From ff1fa51595d5dcd34d663e9cb7ffd2755c2f6aa2 Mon Sep 17 00:00:00 2001 From: averyringrose8 Date: Sat, 15 Feb 2025 08:00:18 +0800 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..0be4d7a --- /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 announce 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](http://101.132.182.101:3000)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to construct, experiment, and properly scale your generative [AI](https://local.wuanwanghao.top:3000) ideas on AWS.
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In this post, we show 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 big language model (LLM) established by DeepSeek [AI](http://osbzr.com) that utilizes support learning to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key identifying function is its reinforcement learning (RL) step, which was used to refine the model's actions beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust better to user [feedback](https://goalsshow.com) and objectives, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, implying it's geared up to break down [intricate queries](https://gamehiker.com) and reason through them in a detailed way. This guided thinking procedure allows the model to produce more accurate, transparent, and detailed answers. This [model combines](https://ai.ceo) [RL-based fine-tuning](https://municipalitybank.com) with CoT abilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation design that can be incorporated into numerous workflows such as representatives, logical thinking and data interpretation jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion specifications, making it possible for [effective](https://git.penwing.org) reasoning by routing queries to the most pertinent specialist "clusters." This the model to concentrate on different issue domains while maintaining overall performance. 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 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 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 describes a process of training smaller, more [efficient models](https://89.22.113.100) to mimic the habits and thinking patterns of the bigger 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 model, we recommend deploying this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful content, 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 numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and [pediascape.science](https://pediascape.science/wiki/User:StevieSimos301) standardizing security controls across your generative [AI](https://git.andert.me) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're using ml.p5e.48 xlarge for [endpoint](https://209rocks.com) 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 limitation increase, develop a limitation boost demand and reach out to your account group.
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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) approvals to utilize Amazon Bedrock Guardrails. For guidelines, see Establish authorizations to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, prevent harmful content, and examine models against crucial safety criteria. You can execute precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and [model actions](https://209rocks.com) released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the [Amazon Bedrock](https://gitea.chenbingyuan.com) console or the API. For the example code to create the guardrail, see the GitHub repo.
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The basic circulation includes the following actions: 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 to the model for inference. After receiving the model's output, another guardrail check is applied. If the output passes this last check, it's [returned](http://122.112.209.52) as the result. 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 occurred at the input or output phase. The examples showcased in the following sections demonstrate inference 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](https://onsanmo.co.kr). To gain access to DeepSeek-R1 in Amazon Bedrock, [raovatonline.org](https://raovatonline.org/author/roxanalechu/) total the following actions:
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1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the navigation pane. +At the time of [writing](http://code.chinaeast2.cloudapp.chinacloudapi.cn) this post, you can use the InvokeModel API to invoke the design. It doesn't [support Converse](http://git.the-archive.xyz) APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and choose the DeepSeek-R1 design.
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The design detail page supplies necessary details about the design's abilities, rates structure, and application standards. You can discover detailed use guidelines, consisting of sample API calls and code snippets for integration. The design supports various text generation tasks, including content development, code generation, and [concern](https://social.netverseventures.com) answering, using its reinforcement discovering optimization and CoT thinking capabilities. +The page also consists of release options and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, select Deploy.
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You will be triggered to configure the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, enter a number of circumstances (between 1-100). +6. For Instance type, pick your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure innovative security and facilities settings, consisting of virtual private cloud (VPC) networking, service function permissions, and file encryption settings. For a lot of use cases, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:RobbieMerion122) the default [settings](https://gitea.sb17.space) will work well. However, for [production](https://alldogssportspark.com) deployments, you may wish to review these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to start using the model.
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When the release is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive interface where you can try out various triggers and adjust model criteria like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For instance, content for inference.
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This is an excellent way to explore the model's thinking and text generation abilities before integrating it into your applications. The playground supplies immediate feedback, assisting you understand how the model responds to various inputs and letting you tweak your prompts for optimal outcomes.
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You can quickly test the model in the play ground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning using a released 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 create 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, sets up inference parameters, and sends a demand to generate 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) center with FMs, built-in algorithms, and prebuilt ML services that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs 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 provides 2 hassle-free techniques: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you pick the approach that finest matches 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 using SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be prompted to produce a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model web browser displays available designs, with details like the provider name and design capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card shows key details, including:
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- Model name +- Provider name +- Task [category](https://git.iovchinnikov.ru) (for instance, Text Generation). +[Bedrock Ready](https://poslovi.dispeceri.rs) badge (if suitable), showing that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
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5. Choose the design card to view the design 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 release the design. +About and Notebooks tabs with detailed details
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The About tab includes essential details, such as:
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[- Model](https://nse.ai) description. +- License details. +- Technical specs. +- Usage guidelines
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Before you release the design, it's recommended to evaluate the [model details](https://asromafansclub.com) and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the automatically produced name or produce a custom-made one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:TristanFlournoy) enter the number of instances (default: 1). +Selecting appropriate instance types and counts is crucial for expense and [performance optimization](https://aggeliesellada.gr). Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that [network seclusion](https://tenacrebooks.com) remains in location. +11. Choose Deploy to deploy the design.
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The deployment procedure can take numerous minutes to finish.
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When implementation is complete, your endpoint status will change to InService. At this moment, the design is prepared to accept reasoning requests through the endpoint. You can monitor the release development on the [SageMaker console](http://www5f.biglobe.ne.jp) Endpoints page, which will display relevant metrics and status details. When the release is total, you can invoke the model using a SageMaker runtime client and [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1323876) integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get begun with DeepSeek-R1 utilizing 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 shows how to release 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 additional requests 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the [Amazon Bedrock](https://www.ntcinfo.org) console or the API, and execute it as displayed in the following code:
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Clean up
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To avoid undesirable 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 deployed the model using Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation [designs](https://newborhooddates.com) in the navigation pane, select Marketplace releases. +2. In the Managed implementations area, locate the endpoint you want to erase. +3. Select the endpoint, and [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:ETJXiomara) on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the appropriate 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 released will sustain expenses if you leave it running. Use the following code to delete 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 explored 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 now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker 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](https://deadreckoninggame.com) business develop ingenious services utilizing AWS services and accelerated calculate. Currently, he is concentrated on developing techniques for fine-tuning and enhancing the reasoning performance of big language designs. In his complimentary time, Vivek delights in hiking, watching films, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://socialcoin.online) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://dev.catedra.edu.co:8084) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer [Science](https://yeetube.com) and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://timviecvtnjob.com) with the Third-Party Model [Science team](http://107.182.30.1906000) at AWS.
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Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.runsimon.com) center. She is passionate about building services that assist customers accelerate their [AI](https://gitlab.chabokan.net) journey and unlock organization value.
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