From 754ddff1ea25f08a92e88a9968c00e2e54c3f416 Mon Sep 17 00:00:00 2001 From: klfmari8957062 Date: Tue, 8 Apr 2025 05:57:09 +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..d1decb4 --- /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 Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://almagigster.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative [AI](https://learninghub.fulljam.com) ideas on AWS.
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In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled variations of the models too.
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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://score808.us) that uses [support discovering](https://gochacho.com) to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential differentiating feature is its support learning (RL) action, which was utilized to improve the model's actions beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, suggesting it's equipped to break down complex questions and factor through them in a detailed way. This guided thinking procedure permits the design to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has caught the industry's attention as a [flexible text-generation](https://cvbankye.com) design that can be integrated into numerous workflows such as agents, sensible thinking and data analysis tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient reasoning by routing questions to the most pertinent expert "clusters." This method permits the design to focus on various problem domains while maintaining general effectiveness. DeepSeek-R1 needs 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 design. 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 model to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more effective designs to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 design, [utilizing](http://47.112.158.863000) 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 suggest deploying this design with guardrails in place. In this blog, we will [utilize Amazon](https://www.eruptz.com) Bedrock Guardrails to present safeguards, prevent damaging material, and evaluate designs against crucial safety requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative [AI](http://115.238.48.210:9015) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and [validate](https://praca.e-logistyka.pl) 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 releasing. To request a limitation increase, develop a [limitation increase](https://usvs.ms) 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 appropriate AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For guidelines, see Establish permissions to use guardrails for [material filtering](https://gitlab.edebe.com.br).
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to introduce safeguards, prevent hazardous material, and assess models against crucial safety requirements. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to examine user inputs and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:KarlWinston60) design actions released 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 [produce](https://zenithgrs.com) 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 design's output, another guardrail check is applied. If the output passes this final check, it's [returned](https://dev.clikviewstorage.com) 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 stage. 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 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 utilize the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 model.
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The design detail page offers vital details about the design's abilities, pricing structure, and implementation guidelines. You can discover detailed use directions, [including sample](https://willingjobs.com) API calls and code snippets for combination. The model supports various text generation tasks, consisting of material development, code generation, and question answering, using its support learning optimization and CoT thinking capabilities. +The page also includes implementation choices and licensing details to assist you get started with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, pick 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 circumstances, get in a number of circumstances (between 1-100). +6. For example type, choose your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can configure advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service role authorizations, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production deployments, you may want to examine these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start using the design.
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When the implementation is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive interface where you can explore different triggers and change model specifications like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For instance, content for reasoning.
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This is an exceptional method to explore the design's thinking and text generation abilities before integrating it into your applications. The play area offers immediate feedback, assisting you comprehend how the model reacts to different inputs and letting you tweak your prompts for ideal outcomes.
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You can rapidly evaluate the design in the play ground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require 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 demonstrates how to perform inference using a released DeepSeek-R1 model through Amazon Bedrock [utilizing](https://git.silasvedder.xyz) the invoke_model and ApplyGuardrail API. You can create 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 actually developed the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends out a request to create text based on 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](https://www.buzzgate.net) ML options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides two practical approaches: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both [techniques](http://59.110.125.1643062) to help you choose the method that best matches 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 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the [navigation pane](http://company-bf.com). +2. First-time users will be triggered to [develop](https://zurimeet.com) a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model browser shows available designs, with details like the provider name and model abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card reveals crucial details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if applicable), showing that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the [model card](https://gogs.sxdirectpurchase.com) to view the model details page.
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The model 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 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 design, it's suggested to review the design details and license terms to verify 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](https://gogs.les-refugies.fr) the immediately created name or produce a custom one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of circumstances (default: 1). +Selecting appropriate instance types and counts is important for expense and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to [release](https://code.dev.beejee.org) the design.
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The release process can take several minutes to complete.
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When implementation is total, your endpoint status will alter to InService. At this point, the design is prepared to accept reasoning requests through the endpoint. You can monitor the [release development](https://kahkaham.net) on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is total, 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 [utilizing](https://social.nextismyapp.com) the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the required AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and [utilize](http://104.248.138.208) DeepSeek-R1 for reasoning programmatically. The code for deploying the model is [offered](https://git.polycompsol.com3000) in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run [additional](https://www.laciotatentreprendre.fr) [demands](https://livy.biz) 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 [utilize](https://tangguifang.dreamhosters.com) 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 shown in the following code:
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Tidy up
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To prevent unwanted charges, finish the actions in this section to clean up your resources.
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Delete the [Amazon Bedrock](https://www.securityprofinder.com) Marketplace release
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If you deployed the model using Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace implementations. +2. In the Managed releases section, locate the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the proper 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 released will sustain costs if you leave it [running](http://101.42.41.2543000). 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 deploy the DeepSeek-R1 model utilizing 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 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 assists emerging generative [AI](https://git.eugeniocarvalho.dev) business develop ingenious services utilizing AWS services and accelerated compute. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the reasoning efficiency of large language models. In his spare time, Vivek delights in hiking, [viewing motion](https://git.sortug.com) pictures, and attempting different foods.
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[Niithiyn Vijeaswaran](https://git.silasvedder.xyz) is a [Generative](https://video.emcd.ro) [AI](https://wiki.whenparked.com) Specialist Solutions Architect with the Third-Party [Model Science](https://duyurum.com) group at AWS. His area of focus is AWS [AI](https://linked.aub.edu.lb) accelerators (AWS Neuron). He holds a [Bachelor's degree](http://47.98.190.109) in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://crossborderdating.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://47.98.190.109) center. She is enthusiastic about developing options that help consumers accelerate their [AI](https://twitemedia.com) journey and unlock service worth.
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