commit d2e5db97ef04d82d829d233d0862cdaa12a01a2b Author: karinachestnut Date: Wed May 28 06:36:40 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..319cf52 --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://git.toolhub.cc)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative [AI](https://jamesrodriguezclub.com) concepts on AWS.
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In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the [distilled versions](http://git2.guwu121.com) of the models also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](https://git.ashcloudsolution.com) that uses support finding out to improve thinking abilities through a multi-stage [training procedure](https://skillfilltalent.com) from a DeepSeek-V3[-Base foundation](https://alldogssportspark.com). A key identifying feature is its reinforcement knowing (RL) step, which was used to improve the design's reactions beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually boosting both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, implying it's equipped to break down intricate questions and reason through them in a detailed manner. This guided reasoning process enables the model to produce more accurate, transparent, and detailed answers. This design combines RL-based [fine-tuning](https://inspiredcollectors.com) with CoT capabilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has caught the industry's attention as a flexible text-generation model that can be incorporated into different workflows such as representatives, logical reasoning and information analysis tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion criteria, allowing efficient inference by [routing questions](http://blueroses.top8888) to the most appropriate professional "clusters." This approach enables the model to [concentrate](http://www.hanmacsamsung.com) on different problem domains while maintaining general effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. 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 providing 1128 GB of [GPU memory](https://job-daddy.com).
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more effective designs to simulate the behavior and thinking patterns of the larger DeepSeek-R1 model, utilizing it as an instructor design.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest [deploying](https://bolsadetrabajo.tresesenta.mx) this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent hazardous content, and assess models against essential security requirements. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://yourfoodcareer.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 if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're using 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 limit increase, develop a request and reach out to your account team.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For instructions, see Set up permissions to utilize guardrails for content filtering.
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Implementing guardrails with the [ApplyGuardrail](https://recrutementdelta.ca) API
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Amazon Bedrock Guardrails allows you to introduce safeguards, avoid hazardous material, and evaluate models against key security requirements. You can [implement safety](http://gogs.gzzzyd.com) steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and model 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 circulation includes the following actions: 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 out to the model for reasoning. After getting the model'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 intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections show inference using 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 foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
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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 utilize the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [service provider](https://www.tqmusic.cn) and choose the DeepSeek-R1 design.
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The design detail page offers necessary details about the model's capabilities, rates structure, and execution standards. You can find detailed usage instructions, including sample [API calls](https://git.manu.moe) and code bits for integration. The design supports different text generation tasks, consisting of material creation, code generation, and [question](https://social.midnightdreamsreborns.com) answering, utilizing its support discovering optimization and CoT reasoning abilities. +The page also consists of release choices and licensing details to assist you get started with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, choose Deploy.
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You will be triggered to set up the release details for DeepSeek-R1. The design 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, 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 circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function consents, and file [encryption settings](https://sugardaddyschile.cl). For most utilize cases, the default settings will work well. However, for [production](https://gitlab-dev.yzone01.com) releases, you might want to review these settings to align with your organization's security and [compliance requirements](https://www.flytteogfragttilbud.dk). +7. Choose Deploy to begin using the model.
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When the deployment is complete, you can check 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 explore various triggers and change model specifications like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For instance, content for inference.
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This is an excellent way to explore the [design's reasoning](http://valueadd.kr) and text generation abilities before [integrating](https://talentsplendor.com) it into your applications. The play ground offers instant feedback, [assisting](https://mhealth-consulting.eu) you comprehend how the model reacts to different inputs and letting you tweak your prompts for optimal outcomes.
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You can quickly evaluate the model in the play area through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference 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 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures inference specifications, and sends out a request to create 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, integrated algorithms, and prebuilt ML [options](https://nemoserver.iict.bas.bg) that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and release them into production using either the UI or SDK.
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[Deploying](https://younetwork.app) DeepSeek-R1 model through SageMaker JumpStart uses two practical methods: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you choose the method that best fits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the [SageMaker](http://1.92.66.293000) console, pick Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the [SageMaker Studio](https://astonvillafansclub.com) console, choose JumpStart in the navigation pane.
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The design web browser displays available designs, with details like the supplier name and design capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design 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), suggesting that this model can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to view the design details page.
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The model details page [consists](http://aat.or.tz) of the following details:
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- The model name and service provider details. +Deploy button to release the model. +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 model, it's advised to evaluate the design details and license terms to validate compatibility with your use case.
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6. [Choose Deploy](https://git.perrocarril.com) to proceed with release.
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7. For Endpoint name, utilize the instantly created name or [photorum.eclat-mauve.fr](http://photorum.eclat-mauve.fr/profile.php?id=250144) develop a custom one. +8. For example type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of circumstances (default: 1). +[Selecting suitable](http://1.14.125.63000) instance types and counts is crucial for expense and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, [Real-time reasoning](http://lnsbr-tech.com) is chosen by default. This is [enhanced](https://livesports808.biz) for sustained traffic and low latency. +10. Review all configurations for precision. For this design, 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 implementation procedure can take numerous minutes to finish.
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When release is complete, your endpoint status will change to InService. At this moment, the design is prepared to accept reasoning demands through the [endpoint](http://39.108.93.0). You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is complete, you can conjure up the [design utilizing](http://expertsay.blog) 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 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying 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 requests 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 develop a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Clean up
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To prevent unwanted charges, complete the steps in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the model utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases. +2. In the Managed deployments section, locate the endpoint you want to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the [endpoint details](https://wathelp.com) to make certain you're deleting 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 model you released will sustain costs 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](https://wellandfitnessgn.co.kr) out how you can access and deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions [Architect](https://git.137900.xyz) for Inference at AWS. He helps emerging generative [AI](https://mypocket.cloud) business construct ingenious solutions utilizing AWS services and accelerated compute. Currently, he is focused on establishing techniques for fine-tuning and enhancing the inference performance of large language models. In his spare time, Vivek enjoys treking, watching movies, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://git.gonstack.com) Specialist Solutions Architect with the Third-Party Model [Science](https://www.globaltubedaddy.com) group at AWS. His area of focus is AWS [AI](http://47.114.82.162:3000) 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 [dealing](http://www.thegrainfather.com.au) with generative [AI](https://mysazle.com) with the Third-Party Model [Science](https://git.easytelecoms.fr) group 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](https://www.bolsadetrabajotafer.com) center. She is enthusiastic about constructing options that assist customers accelerate their [AI](https://medea.medianet.cs.kent.edu) journey and unlock organization value.
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