From f631fd7cbd1559cb868bb1a4b1ab2fbb8713e6a7 Mon Sep 17 00:00:00 2001 From: terriepichardo Date: Sat, 22 Feb 2025 19:19:18 +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..b273a9b --- /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 announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://www.fightdynasty.com)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](http://47.100.72.85:3000) ideas on AWS.
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In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the [distilled variations](https://gigsonline.co.za) of the designs 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://lovn1world.com) that utilizes reinforcement discovering to boost reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial identifying function is its reinforcement knowing (RL) action, which was used to improve the design's actions beyond the [basic pre-training](https://www.huntsrecruitment.com) and fine-tuning process. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately boosting both relevance and [clearness](http://b-ways.sakura.ne.jp). In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, indicating it's equipped to break down complex questions and factor through them in a detailed manner. This guided reasoning procedure enables the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation design that can be integrated into various workflows such as representatives, sensible thinking and information analysis tasks.
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DeepSeek-R1 uses a Mixture of [Experts](http://www.tuzh.top3000) (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion specifications, allowing effective reasoning by routing inquiries to the most appropriate expert "clusters." This [approach enables](https://asesordocente.com) the model to specialize in different problem domains while maintaining total effectiveness. DeepSeek-R1 requires at least 800 GB of [HBM memory](http://www.scitqn.cn3000) in FP8 format for inference. In this post, we will utilize 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 models bring the reasoning capabilities of the main R1 design to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:HildredWarby80) 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective designs to [simulate](https://miderde.de) the habits and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and assess models against criteria. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative [AI](https://aggm.bz) applications.
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Prerequisites
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To [release](https://test.gamesfree.ca) the DeepSeek-R1 design, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limitation increase, produce a limitation increase demand and reach out to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For directions, see Establish authorizations to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, avoid hazardous content, and examine designs against essential safety requirements. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and design actions released on Amazon Bedrock [Marketplace](https://git.profect.de) and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock [console](https://jobs.ofblackpool.com) or the API. For the example code to produce the guardrail, see the GitHub repo.
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The general circulation includes the following steps: First, the system gets 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 model for reasoning. After receiving the model'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 suggesting the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following sections show reasoning using 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 designs (FMs) through [Amazon Bedrock](https://git.ivran.ru). 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 catalog under Foundation models in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to conjure up the model. It doesn't support Converse 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 essential details about the model's abilities, pricing structure, and application standards. You can discover detailed usage directions, including sample API calls and code bits for integration. The [design supports](http://59.110.68.1623000) numerous text generation tasks, including content development, code generation, and concern answering, using its support discovering optimization and CoT reasoning abilities. +The page also consists of implementation alternatives and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick Deploy.
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You will be [triggered](http://www.evmarket.co.kr) to set up the [release details](https://gitea.xiaolongkeji.net) for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, go into a variety of instances (between 1-100). +6. For example type, select your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up innovative security and facilities settings, consisting of virtual private cloud (VPC) networking, service role approvals, and encryption settings. For most use cases, the default settings will work well. However, for production releases, you might wish to evaluate these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the implementation is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive user interface where you can explore different prompts and adjust design specifications like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For instance, material for inference.
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This is an excellent way to explore the [model's reasoning](https://neoshop365.com) and text generation abilities before integrating it into your applications. The play area provides instant feedback, helping you comprehend how the model reacts to various inputs and letting you tweak your triggers for ideal outcomes.
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You can quickly test the design in the playground through the UI. However, to conjure up the deployed model programmatically with any [Amazon Bedrock](https://www.xafersjobs.com) APIs, you need to get the [endpoint ARN](https://bnsgh.com).
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Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning utilizing a [released](http://wiki.lexserve.co.ke) DeepSeek-R1 model 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 create the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, and sends a demand to create text based upon 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 ML options that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and release them into [production utilizing](https://admithel.com) either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart offers two convenient approaches: using the instinctive SageMaker JumpStart UI or carrying out [programmatically](https://govtpakjobz.com) through the SageMaker Python SDK. Let's check out both approaches to help you select the method that best suits your requirements.
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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](http://luodev.cn) users will be triggered to create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model browser displays available designs, with details like the company name and model capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card shows crucial details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if relevant), showing that this design can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to [conjure](http://kyeongsan.co.kr) up 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 design name and provider 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 specs. +- Usage standards
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Before you release the design, it's advised 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 deployment.
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7. For Endpoint name, utilize the immediately created name or create a custom one. +8. For Instance type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the variety of instances (default: 1). +Selecting appropriate circumstances types and counts is important for cost and performance optimization. Monitor your implementation 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 setups for accuracy. For this design, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the model.
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The implementation process can take a number of minutes to finish.
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When release is total, your endpoint status will change to InService. At this moment, the model is ready to accept reasoning demands through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the [deployment](https://welcometohaiti.com) is complete, you can invoke the model utilizing a SageMaker runtime client and incorporate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the necessary AWS authorizations and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for deploying the model 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 create a [guardrail](http://47.111.127.134) using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Tidy up
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To prevent undesirable charges, complete the actions in this area to tidy 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 steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations. +2. In the Managed deployments section, find the endpoint you want 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 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 explored how you can access and release the DeepSeek-R1 model using Bedrock Marketplace 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 models, 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 helps emerging generative [AI](https://gogs.sxdirectpurchase.com) business build innovative services using AWS services and accelerated calculate. Currently, he is focused on establishing strategies for fine-tuning and optimizing the reasoning efficiency of large language models. In his leisure time, Vivek enjoys treking, viewing movies, and attempting various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://gitea.lolumi.com) Specialist Solutions Architect with the [Third-Party Model](https://anychinajob.com) [Science](https://bikapsul.com) group at AWS. His area of focus is AWS [AI](https://smartcampus-seskoal.id) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://es-africa.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://club.at.world) center. She is enthusiastic about building options that help clients accelerate their [AI](https://www.jr-it-services.de:3000) journey and unlock organization worth.
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