From 22453e38b6bd048821cfa1b50dac94f797c3dd82 Mon Sep 17 00:00:00 2001 From: Aileen Hunley Date: Thu, 6 Mar 2025 03:09:17 +0100 Subject: [PATCH] Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-And-Amazon-SageMaker-JumpStart.md | 93 +++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md 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..448e33a --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://git.xantxo-coquillard.fr)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative [AI](https://trademarketclassifieds.com) concepts on AWS.
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In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can [follow comparable](http://8.136.199.333000) actions to deploy the distilled versions of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](https://www.bolsadetrabajotafer.com) that uses support discovering to boost thinking abilities through a multi-stage training process from a DeepSeek-V3[-Base foundation](https://vibefor.fun). An essential distinguishing feature is its support learning (RL) step, which was used to improve the model's responses beyond the basic pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually boosting both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, indicating it's geared up to break down complex questions and reason through them in a detailed manner. This assisted reasoning process enables the design to produce more precise, transparent, and detailed responses. This design combines [RL-based](https://paanaakgit.iran.liara.run) [fine-tuning](http://159.75.133.6720080) with CoT capabilities, aiming to produce structured [responses](https://gitlab.informicus.ru) while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation model that can be integrated into various workflows such as representatives, logical thinking and data analysis jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion parameters, enabling efficient inference by routing inquiries to the most appropriate professional "clusters." This method permits the design to specialize in different issue domains while maintaining total efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 [xlarge instance](https://dsspace.co.kr) to deploy the model. 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 reasoning abilities of the main R1 design to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more efficient designs to mimic the behavior and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as a teacher design.
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You can release 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 site, we will [utilize Amazon](https://git.lewis.id) Bedrock Guardrails to introduce safeguards, avoid hazardous content, and examine designs against essential security requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce multiple guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, [improving](https://gitlab.tiemao.cloud) user experiences and standardizing security controls across your generative [AI](https://agalliances.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you require access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose 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 limitation boost, produce a limit boost demand and connect 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 use Amazon Bedrock Guardrails. For directions, see Set up consents to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, avoid hazardous material, and assess designs against key safety requirements. You can carry out security procedures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to examine user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create 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](http://gnu5.hisystem.com.ar). If the input passes the guardrail check, it's sent out to the model for [reasoning](http://git.storkhealthcare.cn). After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the last 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 took place at the input or output stage. 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 provides you access to over 100 popular, emerging, and specialized structure designs (FMs) through [Amazon Bedrock](https://git.chocolatinie.fr). To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, choose Model catalog under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.
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The model detail page supplies essential [details](https://owangee.com) about the design's abilities, rates structure, and execution standards. You can find detailed use directions, including sample API calls and code snippets for combination. The design supports numerous text generation tasks, consisting of material development, code generation, and concern answering, utilizing its support learning optimization and CoT thinking capabilities. +The page likewise includes release options and licensing details to help you get going with DeepSeek-R1 in your [applications](http://81.71.148.578080). +3. To begin utilizing DeepSeek-R1, pick 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, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, go into a variety of circumstances (between 1-100). +6. For Instance type, choose your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure innovative security and infrastructure settings, [consisting](https://jobedges.com) of virtual private cloud (VPC) networking, service role approvals, [wiki.whenparked.com](https://wiki.whenparked.com/User:Bernadette71H) and file encryption settings. For many use cases, the default settings will work well. However, for releases, you might desire to evaluate these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the deployment is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive user interface where you can explore various prompts and change model specifications like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For example, material for inference.
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This is an exceptional method to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The play area supplies instant feedback, helping you understand how the model reacts to various inputs and letting you fine-tune your prompts for optimal outcomes.
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You can quickly evaluate the design in the play ground 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 [released](https://paanaakgit.iran.liara.run) DeepSeek-R1 endpoint
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The following code example shows how to carry out inference using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures inference 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) center with FMs, built-in algorithms, and prebuilt ML solutions that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers two practical techniques: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you select the approach that best suits your [requirements](https://git.uzavr.ru).
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be prompted to [produce](https://flowndeveloper.site) a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design web browser displays available models, with details like the provider name and [design capabilities](https://careerjunction.org.in).
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card reveals key details, consisting of:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if suitable), showing that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the design
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5. Choose the design card to see the design details page.
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The model details page includes the following details:
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- The model name and provider details. +Deploy button to release the model. +About and Notebooks tabs with detailed details
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The About tab consists of important details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage guidelines
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Before you release the model, it's recommended to examine the design details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, utilize the immediately created name or create a custom one. +8. For Instance type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the number of instances (default: 1). +Selecting proper [circumstances types](https://www.ayc.com.au) and counts is vital for expense and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is [enhanced](https://www.cvgods.com) for sustained traffic and low latency. +10. Review all configurations for accuracy. 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 implementation process can take numerous minutes to complete.
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When release is complete, your [endpoint status](https://www.facetwig.com) will alter to [InService](http://personal-view.com). At this point, the design is ready to accept reasoning demands through the endpoint. You can [monitor](https://titikaka.unap.edu.pe) the deployment progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the [implementation](https://git.tbaer.de) is complete, you can invoke the model using a [SageMaker runtime](https://git.mbyte.dev) client and incorporate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get started with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the [SageMaker](https://www.usbstaffing.com) Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a [detailed](https://47.100.42.7510443) code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the design is offered in the Github here. You can clone the notebook and run 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed 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 tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the design using Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace releases. +2. In the Managed releases area, locate the endpoint you desire 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 appropriate 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](https://172.105.135.218). 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 release the DeepSeek-R1 design using Bedrock Marketplace and [SageMaker](https://git.howdoicomputer.lol) [JumpStart](http://47.104.6.70). Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, 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 for Inference at AWS. He assists emerging generative [AI](https://charmyajob.com) business build innovative solutions using AWS services and accelerated compute. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the inference efficiency of large language models. In his leisure time, Vivek delights in treking, seeing films, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://tnrecruit.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://git.mxr612.top) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](http://gsrl.uk) with the Third-Party Model 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://www.racingfans.com.au) center. She is passionate about developing services that assist consumers accelerate their [AI](http://110.41.19.141:30000) journey and unlock organization value.
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