From 0ad598911d65dfb3ec2e230a580691c843d5bcc1 Mon Sep 17 00:00:00 2001 From: martaa14473286 Date: Wed, 2 Apr 2025 19:56:45 +0000 Subject: [PATCH] Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-And-Amazon-SageMaker-JumpStart.md | 93 +++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md 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..17f736c --- /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](https://www.allclanbattles.com). With this launch, you can now release DeepSeek [AI](https://code.cypod.me)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion parameters to construct, experiment, and properly scale your generative [AI](https://www.social.united-tuesday.org) 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 deploy the distilled versions 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://gitea.star-linear.com) that uses reinforcement finding out to enhance [thinking capabilities](https://code.oriolgomez.com) through a multi-stage training procedure from a DeepSeek-V3[-Base structure](https://sss.ung.si). A crucial identifying function is its support learning (RL) step, which was utilized to improve the model's responses beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, ultimately improving both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, implying it's geared up to break down intricate questions and factor through them in a detailed manner. This guided reasoning process enables the design to [produce](http://b-ways.sakura.ne.jp) more precise, transparent, and detailed answers. This design combines [RL-based fine-tuning](http://8.142.152.1374000) with CoT capabilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually captured the market's attention as a versatile text-generation design that can be incorporated into numerous workflows such as representatives, sensible thinking and information analysis tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion criteria, enabling effective inference by [routing queries](http://8.136.42.2418088) to the most pertinent professional "clusters." This method permits the model to focus on various problem domains while maintaining general effectiveness. 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 comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 model to more efficient architectures based on [popular](https://gl.cooperatic.fr) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more effective models to the habits and 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 design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent hazardous material, and assess 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 just the ApplyGuardrail API. You can develop numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 design, improving user experiences and [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2684771) standardizing safety controls across your generative [AI](https://www.noagagu.kr) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:BonnieValle7) select Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To request a limitation increase, develop a limit increase request and connect to your account team.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock [Guardrails](http://211.117.60.153000). For directions, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:KimMilford) see Establish approvals to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to present safeguards, prevent hazardous material, and assess designs against crucial security requirements. You can carry out safety procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to [evaluate](http://www.pygrower.cn58081) user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.
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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 design for reasoning. After getting the design's output, another guardrail check is used. If the output passes this last check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred 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 gives you access to over 100 popular, emerging, and specialized structure models (FMs) through [Amazon Bedrock](https://git.sunqida.cn). To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, choose Model catalog under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 design.
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The model detail page provides vital details about the design's abilities, rates structure, and execution standards. You can find detailed use guidelines, consisting of sample API calls and code bits for combination. The model supports various text generation tasks, including material production, code generation, and question answering, utilizing its support discovering optimization and CoT reasoning abilities. +The page also includes deployment choices and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
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You will be [prompted](http://www.0768baby.com) to configure the release 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 circumstances, go into a number of instances (in between 1-100). +6. For Instance type, pick your instance type. For optimum performance with DeepSeek-R1, a [GPU-based instance](https://git.coalitionofinvisiblecolleges.org) type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up innovative security and facilities settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and file encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you may wish to review these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the release is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive user interface where you can try out various prompts and adjust model criteria like temperature level and maximum length. +When using 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 method to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The play ground supplies instant feedback, helping you comprehend how the [design reacts](http://www.0768baby.com) to different inputs and letting you fine-tune your prompts for ideal results.
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You can quickly check the model in the play ground 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 reasoning using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, configures inference criteria, and sends a request to [produce text](http://8.134.237.707999) 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) center with FMs, built-in algorithms, and prebuilt ML services that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart offers two practical methods: utilizing the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you pick the approach that best suits 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, pick Studio in the navigation pane. +2. First-time users will be triggered to produce a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The design web browser shows available designs, with details like the provider name and model capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design card reveals key details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if relevant), suggesting that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon [Bedrock APIs](https://jamesrodriguezclub.com) to invoke the design
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5. Choose the model card to view the model details page.
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The design details page includes the following details:
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- The model name and service provider details. +[Deploy button](https://git.io8.dev) to release the design. +About and Notebooks tabs with detailed details
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The About tab consists of important details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage guidelines
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Before you release the model, it's advised to evaluate the design details and [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:JoniNey672) license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, utilize the immediately created name or create a custom-made one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of circumstances (default: 1). +Selecting suitable instance types and counts is vital for expense and [performance optimization](https://heatwave.app). Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for accuracy. For this model, we strongly [advise adhering](http://115.29.48.483000) to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the design.
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The deployment process can take numerous minutes to finish.
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When release is total, your endpoint status will change to InService. At this moment, the design is prepared to accept reasoning requests through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the deployment is complete, you can conjure up the design utilizing a SageMaker runtime customer and incorporate 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 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the needed AWS permissions and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for releasing 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://kurva.su) 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 use 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 revealed in the following code:
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Tidy up
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To avoid unwanted charges, complete the steps in this section to clean up your resources.
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Delete the Amazon Bedrock [Marketplace](http://www.carnevalecommunity.it) release
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If you [released](https://test.bsocial.buzz) 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](https://source.ecoversities.org) pane, pick Marketplace releases. +2. In the Managed implementations area, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the right implementation: 1. [Endpoint](https://coopervigrj.com.br) 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 deployed will sustain costs if you leave it running. Use the following code to delete the endpoint if you wish to stop [sustaining charges](http://47.92.149.1533000). 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 using 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 designs, SageMaker JumpStart pretrained models, 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://realmadridperipheral.com) business construct innovative solutions using AWS services and accelerated compute. Currently, he is focused on establishing methods for fine-tuning and enhancing the inference efficiency of large language models. In his leisure time, Vivek delights in treking, watching films, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://98.27.190.224) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://myjobasia.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and [Bioinformatics](https://www.linkedaut.it).
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](http://git.aivfo.com:36000) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](https://www.ssecretcoslab.com) [AI](http://tobang-bangsu.co.kr) hub. She is passionate about building options that help consumers accelerate their [AI](http://8.140.200.236:3000) journey and unlock business value.
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