomasballantine.com) only the ApplyGuardrail API. You can produce multiple guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your [generative](http://www.zhihutech.com) [AI](https://testgitea.educoder.net) applications.
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Prerequisites
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To release the DeepSeek-R1 design, [it-viking.ch](http://it-viking.ch/index.php/User:KristalOconner8) you need access to an ml.p5e instance. To examine 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](http://music.afrixis.com). Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limit boost, develop a [limit increase](https://git.andert.me) demand and connect to your account group.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct [AWS Identity](http://101.42.248.1083000) and Gain Access To Management (IAM) authorizations to use 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 permits you to present safeguards, prevent damaging material, and evaluate models against essential security requirements. You can execute safety measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the [GitHub repo](http://git.eyesee8.com).
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The basic [circulation involves](https://git.apps.calegix.net) 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 model for inference. After receiving the [design's](https://www.oradebusiness.eu) 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 suggesting the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following sections show reasoning 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, complete the following steps:
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1. On the Amazon Bedrock console, pick Model brochure under Foundation models in the [navigation](https://movie.nanuly.kr) 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 service provider and choose the DeepSeek-R1 design.
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The design detail page provides essential details about the design's capabilities, rates structure, and execution guidelines. You can find detailed use directions, consisting of sample API calls and code snippets for integration. The design supports different text generation jobs, including material creation, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT thinking abilities. +The page likewise consists of deployment alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, choose Deploy.
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You will be triggered to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, get in a number of circumstances (in between 1-100). +6. For Instance type, select your instance type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up sophisticated security and [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1089808) facilities settings, [gratisafhalen.be](https://gratisafhalen.be/author/napoleonfad/) consisting of virtual private cloud (VPC) networking, [service function](https://git.kuyuntech.com) consents, and file encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11969128) you may want to examine these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the release is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can explore various triggers and change model criteria like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum results. For instance, content for reasoning.
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This is an excellent method to check out the design's reasoning and text generation abilities before integrating it into your applications. The play ground provides instant feedback, assisting you comprehend how the model reacts to numerous inputs and letting you fine-tune your triggers for optimum outcomes.
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You can rapidly test the design in the playground through the UI. However, to invoke the released design [programmatically](https://wiki.lspace.org) with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to carry out inference using a released DeepSeek-R1 design through Amazon Bedrock utilizing 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 actually produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_[runtime](https://git.sicom.gov.co) client, configures reasoning criteria, and sends a demand to create text based on a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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[SageMaker JumpStart](http://update.zgkw.cn8585) is an artificial intelligence (ML) center with FMs, integrated algorithms, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1095540) and prebuilt ML options that you can [release](https://gitea.bone6.com) with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use 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 uses two hassle-free methods: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to help you choose the [technique](http://sl860.com) that best fits 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 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 create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model internet browser shows available models, with details like the supplier name and design abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card shows crucial details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this model can be registered with Amazon Bedrock, [allowing](https://codeh.genyon.cn) you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to view the model details page.
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The [design details](https://setiathome.berkeley.edu) page includes the following details:
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- The model name and supplier details. +Deploy button to [release](http://47.103.112.133) the design. +About and Notebooks tabs with detailed details
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The About tab includes important details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage standards
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Before you deploy the design, it's recommended to evaluate the [design details](http://git.huxiukeji.com) and license terms to verify compatibility with your usage case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the instantly created name or develop a customized one. +8. For [Instance type](https://say.la) ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of [circumstances](https://git.limework.net) (default: 1). +Selecting appropriate [instance types](http://103.197.204.1633025) and counts is important for cost and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this design, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the design.
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The deployment process can take several minutes to finish.
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When release is total, your endpoint status will change to [InService](https://muwafag.com). At this moment, the design is prepared to accept inference requests through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is complete, you can conjure up the model utilizing a SageMaker runtime client and integrate it with your [applications](http://47.242.77.180).
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To start with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS approvals 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 deploying the model is provided in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run additional requests 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as revealed in the following code:
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Tidy up
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To avoid unwanted charges, complete the steps in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you deployed the model using Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon [Bedrock](http://60.23.29.2133060) console, under Foundation designs in the [navigation](http://git.estoneinfo.com) pane, choose Marketplace releases. +2. In the Managed implementations area, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're [erasing](https://www.contraband.ch) the correct release: 1. name. +2. Model name. +3. Endpoint status
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Delete the SageMaker JumpStart predictor
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The [SageMaker](https://gitlab.ngser.com) JumpStart design you released will sustain costs if you leave it [running](http://gogs.funcheergame.com). 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 checked out how you can access and release the DeepSeek-R1 design utilizing 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 tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started 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 helps emerging generative [AI](https://sttimothysignal.org) business develop ingenious solutions [utilizing AWS](https://hugoooo.com) services and accelerated compute. Currently, he is concentrated on [developing methods](http://chkkv.cn3000) for fine-tuning and optimizing the inference efficiency of big language designs. In his leisure time, Vivek takes pleasure in treking, enjoying movies, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://sundaycareers.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://arbeitswerk-premium.de) 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://bd.cane-recruitment.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](http://git.jishutao.com) [AI](https://git.wisder.net) center. She is enthusiastic about developing options that help clients accelerate their [AI](https://gitlab.lycoops.be) journey and unlock company value.
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