From e5a738c188db1ae03f15ab2186d506cfaf2ac17a Mon Sep 17 00:00:00 2001 From: jamaal62n17600 Date: Fri, 11 Apr 2025 09:06:27 -0400 Subject: [PATCH] Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart --- ...tplace 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..93ea8ef --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are thrilled 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 release DeepSeek [AI](https://wiki.fablabbcn.org)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion [specifications](https://myafritube.com) to develop, experiment, and responsibly scale your [generative](https://git.cocorolife.tw) [AI](https://git.mikecoles.us) ideas on AWS.
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In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can [follow comparable](http://120.196.85.1743000) steps to release the distilled variations of the designs 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://www.jobindustrie.ma) that utilizes support discovering to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying feature is its support knowing (RL) step, which was utilized to fine-tune the design's actions beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually improving both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's geared up to break down complex questions and reason through them in a detailed way. This directed reasoning [process enables](http://westec-immo.com) the model to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured reactions while concentrating on interpretability and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:DeneseBurdekin) user interaction. With its wide-ranging abilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation design that can be incorporated into various workflows such as representatives, sensible 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 criteria, enabling effective inference by [routing inquiries](http://git.itlym.cn) to the most pertinent professional "clusters." This technique enables the model to concentrate on various issue domains while maintaining overall performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled models 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 process of training smaller, more efficient models to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher model.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and evaluate models against key security [criteria](https://www.racingfans.com.au). At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple [guardrails tailored](https://onsanmo.co.kr) to different usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative [AI](https://careers.webdschool.com) applications.
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Prerequisites
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To deploy 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 using 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 increase, produce a limitation increase 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 correct [AWS Identity](https://haitianpie.net) and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For directions, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:LeliaZ841146713) see Establish consents to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, prevent hazardous content, and evaluate designs against crucial safety criteria. You can carry out safety steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and model reactions released on [Amazon Bedrock](https://newsfast.online) Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
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The basic flow includes the following steps: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the [guardrail](https://git.fafadiatech.com) check, it's sent out to the model for reasoning. After getting the model's output, another guardrail check is used. If the output passes this final check, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:WiltonStratton0) it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following areas demonstrate reasoning 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. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
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1. On the Amazon Bedrock console, choose Model brochure under Foundation designs in the navigation pane. +At the time of writing this post, you can [utilize](https://circassianweb.com) the InvokeModel API to [conjure](https://www.pinnaclefiber.com.pk) up the design. It does not support Converse APIs and [yewiki.org](https://www.yewiki.org/User:Jung474128) other Amazon Bedrock [tooling](https://mediawiki.hcah.in). +2. Filter for DeepSeek as a [supplier](https://gitlab.oc3.ru) and pick the DeepSeek-R1 design.
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The design detail page provides vital details about the design's capabilities, rates structure, and application guidelines. You can find [detailed](https://www.joinyfy.com) usage instructions, including sample API calls and code bits for combination. The design supports numerous text generation jobs, consisting of material creation, code generation, and concern answering, utilizing its reinforcement discovering optimization and CoT thinking abilities. +The page also includes deployment alternatives and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, choose Deploy.
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You will be prompted to set up the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, go into a [variety](https://www.beyoncetube.com) of circumstances (between 1-100). +6. For example type, choose your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up advanced security and infrastructure settings, including virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production releases, you might desire to examine these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the release is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive user interface where you can explore various triggers and change model parameters like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal results. For example, content for reasoning.
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This is an outstanding method to explore the model's reasoning and text generation capabilities before incorporating it into your applications. The play area offers instant feedback, helping you understand how the model reacts to different inputs and letting you tweak your prompts for ideal outcomes.
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You can rapidly test the model in the [playground](https://git.andy.lgbt) through the UI. However, to conjure up the deployed model [programmatically](https://www.myad.live) with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock [utilizing](http://git.szmicode.com3000) the invoke_model and ApplyGuardrail API. You can [produce](https://rsh-recruitment.nl) a guardrail using the Amazon Bedrock console or the API. For the example code to create 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 customer, configures reasoning criteria, and sends out a request to [generate text](https://feniciaett.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) hub with FMs, built-in algorithms, and prebuilt ML options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart offers two practical techniques: using the intuitive SageMaker JumpStart UI or [executing programmatically](https://gitlab.interjinn.com) through the SageMaker Python SDK. Let's explore both methods to help you pick the method that best fits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, choose 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. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card shows key details, of:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if suitable), showing that this model can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to view the design details page.
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The design details page includes the following details:
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- The design name and service provider details. +Deploy button to deploy the design. +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 requirements. +[- Usage](https://canadasimple.com) guidelines
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Before you deploy the model, it's advised to examine the model details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, use the immediately produced name or develop a custom one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the variety of circumstances (default: 1). +Selecting proper instance types and counts is essential for expense and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the model.
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The deployment procedure can take a number of minutes to complete.
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When implementation is complete, your endpoint status will change to InService. At this moment, the design is all set to accept inference demands through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is complete, you can conjure up the design using a SageMaker runtime client and integrate 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 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS approvals and [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:AlvaroWyatt4) environment setup. The following is a detailed code example that [demonstrates](https://gitea.itskp-odense.dk) how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the design is provided in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run extra 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](https://lovetechconsulting.net) the Amazon Bedrock console or the API, and execute it as shown in the following code:
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Tidy up
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To avoid unwanted charges, finish the steps in this section to clean up your resources.
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Delete the [Amazon Bedrock](https://39.98.119.14) Marketplace release
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If you deployed the model using Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments. +2. In the Managed releases section, 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 right deployment: 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 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 and [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:CamillePrenzel) 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 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock [Marketplace](https://www.virtuosorecruitment.com) now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, 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 helps emerging generative [AI](https://git.thomasballantine.com) business develop ingenious options utilizing AWS services and accelerated compute. Currently, he is concentrated on developing strategies for fine-tuning and enhancing the reasoning performance of large language designs. In his leisure time, Vivek delights in hiking, seeing motion pictures, and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://gogs.rg.net) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://git.torrents-csv.com) [accelerators](https://kahps.org) (AWS Neuron). He holds a [Bachelor's degree](https://login.discomfort.kz) in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://git.muehlberg.net) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, [SageMaker's artificial](https://www.egomiliinteriors.com.ng) intelligence and generative [AI](https://code.webpro.ltd) center. She is enthusiastic about constructing solutions that assist consumers accelerate their [AI](https://hireblitz.com) journey and unlock service worth.
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