commit d4334715b72d996c388ee29f679dde1735a6b21b Author: richhorne7021 Date: Fri Feb 21 04:20:45 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..9787eb2 --- /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 release DeepSeek [AI](http://47.101.131.235:3000)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative [AI](https://bolsadetrabajo.tresesenta.mx) concepts on AWS.
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In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled versions of the models as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://support.mlone.ai) that utilizes reinforcement discovering to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial identifying feature is its support knowing (RL) action, which was used to refine the design's responses beyond the standard pre-training and fine-tuning procedure. By [incorporating](http://47.103.112.133) RL, DeepSeek-R1 can adapt more to user feedback and goals, ultimately enhancing both [significance](https://www.wotape.com) and clarity. In addition, DeepSeek-R1 [utilizes](https://www.jangsuori.com) a chain-of-thought (CoT) technique, indicating it's equipped to break down complicated inquiries and factor through them in a detailed manner. This directed reasoning procedure enables the design to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has captured the [industry's attention](https://paanaakgit.iran.liara.run) as a flexible text-generation model that can be integrated into various workflows such as agents, sensible thinking and data analysis jobs.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) [architecture](http://pyfup.com3000) and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, enabling efficient inference by [routing queries](https://smarthr.hk) to the most relevant professional "clusters." This approach permits the design to concentrate on different problem domains while maintaining overall [effectiveness](https://bocaiw.in.net). DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 [distilled](http://recruitmentfromnepal.com) designs bring the [thinking abilities](https://git.peaksscrm.com) of the main R1 design to more efficient architectures based upon 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 sized, more efficient models to simulate the behavior and [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) thinking patterns of the larger DeepSeek-R1 model, utilizing it as an instructor model.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging material, and evaluate designs against key security requirements. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](https://edujobs.itpcrm.net) [applications](https://jobs.theelitejob.com).
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for [endpoint usage](https://git.aionnect.com). 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, produce a limitation increase request and reach out to your account group.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Set up consents to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, prevent harmful content, and examine designs against crucial safety requirements. You can implement precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and [model actions](http://woorichat.com) 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 includes the following steps: First, the system gets an input for the design. 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 receiving the [model's](http://jobshut.org) output, another [guardrail check](https://www.chinami.com) is applied. If the output passes this last check, it's returned as the last result. However, if either the input or output is intervened by the guardrail, a message is [returned suggesting](https://lepostecanada.com) the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following [sections](https://www.tippy-t.com) show [inference utilizing](https://asg-pluss.com) this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized structure designs (FMs) through [Amazon Bedrock](https://aquarium.zone). To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, pick Model catalog under Foundation models in the navigation pane. +At the time of composing this post, you can use the [InvokeModel API](http://git.vimer.top3000) to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 design.
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The design detail page offers necessary details about the [model's](https://ai.ceo) capabilities, rates structure, and application standards. You can find detailed use instructions, consisting of sample API calls and code bits for combination. The model supports different text generation jobs, consisting of material creation, code generation, and question answering, using its support discovering optimization and CoT thinking capabilities. +The page also consists of implementation options and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To [start utilizing](https://kyigit.kyigd.com3000) DeepSeek-R1, [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:MyraCollocott58) choose Deploy.
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You will be triggered to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, go into a number of instances (in between 1-100). +6. For Instance type, pick your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up advanced security and facilities settings, including virtual personal cloud (VPC) networking, service role approvals, and file encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you might wish to evaluate these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the release is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive interface where you can experiment with different triggers and adjust model parameters like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For example, content for inference.
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This is an excellent way to explore the design's thinking and text generation abilities before incorporating it into your applications. The playground offers instant feedback, helping you understand how the design reacts to different inputs and letting you tweak your prompts for ideal results.
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You can rapidly test the model in the play ground through the UI. However, to invoke the released 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 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 utilizing 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 parameters, and sends a demand to generate text based on 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, integrated algorithms, and prebuilt ML [options](https://islamichistory.tv) that you can deploy 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](http://47.244.181.255) either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 practical techniques: using the [instinctive SageMaker](http://hitbat.co.kr) [JumpStart](https://social-lancer.com) UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you pick the approach that finest suits 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, pick Studio in the navigation pane. +2. First-time users will be prompted to produce a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model browser displays available models, with details like the supplier name and design capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card reveals crucial details, including:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if relevant), showing that this model can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to see the design details page.
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The design details page consists of the following details:
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- The design name and provider details. +Deploy button to release 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 guidelines
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Before you release the design, it's recommended to examine the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, utilize the instantly produced name or [pediascape.science](https://pediascape.science/wiki/User:Faith61S993) develop a customized one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For [Initial circumstances](http://tesma.co.kr) count, enter the variety of instances (default: 1). +Selecting suitable [instance](https://app.joy-match.com) types and counts is essential for expense and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we highly suggest adhering to SageMaker JumpStart default settings and making certain that [network seclusion](https://www.flytteogfragttilbud.dk) remains in place. +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 is total, your endpoint status will change to InService. At this moment, the model is ready to accept inference requests through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is total, you can conjure up the design utilizing a SageMaker runtime client and incorporate it with your applications.
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Deploy DeepSeek-R1 using 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 permissions](https://dating.checkrain.co.in) and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:FabianQ0253599) reasoning programmatically. The code for [deploying](http://115.159.107.1173000) the model is [supplied](https://nextcode.store) in the Github here. You can clone the note pad 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 reasoning 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 using the Amazon Bedrock console or the API, and execute it as revealed in the following code:
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Tidy up
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To avoid unwanted 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](https://aloshigoto.jp) the design utilizing Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments. +2. In the Managed implementations section, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, [pick Delete](https://www.hyxjzh.cn13000). +4. Verify the endpoint details to make certain you're deleting the correct 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 model you deployed will [sustain expenses](https://dramatubes.com) 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 explored 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 get started. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart [pretrained](https://remnantstreet.com) 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.rozgar.site) business construct ingenious solutions utilizing AWS services and accelerated calculate. Currently, he is focused on establishing techniques for fine-tuning and optimizing the reasoning performance of big language models. In his spare time, Vivek enjoys treking, enjoying movies, and trying various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://connect.taifany.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://picturegram.app) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and [Bioinformatics](https://careers.cblsolutions.com).
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://forum.infinity-code.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://app.deepsoul.es) hub. She is enthusiastic about building solutions that assist clients accelerate their [AI](https://coopervigrj.com.br) journey and unlock company value.
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