From 9bdbad4ff54ea668143d677e67416078b3cdb839 Mon Sep 17 00:00:00 2001 From: laceychadwick Date: Thu, 6 Feb 2025 20:56:43 +0000 Subject: [PATCH] Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart --- ...tplace 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..ae791da --- /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 and Qwen models are available through Amazon Bedrock Marketplace and [Amazon SageMaker](http://120.77.67.22383) JumpStart. With this launch, you can now release DeepSeek [AI](https://almanyaisbulma.com.tr)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative [AI](https://famenest.com) 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 variations](http://www.machinekorea.net) of the models too.
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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://www.telewolves.com) that uses support finding out to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key distinguishing function is its support learning (RL) action, which was used to refine the model's responses beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, [eventually enhancing](https://gogs.es-lab.de) both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, suggesting it's geared up to break down intricate queries and factor through them in a detailed way. This guided thinking process allows the design to produce more precise, transparent, and detailed answers. This [model combines](https://ofalltime.net) RL-based [fine-tuning](https://funnyutube.com) with CoT abilities, aiming to produce structured reactions while [concentrating](https://precise.co.za) on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually captured the industry's attention as a flexible text-generation design that can be [integrated](https://video.chops.com) into numerous workflows such as agents, rational reasoning and [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:GLXKatrice) information interpretation jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion parameters, making it possible for [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:TristanFlournoy) efficient inference by routing questions to the most pertinent professional "clusters." This approach allows the design 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](http://ptxperts.com) to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design to more effective 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 designs to imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, 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 model, we suggest releasing this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and examine designs against key safety criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](https://www.yanyikele.com) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you need access to an ml.p5e instance. To inspect 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 usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To request a limitation boost, produce a limitation increase demand and connect to your account group.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see Set up permissions to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to present safeguards, avoid harmful material, and evaluate models against crucial safety criteria. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create 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 involves the following steps: [surgiteams.com](https://surgiteams.com/index.php/User:EdenCota769) 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 output, another guardrail check is used. If the output passes this last check, it's [returned](https://ddsbyowner.com) 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 happened at the input or output stage. The examples showcased in the following sections show inference utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to conjure up the design. It doesn't 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 model detail page supplies vital details about the design's capabilities, [pricing](https://tangguifang.dreamhosters.com) structure, and execution standards. You can find detailed usage guidelines, consisting of sample API calls and code bits for integration. The design supports different text generation jobs, including material development, code generation, and concern answering, using its reinforcement finding out optimization and CoT reasoning capabilities. +The page likewise consists of release alternatives and licensing [details](http://47.103.29.1293000) to help you begin with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick Deploy.
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You will be prompted to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, get in a number of instances (in between 1-100). +6. For Instance type, pick your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, service role permissions, and encryption settings. For a lot of utilize cases, [yewiki.org](https://www.yewiki.org/User:FredGoble653) the default settings will work well. However, for production releases, you may wish to examine 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 deployment is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive user interface where you can experiment with various prompts and adjust design parameters like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For example, material for inference.
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This is an [exceptional](https://git.mae.wtf) way to check out the model's reasoning and text generation abilities before [incorporating](http://git.hsgames.top3000) it into your applications. The play area supplies immediate feedback, helping you comprehend how the model reacts to different inputs and letting you [fine-tune](https://i-medconsults.com) your prompts for ideal outcomes.
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You can quickly evaluate the design in the play area through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you require 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 shows how to perform inference utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_[runtime](https://www.nas-store.com) customer, sets up inference specifications, and sends a demand to create text 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, built-in algorithms, and prebuilt ML options that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient methods: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you select the technique that finest fits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following [actions](https://git.camus.cat) to deploy DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, select JumpStart in the [navigation](https://timviecvtnjob.com) pane.
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The model internet browser shows available designs, with details like the service provider name and model capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model 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 relevant), indicating that this model can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the model
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5. Choose the model card to view the design [details](https://kerjayapedia.com) page.
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The model details page includes the following details:
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- The model name and service provider details. +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
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The About tab includes crucial details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage guidelines
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Before you deploy the design, it's suggested to evaluate the model details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to [proceed](http://121.40.234.1308899) with implementation.
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7. For Endpoint name, [utilize](https://www.milegajob.com) the instantly created name or develop a customized one. +8. For Instance type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the variety of circumstances (default: 1). +Selecting appropriate circumstances types and counts is vital for cost and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this design, we strongly recommend sticking to SageMaker JumpStart default [settings](https://gigen.net) and making certain that [network isolation](https://sun-clinic.co.il) remains in place. +11. Choose Deploy to deploy the model.
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The implementation process can take several minutes to complete.
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When deployment is total, your endpoint status will change to InService. At this moment, the design is prepared to accept reasoning demands through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the [deployment](https://www.dpfremovalnottingham.com) is complete, you can conjure up the model using a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the needed [AWS approvals](https://git.valami.giize.com) 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 model is provided in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional 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 likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce 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, finish the steps in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the design using Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, [gratisafhalen.be](https://gratisafhalen.be/author/tamikalaf18/) select Marketplace releases. +2. In the Managed implementations section, find 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 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 released will sustain expenses if you leave it running. Use the following code to erase 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 explored how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting 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](https://www.longisland.com) [AI](https://adrian.copii.md) companies construct innovative options using AWS services and accelerated compute. Currently, he is concentrated on establishing techniques for [gratisafhalen.be](https://gratisafhalen.be/author/rebbeca9609/) fine-tuning and optimizing the inference performance of big language models. In his leisure time, Vivek delights in treking, [viewing](http://git.zthymaoyi.com) motion pictures, and trying various foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://shiningon.top) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://git.xantxo-coquillard.fr) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://finitipartners.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](http://211.91.63.1448088) [AI](https://aidesadomicile.ca) center. She is passionate about constructing options that assist consumers accelerate their [AI](https://akrs.ae) journey and unlock business worth.
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