From 33962db0c94a8089c1bf5cd2e5b54a72bd0cb8de Mon Sep 17 00:00:00 2001 From: adelakater4814 Date: Thu, 6 Feb 2025 13:31:27 +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..299499a --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are delighted to reveal that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and [Amazon SageMaker](https://dev.yayprint.com) JumpStart. With this launch, you can now release DeepSeek [AI](https://lab.chocomart.kz)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](http://www.vokipedia.de) concepts 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 steps to deploy the [distilled variations](https://textasian.com) of the models also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](http://www.szkis.cn:13000) that utilizes reinforcement finding out to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3[-Base foundation](https://git.unicom.studio). A key identifying feature is its reinforcement learning (RL) action, which was utilized to refine the model's reactions beyond the basic pre-training and tweak process. By [integrating](https://ifairy.world) RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually enhancing both [relevance](http://www.haimimedia.cn3001) and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, meaning it's equipped to break down intricate queries and reason through them in a detailed way. This assisted thinking process permits the design to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured responses while concentrating on [interpretability](https://grailinsurance.co.ke) and user interaction. With its comprehensive abilities DeepSeek-R1 has actually caught the market's attention as a flexible text-generation model that can be integrated into various 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 allows activation of 37 billion parameters, making it possible for efficient reasoning by routing questions to the most pertinent professional "clusters." This technique allows the model to specialize in various problem domains while maintaining general performance. DeepSeek-R1 needs at least 800 GB of [HBM memory](https://svn.youshengyun.com3000) in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the [thinking capabilities](https://www.unotravel.co.kr) of the main R1 design to more effective architectures based upon popular open [designs](https://dessinateurs-projeteurs.com) like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more efficient designs to mimic the habits and [thinking patterns](https://git.sunqida.cn) of the bigger DeepSeek-R1 design, [utilizing](https://gogs.greta.wywiwyg.net) it as a teacher model.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or [Bedrock Marketplace](https://www.uaelaboursupply.ae). Because DeepSeek-R1 is an emerging model, we suggest deploying this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and assess designs against crucial safety requirements. At the time of composing this blog site, for DeepSeek-R1 [implementations](http://sopoong.whost.co.kr) on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](https://4stour.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and validate 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 deploying. To ask for a limit boost, create a limit increase demand and reach out to your account team.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Set up 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 material, and assess models against essential safety criteria. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables 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 basic circulation involves the following steps: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for [[empty]](https://vydiio.com/@barrettburd37?page=about) inference. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is [returned indicating](https://oliszerver.hu8010) the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following sections show inference utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation designs (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, choose Model brochure under Foundation models in the navigation pane. +At the time of composing this post, you can use 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 provides essential details about the design's capabilities, rates structure, and application guidelines. You can discover detailed use guidelines, including sample API calls and code bits for combination. The model supports different text [generation](https://sameday.iiime.net) jobs, including content production, code generation, and concern answering, utilizing its reinforcement discovering optimization and CoT reasoning abilities. +The page likewise consists of deployment choices and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, select Deploy.
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You will be prompted to configure the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, enter a variety of circumstances (in between 1-100). +6. For example type, [185.5.54.226](http://185.5.54.226/asaboucher0463) 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 configure innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production releases, you may want to examine these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to start [utilizing](https://gst.meu.edu.jo) the design.
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When the release is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in playground to access an interactive user interface where you can explore various triggers and change model parameters like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For example, content for inference.
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This is an outstanding method to explore the design's thinking and [103.77.166.198](http://103.77.166.198:3000/leticiaveitch2) text generation capabilities before incorporating it into your applications. The play ground offers instant feedback, helping you understand how the model reacts to various inputs and letting you tweak your prompts for ideal results.
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You can quickly evaluate the design in the play area through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference using guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a [guardrail utilizing](https://skytechenterprisesolutions.net) the [Amazon Bedrock](https://inspirationlift.com) console or the API. For the example code to [develop](http://media.clear2work.com.au) the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference parameters, and sends out a request to generate text based on a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial [intelligence](https://git.partners.run) (ML) hub with FMs, built-in algorithms, and prebuilt ML services 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 design through SageMaker JumpStart offers 2 hassle-free techniques: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's [explore](http://120.79.27.2323000) both approaches to assist you select the approach that best matches 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, choose Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model internet browser displays available models, with details like the supplier name and model capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card shows key details, consisting of:
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[- Model](http://git.sysoit.co.kr) name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if appropriate), suggesting that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to view the model details page.
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The design details page includes the following details:
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- The design name and supplier details. +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
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The About tab consists of essential details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage guidelines
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Before you release the model, it's recommended to review the design details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, use the automatically produced name or create a custom-made one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For [Initial circumstances](https://phoebe.roshka.com) count, enter the number of circumstances (default: 1). +Selecting suitable instance types and counts is crucial for expense and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, [Real-time inference](http://122.51.51.353000) is chosen by default. This is enhanced for [sustained traffic](http://git.liuhung.com) and low latency. +10. Review all configurations for accuracy. For this model, we highly recommend sticking to SageMaker JumpStart default settings and making certain that [network isolation](https://gitlab.edebe.com.br) remains in place. +11. Choose Deploy to [release](https://propbuysells.com) the design.
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The deployment procedure can take numerous minutes to finish.
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When release is complete, your endpoint status will change to [InService](http://colorroom.net). At this moment, the model is all set to accept reasoning requests through the endpoint. You can monitor the implementation development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is total, you can invoke the model 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 get started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS consents and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is supplied in the Github here. You can clone the notebook and run from [SageMaker Studio](http://123.56.193.1823000).
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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 also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing 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 avoid unwanted charges, complete the [actions](https://europlus.us) in this area to clean up your resources.
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Delete the [Amazon Bedrock](https://vooxvideo.com) Marketplace deployment
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If you released the model using Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases. +2. In the Managed releases area, find the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the right deployment: 1. [Endpoint](https://git.desearch.cc) 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. Use the following code to erase the endpoint if you want 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 get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, [Amazon SageMaker](https://superappsocial.com) JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going 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](http://45.55.138.82:3000) business build innovative solutions using AWS [services](http://13.209.39.13932421) and sped up compute. Currently, he is focused on developing strategies for fine-tuning and optimizing the inference efficiency of big language models. In his totally free time, Vivek enjoys treking, enjoying films, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://kittelartscollege.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://agalliances.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and [Bioinformatics](http://47.98.226.2403000).
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](http://ecoreal.kr) with the Third-Party Model Science team 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](https://ckzink.com) center. She is passionate about developing solutions that help customers accelerate their [AI](https://www.sewosoft.de) journey and unlock service worth.
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