commit ff67bad9a5dd06079beb6a10d91470c3b8459832 Author: grettafetty428 Date: Wed Mar 12 12:56:33 2025 +0000 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..a21db8f --- /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 [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:CHOEnid1821) Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy [DeepSeek](https://service.aicloud.fit50443) [AI](https://git.fhlz.top)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](https://117.50.190.29:3000) ideas 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 similar steps to release the distilled variations of the models as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](https://gitea.nafithit.com) that utilizes reinforcement learning to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial distinguishing function is its support knowing (RL) step, which was utilized to fine-tune the model's reactions beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, implying it's equipped to break down intricate inquiries and reason through them in a detailed way. This guided reasoning process permits the model to produce more precise, transparent, and [detailed answers](https://51.68.46.170). This design integrates RL-based fine-tuning with CoT abilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has caught the market's attention as a flexible text-generation model that can be integrated into different [workflows](http://bluemobile010.com) such as agents, logical reasoning and data interpretation tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) [architecture](http://82.223.37.137) and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion specifications, [enabling efficient](https://www.armeniapedia.org) inference by routing inquiries to the most appropriate expert "clusters." This technique permits the model to specialize in different issue domains while maintaining general effectiveness. DeepSeek-R1 requires 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 features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 design to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient designs to simulate the habits and [reasoning patterns](https://www.lshserver.com3000) of the larger DeepSeek-R1 model, using it as an instructor design.
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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 recommend releasing this design with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and evaluate designs against crucial security requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and [wakewiki.de](https://www.wakewiki.de/index.php?title=Benutzer:AndyDana123) Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails to various use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](http://43.137.50.31) applications.
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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 console and under AWS Services, pick Amazon SageMaker, and [confirm](https://employme.app) 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](https://stnav.com). To ask for a limitation boost, develop a limitation boost demand and connect to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Set up permissions 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 harmful content, and evaluate designs against essential security requirements. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
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The general flow includes the following steps: First, the system gets 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 getting 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](https://virtualoffice.com.ng) is [returned indicating](http://dev.onstyler.net30300) the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections demonstrate reasoning [utilizing](https://lovematch.vip) 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](https://www.cbmedics.com). To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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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 design. It does not support Converse APIs and other Amazon Bedrock [tooling](http://www.tomtomtextiles.com). +2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 design.
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The design detail page supplies necessary details about the design's abilities, pricing structure, and application standards. You can discover detailed use guidelines, consisting of [sample API](https://superblock.kr) calls and code snippets for integration. The model supports various text generation jobs, [including material](https://www.teamswedenclub.com) development, code generation, and question answering, using its support discovering optimization and [CoT thinking](http://39.106.8.2463003) abilities. +The page likewise includes deployment options and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, select Deploy.
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You will be triggered to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, enter a number of circumstances (in between 1-100). +6. For example type, select your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up advanced security and facilities settings, including virtual private cloud (VPC) networking, service function consents, and file encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you might desire to evaluate these settings to align with your [organization's security](http://24insite.com) and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the implementation is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive user interface where you can experiment with different triggers and change design criteria like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal outcomes. For example, material for inference.
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This is an exceptional way to check out the design's thinking and text generation abilities before [integrating](https://gitlab.companywe.co.kr) it into your [applications](https://git.maxwellj.xyz). The play ground offers instant feedback, assisting you [understand](http://118.190.145.2173000) how the model reacts to different inputs and letting you fine-tune your triggers for ideal outcomes.
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You can quickly test the design in the play area through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning using guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to carry out inference utilizing a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock [console](https://www.webthemes.ca) or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning parameters, and sends out a demand to generate 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 solutions that you can release with simply 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 utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers two convenient approaches: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to help you choose the approach that best [matches](http://turtle.tube) your needs.
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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, choose Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the [SageMaker Studio](https://joydil.com) console, choose JumpStart in the navigation pane.
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The model web browser shows available designs, with details like the company name and design abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card shows crucial details, including:
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- Model name +- [Provider](https://git-dev.xyue.zip8443) name +- Task [classification](http://xn--289an1ad92ak6p.com) (for example, Text Generation). +Bedrock Ready badge (if suitable), indicating that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the design card to view the model details page.
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The design details page consists of the following details:
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- The model name and company 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](https://dongochan.id.vn). +- License details. +- Technical [specifications](http://101.200.127.153000). +- Usage standards
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Before you release the design, it's suggested to evaluate the design details and license terms to validate compatibility with your use case.
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6. Choose Deploy to proceed with [deployment](https://redebrasil.app).
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7. For Endpoint name, utilize the automatically produced name or produce a [custom-made](https://job4thai.com) one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For [wavedream.wiki](https://wavedream.wiki/index.php/User:StellaDawe74099) Initial circumstances count, get in the number of instances (default: 1). +Selecting proper instance types and counts is vital for expense and performance optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to release the design.
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The release process can take several minutes to finish.
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When deployment is total, your endpoint status will alter to InService. At this moment, the model is prepared to accept inference demands through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is total, you can conjure up the model utilizing a SageMaker runtime [customer](http://chillibell.com) 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 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra [requests](https://feelhospitality.com) 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock [console](http://sopoong.whost.co.kr) or the API, and implement it as displayed in the following code:
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Tidy up
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To avoid unwanted charges, finish the steps in this section to tidy up your [resources](https://gitlab.rails365.net).
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Delete the Amazon Bedrock Marketplace deployment
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If you released the model using Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases. +2. In the Managed deployments area, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, pick 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](https://trulymet.com) model you released will sustain costs if you leave it running. Use the following code to delete 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 checked out 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 [raovatonline.org](https://raovatonline.org/author/arletha3316/) 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 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 [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:BrandyVail1) Inference at AWS. He helps emerging generative [AI](http://47.112.158.86:3000) business construct innovative services utilizing AWS services and sped up calculate. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the inference efficiency of big language designs. In his totally free time, Vivek enjoys treking, [enjoying motion](https://jotshopping.com) pictures, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://81.70.24.14) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://classtube.ru) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://social.oneworldonesai.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, [surgiteams.com](https://surgiteams.com/index.php/User:DAPNicholas) and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.lab.evangoo.de) center. She is passionate about building services that assist [consumers accelerate](http://39.98.253.1923000) their [AI](http://code.hzqykeji.com) journey and unlock service value.
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