From 3907766367e9a3e91f097c78af0b3d0f141e3bf3 Mon Sep 17 00:00:00 2001 From: Audrey Deitz Date: Fri, 7 Feb 2025 19:12:31 +0800 Subject: [PATCH] Add '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..d221bd6 --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://dev.yayprint.com)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your [generative](https://social1776.com) [AI](https://git.home.lubui.com:8443) ideas on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the designs too.
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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://siman.co.il) that utilizes support learning to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential identifying function is its support knowing (RL) step, which was utilized to refine the model's actions beyond the basic pre-training and fine-tuning procedure. By [including](https://homejobs.today) RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, eventually enhancing both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, suggesting it's equipped to break down complex inquiries and reason through them in a detailed way. This assisted thinking [process enables](http://mohankrishnareddy.com) the design to produce more accurate, transparent, and detailed responses. This model combines RL-based [fine-tuning](https://encone.com) with CoT capabilities, aiming to produce structured responses while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as agents, rational reasoning and information interpretation jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion criteria, enabling effective inference by routing inquiries to the most appropriate specialist "clusters." This approach permits the model to specialize in various issue domains while maintaining total effectiveness. DeepSeek-R1 requires at least 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 offering](http://udyogservices.com) 1128 GB of GPU memory.
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DeepSeek-R1 distilled models 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 procedure of training smaller sized, more efficient designs to mimic the habits and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor model.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or [Bedrock](https://www.trappmasters.com) Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with [guardrails](https://precise.co.za) in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and examine models against key security criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative [AI](https://warleaks.net) applications.
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Prerequisites
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To release the DeepSeek-R1 model, 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, pick Amazon SageMaker, and verify 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 request a limit boost, develop a limitation boost demand and connect to your account group.
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Because you will be [deploying](https://accc.rcec.sinica.edu.tw) this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) [consents](https://www.ajirazetu.tz) to use Amazon Bedrock Guardrails. For instructions, 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 enables you to introduce safeguards, prevent damaging content, and evaluate models against [key security](https://www.iwatex.com) requirements. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop 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 general circulation includes the following steps: First, the system [receives](http://appleacademy.kr) 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 receiving the design's output, another guardrail check is used. If the output passes this last check, it's returned as the outcome. However, if either the input or output is [stepped](https://www.weben.online) 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 reasoning using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, select Model brochure under Foundation designs in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 design.
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The design detail page provides important details about the model's abilities, prices structure, and execution guidelines. You can find detailed use instructions, including sample API calls and code snippets for integration. The model supports different text generation jobs, including content production, code generation, and question answering, using its reinforcement discovering optimization and CoT thinking capabilities. +The page also includes implementation alternatives and licensing details to assist you get begun with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, [select Deploy](https://git.owlhosting.cloud).
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You will be prompted to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Number of instances, go into a variety of instances (in between 1-100). +6. For example type, pick your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up advanced security and facilities settings, consisting of [virtual private](https://ravadasolutions.com) cloud (VPC) networking, service role permissions, and file encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you might wish to examine these [settings](http://49.50.103.174) to align with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the implementation is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive interface where you can explore various triggers and adjust design parameters like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For example, content for inference.
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This is an outstanding way to check out the design's reasoning and text generation abilities before integrating it into your applications. The playground provides instant feedback, assisting you understand how the model reacts to various inputs and letting you fine-tune your prompts for ideal outcomes.
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You can rapidly check the model in the play area through the UI. However, to invoke the [released model](http://www.xn--he5bi2aboq18a.com) programmatically with any APIs, you need to get the endpoint ARN.
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Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce 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 created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, configures inference criteria, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:Princess3594) and sends out a request to produce text based on a user timely.
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Deploy DeepSeek-R1 with [SageMaker](https://git.saidomar.fr) JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) hub 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 designs to your usage case, with your information, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides two 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 finest suits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to deploy DeepSeek-R1 utilizing 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 web browser displays available models, with details like the service provider name and design capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card reveals key details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if appropriate), suggesting 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 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 model. +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 specs. +- Usage standards
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Before you deploy the design, it's advised to evaluate the design details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to continue with implementation.
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7. For [Endpoint](https://gitlab.wah.ph) name, utilize the instantly created name or create a custom-made 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 and counts is important for cost and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and [low latency](http://jobsgo.co.za). +10. Review all setups for accuracy. For this design, we highly recommend sticking to SageMaker JumpStart [default settings](https://www.viewtubs.com) and making certain that network isolation remains in place. +11. Choose Deploy to release the model.
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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. At this moment, the design is all set to accept reasoning demands through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is complete, you can invoke the model using a SageMaker runtime client and incorporate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To begin 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 consents and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for deploying the model is supplied in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra requests against the predictor:
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Implement guardrails and run reasoning 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 develop a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:
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Clean up
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To prevent 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 model utilizing Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the [navigation](https://duyurum.com) pane, pick Marketplace implementations. +2. In the Managed deployments section, find the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're deleting the right 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 deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you desire 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 using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with [Amazon SageMaker](https://git.home.lubui.com8443) [JumpStart](https://nakshetra.com.np) models, SageMaker JumpStart pretrained designs, 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 is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://recrutevite.com) business build ingenious options using AWS services and accelerated calculate. Currently, he is focused on developing strategies for fine-tuning and optimizing the reasoning performance of big language models. In his [leisure](https://kiwiboom.com) time, Vivek enjoys hiking, viewing motion pictures, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://mohankrishnareddy.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His [location](http://secdc.org.cn) of focus is AWS [AI](https://cyberbizafrica.com) [accelerators](http://gpis.kr) (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](https://app.joy-match.com) with generative [AI](https://matchmaderight.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://christiancampnic.com) center. She is enthusiastic about building solutions that help consumers accelerate their [AI](http://66.85.76.122:3000) journey and unlock service worth.
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