From 1348c170f2d3486a6b1db14d95ae87cc6279f74e Mon Sep 17 00:00:00 2001 From: Chandra Paling Date: Fri, 7 Feb 2025 11:42:25 +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..9387ee5 --- /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 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 deploy DeepSeek [AI](https://schoolmein.com)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled [variations ranging](http://zhangsheng1993.tpddns.cn3000) from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative [AI](https://git.bluestoneapps.com) concepts on AWS.
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In this post, we how to begin with DeepSeek-R1 on Amazon Bedrock [Marketplace](https://www.apkjobs.site) and SageMaker JumpStart. You can follow comparable steps to deploy 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://hireteachers.net) that uses reinforcement discovering to improve reasoning [abilities](https://insta.tel) through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential differentiating feature is its reinforcement knowing (RL) action, which was used to fine-tune the model's reactions beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, eventually improving both [relevance](https://www.ntcinfo.org) and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, implying it's equipped to break down complex inquiries and reason through them in a detailed manner. This guided reasoning process allows the design to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation design that can be incorporated into different workflows such as representatives, rational thinking and data [analysis jobs](http://106.15.48.1323880).
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, enabling effective inference by routing queries to the most appropriate specialist "clusters." This method permits the design to specialize in different issue domains while maintaining general efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge features 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 on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of [training](https://gitea.star-linear.com) smaller sized, more effective models to mimic the habits and thinking patterns of the larger DeepSeek-R1 design, utilizing it as a [teacher model](https://www.bluedom.fr).
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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 suggest deploying this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and evaluate designs against essential safety requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, [garagesale.es](https://www.garagesale.es/author/lucamcrae20/) Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](https://subamtv.com) [applications](https://hugoooo.com).
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Prerequisites
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To deploy the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the [Service Quotas](http://www.book-os.com3000) console and under AWS Services, pick Amazon SageMaker, and verify you're using 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 request a limitation boost, create a limitation increase request and connect to your account team.
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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) consents to utilize Amazon Bedrock Guardrails. For instructions, see Establish approvals to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock [Guardrails enables](https://geetgram.com) you to introduce safeguards, prevent damaging content, and examine designs against essential safety requirements. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and design responses deployed 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 produce the guardrail, see the GitHub repo.
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The basic circulation includes 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 to the model for reasoning. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the last outcome. 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 areas demonstrate inference 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 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 catalog under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 model.
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The design detail page offers vital details about the [model's](https://www.social.united-tuesday.org) capabilities, rates structure, and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:FranklinBreillat) execution guidelines. You can discover detailed usage directions, including sample API calls and code bits for combination. The model supports numerous text generation jobs, including material development, code generation, and concern answering, using its support finding out optimization and CoT reasoning capabilities. +The page also includes deployment options and licensing details to help you start 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 deployment 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 Number of circumstances, enter a variety of instances (in between 1-100). +6. For example type, select your circumstances type. For ideal efficiency with DeepSeek-R1, a [GPU-based circumstances](https://git.komp.family) type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up sophisticated security and [infrastructure](http://1.117.194.11510080) settings, consisting of virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For most utilize cases, the default settings will work well. However, for production releases, you may wish to review these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the deployment is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive user interface where you can try out various prompts and change design specifications like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal results. For instance, content for reasoning.
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This is an excellent method to explore the model's reasoning and text [generation capabilities](https://solegeekz.com) before integrating it into your applications. The play ground supplies instant feedback, assisting you understand how the model reacts to different inputs and letting you fine-tune your prompts for optimum outcomes.
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You can quickly test the design in the play ground through the UI. However, [35.237.164.2](https://35.237.164.2/wiki/User:BessieFitzRoy) to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a [guardrail](https://startuptube.xyz) [utilizing](https://xtragist.com) the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the [GitHub repo](https://betalk.in.th). After you have produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends a demand to create 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 (ML) center with FMs, integrated algorithms, and prebuilt ML options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:MichelleHarmer9) deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides two convenient techniques: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you select the technique 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 release 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 triggered to create a domain. +3. On the SageMaker Studio console, pick [JumpStart](https://devfarm.it) in the navigation pane.
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The design web browser shows available designs, with details like the company name and design capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card shows crucial details, including:
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- Model name +- Provider name +- Task [classification](http://nas.killf.info9966) (for example, Text Generation). +Bedrock Ready badge (if appropriate), indicating that this model can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the model card to see the design details page.
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The design details page includes the following details:
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- The model name and company details. +Deploy button to deploy 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 description. +- License details. +- Technical specs. +- Usage standards
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Before you release the design, it's recommended to examine the [design details](https://horizonsmaroc.com) and license terms to verify compatibility with your usage case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, utilize the automatically created name or create a custom one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the number of circumstances (default: 1). +Selecting proper circumstances types and counts is crucial for expense and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, [Real-time inference](https://demo.pixelphotoscript.com) is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we highly advise sticking to [SageMaker JumpStart](https://www.uaehire.com) default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the model.
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The release procedure can take numerous minutes to complete.
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When implementation is complete, your [endpoint status](https://git.wisder.net) will alter to InService. At this point, the model is prepared to accept reasoning requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and [status details](https://e-gitlab.isyscore.com). When the implementation is complete, you can [conjure](https://39.105.45.141) up 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 need to install the SageMaker Python SDK and make certain you have the necessary AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to [release](https://git.thetoc.net) and utilize DeepSeek-R1 for inference programmatically. The code for releasing the design is supplied in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run additional requests against the predictor:
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[Implement guardrails](http://1.92.128.2003000) and run inference with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can likewise utilize 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 displayed in the following code:
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Tidy up
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To prevent unwanted charges, complete the actions in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the design utilizing 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 deployments. +2. In the Managed releases section, find the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate implementation: 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 deployed will sustain expenses if you leave it running. 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 release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](https://git.programming.dev) or [wavedream.wiki](https://wavedream.wiki/index.php/User:KarlBeardsley7) Amazon Bedrock Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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 [AI](https://jktechnohub.com) companies build ingenious options utilizing AWS services and sped up compute. Currently, he is focused on developing strategies for fine-tuning and optimizing the inference efficiency of large language designs. In his downtime, Vivek enjoys treking, viewing motion pictures, and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://www.heart-hotel.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://workonit.co) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and [links.gtanet.com.br](https://links.gtanet.com.br/vernon471078) Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://winf.dhsh.de) with the Third-Party Model [Science](https://catvcommunity.com.tr) team at AWS.
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Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](http://gogs.funcheergame.com) [AI](http://101.132.136.5:8030) center. She is enthusiastic about constructing services that help clients accelerate their [AI](http://ccconsult.cn:3000) journey and unlock organization worth.
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