commit 46b0ca379dd92f9bbcb83aff82eee66235be2a37 Author: Adalberto Roddy Date: Sun Feb 9 16:17:20 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..a57aa96 --- /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 Qwen designs are available through Amazon [Bedrock Marketplace](https://www.milegajob.com) and [Amazon SageMaker](https://canadasimple.com) JumpStart. With this launch, you can now deploy DeepSeek [AI](http://steriossimplant.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](https://dubairesumes.com) ideas on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps 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](https://git.pxlbuzzard.com) that utilizes reinforcement finding out to enhance reasoning capabilities through a [multi-stage training](https://vsbg.info) process from a DeepSeek-V3-Base foundation. An [essential identifying](http://bhnrecruiter.com) function is its support learning (RL) step, which was utilized to refine the model's actions beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:FranklinBreillat) objectives, eventually boosting both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, suggesting it's equipped to break down complicated inquiries and reason through them in a detailed way. This guided thinking procedure permits the model to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its [extensive abilities](http://223.68.171.1508004) DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation model that can be integrated into various workflows such as representatives, logical thinking and [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:SoonWinfrey7778) information interpretation jobs.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion specifications, enabling efficient reasoning by routing questions to the most appropriate professional "clusters." This method enables the model to specialize in various problem domains while maintaining general efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, [135.181.29.174](http://135.181.29.174:3001/aureliogpp7753/hrvatskinogomet/wiki/DeepSeek-R1+Model+now+Available+in+Amazon+Bedrock+Marketplace+And+Amazon+SageMaker+JumpStart.-) we will utilize an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 model to more [effective architectures](http://poscotech.co.kr) based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective designs to simulate the habits and thinking patterns of the larger DeepSeek-R1 design, using it as an instructor [surgiteams.com](https://surgiteams.com/index.php/User:Darnell83J) model.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and examine models against crucial security criteria. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](https://lepostecanada.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you [require access](https://theindietube.com) to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) pick Amazon SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To request a limit increase, create a limit boost request and reach out to your account group.
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Because you will be [releasing](http://revoltsoft.ru3000) this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Set up consents to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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[Amazon Bedrock](https://git.gilgoldman.com) Guardrails allows you to present safeguards, prevent hazardous material, and examine designs against crucial safety criteria. You can execute security measures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to [apply guardrails](https://speeddating.co.il) to assess user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
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The basic circulation includes the following actions: First, the system gets an input for the design. This input is then [processed](http://175.24.176.23000) through the ApplyGuardrail API. If the input passes the [guardrail](http://git.ndjsxh.cn10080) check, it's sent to the design for inference. After receiving the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the 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 took place at the input or output phase. The examples showcased in the following sections demonstrate reasoning utilizing this API.
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Deploy DeepSeek-R1 in [Amazon Bedrock](http://bhnrecruiter.com) Marketplace
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Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To [gain access](https://quickservicesrecruits.com) to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 design.
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The design detail page offers necessary details about the [model's](https://demanza.com) abilities, rates structure, and implementation standards. You can find detailed usage guidelines, [consisting](http://git.liuhung.com) of sample API calls and code bits for combination. The design supports various text generation jobs, including material production, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT reasoning abilities. +The page likewise consists of implementation alternatives and licensing details to help you begin 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 model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, enter a variety of instances (between 1-100). +6. For Instance type, select your instance type. For [optimal efficiency](https://dev.ncot.uk) with DeepSeek-R1, a [GPU-based circumstances](https://trulymet.com) type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service role authorizations, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you might wish to examine these [settings](http://steriossimplant.com) to align with your organization's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the implementation is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive user interface where you can experiment with different triggers and adjust design parameters like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum results. For example, content for reasoning.
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This is an outstanding way to explore the design's reasoning and text generation abilities before incorporating it into your applications. The play ground offers immediate feedback, assisting you comprehend how the [design reacts](https://git.lewis.id) to various inputs and letting you fine-tune your prompts for ideal results.
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You can rapidly test the design in the play area through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference using a deployed DeepSeek-R1 design 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 produce 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, sets up inference specifications, and sends out a demand to create 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) center with FMs, built-in algorithms, and prebuilt ML options that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides two convenient techniques: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you pick the technique that finest suits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design browser shows available designs, with details like the company name and design abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card shows crucial details, consisting of:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this model can be signed up 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 model details page.
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The design details page includes the following details:
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- The design name and provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of crucial details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage standards
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Before you deploy the design, it's recommended to review the design details and license terms to [validate compatibility](https://www.jobindustrie.ma) with your use case.
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6. Choose Deploy to [proceed](https://gl.ignite-vision.com) with deployment.
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7. For Endpoint name, use the immediately generated name or develop a custom one. +8. For example type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of instances (default: 1). +Selecting suitable instance types and counts is important for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. For this model, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to deploy the design.
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The deployment process can take several minutes to finish.
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When [implementation](https://git.berezowski.de) is complete, your endpoint status will change to [InService](https://hitechjobs.me). At this moment, the model is ready to accept inference demands through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will show and status details. When the release is complete, you can invoke the model utilizing a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To start with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS permissions and [environment setup](http://116.63.157.38418). The following is a detailed code example that shows how to deploy and [utilize](http://171.244.15.683000) 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 extra demands 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
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Tidy up
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To avoid [undesirable](http://social.redemaxxi.com.br) charges, complete the steps in this area to clean up your resources.
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Delete the [Amazon Bedrock](https://bootlab.bg-optics.ru) Marketplace deployment
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If you deployed the design utilizing 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 releases. +2. In the Managed implementations section, locate the endpoint you want to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're erasing the correct release: [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11862161) 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 released 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 deploy the DeepSeek-R1 model using 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 designs, 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](http://www.homeserver.org.cn3000) Architect for Inference at AWS. He helps emerging generative [AI](https://sun-clinic.co.il) companies build ingenious solutions using AWS services and accelerated compute. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the reasoning efficiency of large language models. In his spare time, Vivek enjoys treking, seeing films, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:PoppyForand) and attempting various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://gitlab.dangwan.com) Specialist Solutions Architect with the Third-Party Model [Science](https://49.12.72.229) group at AWS. His location of focus is AWS [AI](http://93.104.210.100:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://git.agri-sys.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://suvenir51.ru) hub. She is enthusiastic about developing services that help clients accelerate their [AI](http://39.106.8.246:3003) journey and unlock business value.
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