From b43a4ed06353d2e89decb9bcd4f5ab40db8f8590 Mon Sep 17 00:00:00 2001 From: landonsheehan2 Date: Thu, 6 Mar 2025 09:25:40 +0000 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..ce6e850 --- /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.didi.la)'s [first-generation frontier](https://git.zyhhb.net) model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion specifications to build, experiment, and properly scale your generative [AI](https://younetwork.app) 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 similar steps to release the distilled versions of the designs also.
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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://academy.theunemployedceo.org) that uses support learning to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3[-Base foundation](http://www.xn--739an41crlc.kr). A crucial identifying feature is its reinforcement learning (RL) step, which was utilized to improve the design's responses beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually improving both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, suggesting it's equipped to break down complicated questions and factor through them in a detailed manner. This assisted thinking procedure enables the design to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured [actions](https://letsstartjob.com) while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation design that can be incorporated into different workflows such as agents, sensible [thinking](https://24frameshub.com) and data analysis tasks.
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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 specifications, making it possible for effective inference by [routing](http://124.71.40.413000) queries to the most relevant expert "clusters." This approach permits the design to specialize in various issue 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 utilize an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:FelipaH487724631) 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective designs to imitate the habits and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as an instructor 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 advise deploying this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and evaluate models against crucial security requirements. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://beta.talentfusion.vn) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, 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, choose Amazon SageMaker, and validate you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limitation boost, develop a limit boost request 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 and [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:CoyFreehill) Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For guidelines, see Set up 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 introduce safeguards, avoid damaging content, and [evaluate models](https://kaykarbar.com) against key safety criteria. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and design responses 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 general flow involves the following actions: First, the system receives an input for the model. This input is then processed through the [ApplyGuardrail API](https://git.profect.de). 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 final check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas show reasoning using 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, pick Model catalog under Foundation models in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and select the DeepSeek-R1 design.
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The model detail page provides essential details about the design's capabilities, prices structure, and execution standards. You can find detailed use guidelines, consisting of sample API calls and code snippets for combination. The model supports numerous text generation jobs, including material production, code generation, and concern answering, using its reinforcement finding out optimization and CoT reasoning abilities. +The page likewise consists of release options and licensing details to assist you start with DeepSeek-R1 in your [applications](https://play.sarkiniyazdir.com). +3. To begin utilizing DeepSeek-R1, pick Deploy.
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You will be triggered to set up the release details for DeepSeek-R1. The model ID will be [pre-populated](http://47.114.187.1113000). +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, get in a number of instances (between 1-100). +6. For example type, select your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can configure innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role consents, and encryption settings. For a lot of use cases, the default settings will work well. However, for production implementations, you may desire to examine these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin using the design.
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When the release is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive user interface where you can experiment with various prompts and change model parameters like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For instance, content for inference.
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This is an outstanding way to explore the model's reasoning and text generation abilities before incorporating it into your applications. The play area provides immediate feedback, helping you comprehend how the design reacts to different inputs and letting you fine-tune your triggers for optimum outcomes.
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You can rapidly evaluate the model in the play area through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you [require](http://dibodating.com) 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 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 develop the guardrail, see the GitHub repo. After you have [produced](https://gitea.adminakademia.pl) the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends out a demand to create [text based](https://clearcreek.a2hosted.com) 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, integrated algorithms, and prebuilt ML services that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 practical approaches: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to help you select the technique 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 using 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 produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the [navigation pane](https://alldogssportspark.com).
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The model web browser [displays](https://gitlab.tncet.com) available models, with details like the [provider](https://myjobasia.com) name and design capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card shows key details, consisting of:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if applicable), indicating that this model can be [registered](http://forum.ffmc59.fr) with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the design
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5. Choose the design card to view the model details page.
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The model details page includes the following details:
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- The model name and supplier details. +[Deploy button](https://git.elder-geek.net) to deploy the design. +About and Notebooks tabs with [detailed](https://zikorah.com) details
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The About tab includes essential details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage standards
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Before you deploy the design, it's suggested to examine the model details and license terms to confirm 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 instantly produced name or produce a custom-made one. +8. For Instance type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of circumstances (default: 1). +Selecting proper instance types and counts is vital for cost and [efficiency optimization](https://gitlab.isc.org). Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to release the model.
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The implementation procedure can take several minutes to finish.
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When implementation is total, your endpoint status will alter to InService. At this point, the design is ready to accept reasoning demands through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is total, you can invoke the design utilizing a SageMaker runtime customer and incorporate it with your applications.
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Deploy DeepSeek-R1 utilizing 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 Python SDK and make certain you have the essential AWS consents and environment setup. The following is a detailed code example that demonstrates how to deploy and [utilize](https://www.ayurjobs.net) DeepSeek-R1 for [reasoning programmatically](https://wino.org.pl). The code for releasing the design is supplied in the Github here. You can clone the notebook and range 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 [execute](https://pk.thehrlink.com) it as shown in the following code:
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Tidy up
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To prevent unwanted charges, complete the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the model 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, choose Marketplace implementations. +2. In the Managed deployments area, locate the endpoint you desire to delete. +3. Select the endpoint, and on the [Actions](http://120.24.186.633000) menu, pick 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 model you deployed will [sustain expenses](https://rapostz.com) 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 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 using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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](https://afacericrestine.ro) [AI](https://foris.gr) business build ingenious options utilizing AWS services and accelerated compute. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning efficiency of large language designs. In his downtime, Vivek delights in hiking, watching movies, and trying various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://media.labtech.org) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://2t-s.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://hitechjobs.me) 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://47.107.80.236:3000) hub. She is enthusiastic about building options that help clients accelerate their [AI](https://vtuvimo.com) journey and unlock company worth.
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