commit c89339d60f85221622ab1dc581641d7535c06707 Author: jacklynkieran7 Date: Thu Apr 10 13:31:40 2025 +0800 Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart 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..28a1662 --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://8.140.200.236:3000)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative [AI](https://git.buzhishi.com:14433) ideas on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://git.cooqie.ch) that utilizes reinforcement learning to improve thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A feature is its reinforcement knowing (RL) action, which was utilized to [fine-tune](https://www.execafrica.com) the model's actions beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually boosting both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, indicating it's equipped to break down complicated inquiries and reason through them in a detailed manner. This assisted thinking [process](https://git.hmcl.net) allows the model to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation design that can be incorporated into numerous workflows such as agents, sensible thinking and information interpretation jobs.
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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 parameters, allowing efficient reasoning by routing questions to the most pertinent expert "clusters." This method enables the design to concentrate on various problem domains while maintaining total effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for [it-viking.ch](http://it-viking.ch/index.php/User:SherryWur1) reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 [GPUs providing](https://csmsound.exagopartners.com) 1128 GB of GPU memory.
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DeepSeek-R1 distilled [designs](http://www.zhihutech.com) bring the reasoning capabilities of the main R1 model to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective models to mimic the habits and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor model.
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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 model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and evaluate designs against key security criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, [Bedrock Guardrails](https://www.mk-yun.cn) supports only the ApplyGuardrail API. You can produce multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative [AI](https://tageeapp.com) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To [examine](https://job4thai.com) if you have quotas for P5e, open the Service Quotas [console](https://splink24.com) 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 circumstances in the AWS Region you are releasing. To request a limit boost, produce a limitation increase request and connect to your account team.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For guidelines, see Set up consents to use [guardrails](https://labs.hellowelcome.org) for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, prevent hazardous content, and examine models against crucial security criteria. You can execute precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and design actions 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 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 model for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is [returned](https://www.webthemes.ca) showing the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections demonstrate reasoning using 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 structure models (FMs) through [Amazon Bedrock](http://svn.ouj.com). To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the [Amazon Bedrock](https://redmonde.es) console, select Model catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to [conjure](https://gitea.nongnghiepso.com) 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 model.
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The model detail page offers important details about the model's abilities, pricing structure, and application standards. You can find [detailed](https://nailrada.com) use instructions, consisting of sample API calls and code bits for integration. The design supports numerous text generation jobs, consisting of content production, code generation, and question answering, utilizing its support learning optimization and CoT reasoning abilities. +The page also includes release choices and licensing details to help you get begun with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, pick Deploy.
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You will be triggered to set up the implementation 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 Variety of circumstances, get in a number of instances (in between 1-100). +6. For Instance type, pick your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can configure innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service role permissions, and encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you might desire to review these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the release is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive user interface where you can experiment with various triggers and adjust model parameters like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal results. For example, material for inference.
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This is an outstanding method to check out the model's thinking and text generation abilities before integrating it into your [applications](https://csmsound.exagopartners.com). The play ground supplies instant feedback, helping you comprehend how the design reacts to numerous inputs and letting you fine-tune your triggers for optimal results.
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You can rapidly check the model in the play area through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run [reasoning utilizing](http://47.93.192.134) guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference utilizing a deployed DeepSeek-R1 model 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 created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends a request to produce text based 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) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart offers two convenient methods: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the [SageMaker](https://www.behavioralhealthjobs.com) Python SDK. Let's check out both methods to assist you pick the technique that finest 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 utilizing SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. [First-time](https://rapostz.com) users will be prompted to develop a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design internet browser shows available models, with details like the provider name and [design capabilities](http://47.119.27.838003).
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 [design card](https://www.flughafen-jobs.com). +Each model card shows crucial details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if relevant), indicating that this design can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the design card to see the design details page.
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The model details page consists of the following details:
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- The design name and company details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
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The About tab includes [crucial](http://112.124.19.388080) details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage standards
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Before you deploy the model, it's advised to review the model details and license terms to validate compatibility with your use case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, utilize the immediately produced name or develop a customized one. +8. For [Instance type](https://www.ayc.com.au) ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of circumstances (default: 1). +Selecting suitable instance types and counts is vital for cost and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the model.
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The deployment procedure can take a number of minutes to complete.
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When release is total, your endpoint status will alter to InService. At this moment, the model is ready to accept inference demands through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will [display relevant](https://git.mista.ru) metrics and status details. When the implementation is total, you can conjure up the model utilizing a SageMaker runtime customer and integrate it with your [applications](http://xiaomaapp.top3000).
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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 approvals](https://lab.chocomart.kz) and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is provided in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run additional demands 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 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 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 released 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 releases. +2. In the Managed implementations area, 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 erasing the right release: 1. Endpoint name. +2. Model name. +3. [Endpoint](https://51.75.215.219) 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 wish 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 begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, 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 assists emerging generative [AI](https://www.behavioralhealthjobs.com) business build innovative [services utilizing](http://demo.qkseo.in) AWS services and sped up [calculate](https://www.characterlist.com). Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the inference efficiency of large language designs. In his free time, Vivek delights in treking, seeing movies, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://finance.azberg.ru) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://pantalassicoembalagens.com.br) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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[Jonathan Evans](http://202.164.44.2463000) is a Professional Solutions Architect working on generative [AI](https://mixup.wiki) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://gitlab.xma1.de) center. She is enthusiastic about building options that assist consumers accelerate their [AI](http://makerjia.cn:3000) journey and unlock business value.
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