commit 1f0f9bd08bf42278c4e142860d25eff269e624fe Author: tatianabeckwit Date: Mon Feb 17 14:26:34 2025 +0330 Update '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..cda78e7 --- /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](https://apk.tw)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion [criteria](http://1.15.150.903000) to build, experiment, and responsibly scale your generative [AI](https://git.mtapi.io) concepts 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 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 design (LLM) established by DeepSeek [AI](https://paknoukri.com) that uses reinforcement finding out to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential distinguishing feature is its reinforcement learning (RL) step, which was utilized to refine the model's responses beyond the basic pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, implying it's geared up to break down complicated queries and factor through them in a detailed manner. This directed reasoning procedure allows the design to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to create 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 different workflows such as representatives, sensible [reasoning](https://remnanthouse.tv) and information analysis jobs.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion criteria, enabling efficient reasoning by routing queries to the most pertinent specialist "clusters." This [approach](http://git.armrus.org) allows the design to concentrate on different problem domains while maintaining total performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the design. 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 thinking abilities of the main R1 model to more [efficient architectures](https://gitlab.dituhui.com) based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). [Distillation describes](http://47.97.178.182) a process of training smaller, more efficient designs to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher design.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and examine models against crucial security criteria. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative [AI](http://118.190.175.108:3000) [applications](https://camtalking.com).
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Prerequisites
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To deploy the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're utilizing 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 releasing. To ask for a limitation boost, develop a limit increase demand and connect to your account team.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For instructions, see Set up approvals to use guardrails for [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:JeannieGossett) content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, prevent damaging material, and assess models against key security requirements. You can carry out safety steps for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to examine user inputs and model actions [released](https://git.li-yo.ts.net) on Amazon Bedrock Marketplace and SageMaker [JumpStart](https://zapinacz.pl). You can develop 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 general circulation includes the following actions: 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 to the design for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the final 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 stage. The examples showcased in the following areas show reasoning utilizing 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. 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 brochure 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 provider and choose the DeepSeek-R1 model.
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The design detail page provides necessary details about the model's capabilities, prices structure, and execution guidelines. You can discover detailed usage guidelines, including sample API calls and code snippets for combination. The design supports numerous text generation jobs, consisting of material creation, code generation, and concern answering, utilizing its reinforcement finding out optimization and [CoT reasoning](http://101.34.66.2443000) abilities. +The page also consists of implementation choices and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To [start utilizing](http://120.36.2.2179095) DeepSeek-R1, choose Deploy.
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You will be triggered to configure the deployment 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 Number of instances, get in a variety of circumstances (in between 1-100). +6. For example type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up advanced security and infrastructure settings, including virtual personal cloud (VPC) networking, service role authorizations, and encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you may want to evaluate these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the release is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive user interface where you can explore various triggers and change model specifications like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For instance, material for inference.
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This is an exceptional way to explore the model's thinking and text generation capabilities before incorporating it into your [applications](https://gogs.sxdirectpurchase.com). The playground provides immediate feedback, assisting you understand how the design responds to various inputs and letting you fine-tune your prompts for optimal results.
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You can rapidly evaluate the design in the play ground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the [endpoint ARN](http://git.attnserver.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 deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop 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 actually produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends out a request to [generate text](https://www.meditationgoodtip.com) 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 deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 convenient methods: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you pick the approach that finest matches your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 utilizing 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 console, pick JumpStart in the navigation pane.
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The design available models, with details like the service provider name and model abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card reveals crucial details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if applicable), showing that this design can be registered with Amazon Bedrock, permitting you to [utilize Amazon](https://gigsonline.co.za) Bedrock APIs to conjure up the model
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5. Choose the [design card](http://82.157.77.1203000) to see 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 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](https://git.cyu.fr) standards
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Before you deploy the model, it's suggested to review the [model details](http://47.93.234.49) and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, use the instantly generated name or create a custom-made one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of instances (default: 1). +Selecting suitable circumstances types and counts is essential for cost and efficiency optimization. [Monitor](https://git.qingbs.com) your [implementation](https://admin.gitea.eccic.net) 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 model, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to deploy the model.
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The deployment procedure can take several minutes to finish.
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When deployment is complete, your endpoint status will alter to InService. At this moment, the model is all set to accept inference requests through the endpoint. You can keep track of the release progress on the SageMaker console Endpoints page, which will display relevant [metrics](https://globalabout.com) 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.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get begun 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 shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is supplied in the Github here. You can clone the notebook 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 create a guardrail using the Amazon Bedrock console or the API, and implement it as shown 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 deployment
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If you deployed the design 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, pick Marketplace releases. +2. In the Managed implementations area, find the [endpoint](https://vydiio.com) 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 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 expenses 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 checked out how you can access and deploy the DeepSeek-R1 design utilizing Bedrock Marketplace and [SageMaker JumpStart](https://www.diekassa.at). Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker [JumpStart](https://washcareer.com) designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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://notitia.tv) business build innovative solutions using AWS services and sped up compute. Currently, he is focused on establishing methods for fine-tuning and optimizing the reasoning efficiency of large language designs. In his spare time, Vivek takes pleasure in hiking, enjoying motion pictures, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://timviec24h.com.vn) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://arbeitswerk-premium.de) 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 working on generative [AI](http://compass-framework.com:3000) with the Third-Party Model [Science](https://edujobs.itpcrm.net) group at AWS.
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Banu Nagasundaram leads item, engineering, and tactical partnerships for [Amazon SageMaker](https://telecomgurus.in) JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.stmlnportal.com) center. She is enthusiastic about building solutions that help customers accelerate their [AI](http://www.xn--he5bi2aboq18a.com) journey and unlock organization value.
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