From 974c10bf8bb5cab3e888223dc9102ede2f6303de Mon Sep 17 00:00:00 2001 From: Abdul Easterby Date: Fri, 28 Feb 2025 03:42:35 +0000 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..2b7b284 --- /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 deploy DeepSeek [AI](https://baitshepegi.co.za)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion specifications to build, experiment, and properly scale your generative [AI](https://ambitech.com.br) ideas on AWS.
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In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy 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) developed by DeepSeek [AI](https://gitea.scalz.cloud) that utilizes support finding out to enhance thinking abilities through a [multi-stage training](http://101.200.220.498001) procedure from a DeepSeek-V3-Base foundation. A crucial [differentiating feature](https://hebrewconnect.tv) is its reinforcement knowing (RL) action, which was used to fine-tune the model's actions beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, ultimately improving both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, indicating it's geared up to break down complex queries and reason through them in a detailed manner. This guided reasoning procedure enables the design to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as agents, logical reasoning and data interpretation 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 criteria, making it possible for effective reasoning by routing questions to the most pertinent specialist "clusters." This [method enables](https://one2train.net) the model to focus on various problem domains while maintaining general effectiveness. DeepSeek-R1 needs 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 to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 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 models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more efficient designs to imitate the behavior and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as a teacher design.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an [emerging](https://gitea.malloc.hackerbots.net) design, we suggest releasing this model with guardrails in place. In this blog, we will utilize Amazon [Bedrock](http://106.55.234.1783000) Guardrails to introduce safeguards, prevent hazardous material, and evaluate designs against key safety criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative [AI](https://wavedream.wiki) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm you're utilizing 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 request a limit boost, produce a limitation boost request and reach out to your account team.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For directions, see Establish approvals 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 present safeguards, avoid harmful content, and examine models against key security requirements. You can carry out safety steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a [guardrail utilizing](https://surreycreepcatchers.ca) 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 involves the following steps: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the design for reasoning. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the last outcome. 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 stage. The examples showcased in the following areas show reasoning utilizing 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 structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, choose Model brochure under Foundation models in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 design.
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The model detail page supplies essential details about the model's abilities, rates structure, and implementation standards. You can find detailed use guidelines, including sample API calls and code snippets for combination. The model supports numerous text generation tasks, consisting of content development, code generation, and concern answering, using its support learning optimization and CoT reasoning abilities. +The page likewise includes deployment options and [89u89.com](https://www.89u89.com/author/cristinevan/) licensing details to assist you get begun with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, [pick Deploy](http://1138845-ck16698.tw1.ru).
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You will be prompted to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, enter a variety of instances (in between 1-100). +6. For Instance type, pick your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you may desire 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 complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in playground to access an interactive interface where you can experiment with different triggers and change design parameters like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal results. For example, material for inference.
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This is an exceptional way to explore the model's thinking and text generation capabilities before integrating it into your applications. The play area offers instant feedback, assisting you comprehend how the model reacts to different inputs and letting you fine-tune your prompts for optimum results.
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You can quickly check the model in the play area through the UI. However, to [conjure](http://121.37.208.1923000) up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning using guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce 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 developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, and sends out a demand to create 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) hub with FMs, integrated algorithms, and prebuilt ML services that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:Homer93G479471) pre-trained models 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 model through SageMaker JumpStart provides two hassle-free approaches: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you choose the technique that finest suits your [requirements](https://partyandeventjobs.com).
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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, pick 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](http://gitea.anomalistdesign.com).
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The model web browser shows available designs, with details like the supplier name and design capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card reveals crucial details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if suitable), suggesting that this model can be registered with Amazon Bedrock, enabling you to use [Amazon Bedrock](https://wiki.sublab.net) APIs to invoke the design
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5. Choose the model card to see the model details page.
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The model details page includes the following details:
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- The design name and supplier details. +Deploy button to deploy 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 specifications. +- Usage standards
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Before you deploy the design, it's advised to examine the model details and license terms to validate compatibility with your use case.
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6. Choose Deploy to proceed with implementation.
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7. For Endpoint name, utilize the immediately generated name or produce a customized one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For [Initial instance](http://122.51.17.902000) count, go into the variety of circumstances (default: 1). +Selecting suitable [instance](http://gitlab.kci-global.com.tw) types and counts is crucial for expense and [performance optimization](https://wiki.vifm.info). Monitor your release to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for [precision](https://jobsubscribe.com). For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the design.
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The release process can take numerous minutes to complete.
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When [deployment](https://sso-ingos.ru) is total, your endpoint status will alter to InService. At this moment, the design is prepared to accept inference demands through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the [release](http://www.hanmacsamsung.com) is complete, you can invoke the model utilizing a SageMaker runtime client and integrate it with your applications.
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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 set up the SageMaker Python SDK and make certain you have the necessary AWS approvals 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 [releasing](https://agapeplus.sg) the model is provided in the Github here. You can clone the notebook and run from .
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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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can [develop](https://udyogseba.com) a guardrail utilizing the Amazon Bedrock console or the API, [wakewiki.de](https://www.wakewiki.de/index.php?title=Benutzer:AlexWoolnough3) and execute it as revealed in the following code:
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Clean up
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To avoid unwanted charges, complete the steps in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the design utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases. +2. In the Managed implementations section, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, [ratemywifey.com](https://ratemywifey.com/author/fletawiese/) choose Delete. +4. Verify the endpoint details to make certain you're erasing the appropriate 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 model you deployed will sustain costs if you leave it [running](http://wrgitlab.org). Use the following code to delete the [endpoint](https://fromkorea.kr) if you wish 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](http://team.pocketuniversity.cn) and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart [pretrained](http://git.baobaot.com) 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 [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:WiltonOLeary84) Inference at AWS. He assists emerging generative [AI](https://git.fracturedcode.net) companies construct innovative options using AWS services and accelerated calculate. Currently, he is concentrated on establishing methods for [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2684771) fine-tuning and optimizing the reasoning performance of large language designs. In his downtime, Vivek enjoys hiking, seeing movies, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://minka.gob.ec) Specialist Solutions [Architect](http://106.55.234.1783000) with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://114.55.169.15:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://www.lotusprotechnologies.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://c3tservices.ca) hub. She is [passionate](http://test.wefanbot.com3000) about building solutions that assist customers accelerate their [AI](https://www.k4be.eu) journey and unlock service value.
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