commit 4c64662061a9be649448c7c874e190ca5e0382e2 Author: petra472666688 Date: Wed Apr 9 07:31:32 2025 +0800 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..e7d79fa --- /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 designs are available through Amazon Bedrock Marketplace and [Amazon SageMaker](https://snapfyn.com) JumpStart. With this launch, you can now release DeepSeek [AI](http://203.171.20.94:3000)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](https://www.runsimon.com) ideas 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 of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](http://8.140.244.224:10880) that uses support discovering to improve thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key distinguishing function is its reinforcement knowing (RL) step, which was utilized to improve the design's responses beyond the basic pre-training and tweak process. By [incorporating](https://git.lolilove.rs) RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, indicating it's geared up to break down and factor through them in a detailed manner. This guided reasoning procedure permits the design to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has captured the industry's attention as a [versatile text-generation](https://robbarnettmedia.com) design that can be incorporated into different workflows such as representatives, rational reasoning and data analysis tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion criteria, enabling effective reasoning by routing questions to the most appropriate specialist "clusters." This approach enables the design to specialize in various issue domains while maintaining overall efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for [reasoning](http://zhangsheng1993.tpddns.cn3000). In this post, we will utilize an ml.p5e.48 [xlarge circumstances](http://www.iilii.co.kr) to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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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 refers to a process of training smaller, more [effective models](https://www.cbl.aero) to imitate the habits and reasoning patterns of the larger DeepSeek-R1 design, 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 design, we advise deploying this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging content, and examine designs against essential security criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several [guardrails tailored](https://kaymack.careers) to different usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://hyperwrk.com) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you require 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 circumstances in the AWS Region you are releasing. To request a limitation increase, produce a limitation boost demand and reach out to your account team.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) authorizations to use [Amazon Bedrock](https://hatchingjobs.com) Guardrails. For guidelines, see Set up approvals to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging material, and examine designs against crucial security criteria. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and model reactions [deployed](https://www.scikey.ai) 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 create the guardrail, see the GitHub repo.
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The basic circulation involves the following steps: First, the system receives 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 receiving the model's output, another guardrail check is used. If the output passes this last check, it's [returned](http://yanghaoran.space6003) as the last outcome. However, if either the input or output is intervened by the guardrail, a message is [returned](https://cloudsound.ideiasinternet.com) showing the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following areas demonstrate inference using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock [Marketplace](https://video.chops.com) gives 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 actions:
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1. On the Amazon Bedrock console, choose Model catalog under Foundation models in the navigation pane. +At the time of writing 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 pick the DeepSeek-R1 model.
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The model detail page provides essential details about the design's abilities, prices structure, and execution guidelines. You can discover detailed use directions, consisting of sample API calls and code snippets for combination. The design supports different text generation tasks, consisting of material development, code generation, and concern answering, utilizing its support discovering optimization and CoT thinking abilities. +The page also includes deployment choices and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, pick Deploy.
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You will be prompted to set up the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, go into a variety of instances (between 1-100). +6. For Instance type, select your instance type. For optimal efficiency with DeepSeek-R1, a [GPU-based](http://www.iilii.co.kr) instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up sophisticated security and infrastructure settings, including virtual private cloud (VPC) networking, service function permissions, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you might wish to review these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the implementation is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive interface where you can try out different prompts and adjust model parameters like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For instance, content for inference.
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This is an excellent method to check out the design's reasoning and text generation capabilities before integrating it into your applications. The playground offers immediate feedback, assisting you understand how the model responds to different inputs and [letting](https://cmegit.gotocme.com) you fine-tune your triggers for optimal outcomes.
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You can rapidly check the design in the playground through the UI. However, to conjure up the deployed 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 carry out inference using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, configures inference criteria, and sends out a demand to create 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, built-in algorithms, and prebuilt ML options that you can deploy with just a few 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](https://pojelaime.net) DeepSeek-R1 model through SageMaker JumpStart uses two practical approaches: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both [techniques](https://www.designxri.com) to assist you choose the technique that finest fits your [requirements](https://jobs.quvah.com).
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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 console, select Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model [browser](http://release.rupeetracker.in) 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 design card. +Each design card shows essential details, consisting of:
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[- Model](https://www.hirerightskills.com) name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if relevant), indicating that this design can be registered with Amazon Bedrock, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:RaymonQ0833) permitting you to use Amazon Bedrock APIs to invoke the model
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5. Choose the model card to view the design details page.
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The design details page consists of the following details:
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- The model 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 essential details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage standards
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Before you deploy the model, it's recommended to review the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, use the immediately created name or produce a custom-made one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial [instance](http://43.137.50.31) count, enter the variety of circumstances (default: 1). +Selecting suitable instance types and counts is crucial for cost and efficiency optimization. Monitor [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/willisrosson) your implementation to change these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we strongly recommend sticking to [SageMaker JumpStart](https://holisticrecruiters.uk) default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the model.
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The release process can take numerous minutes to finish.
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When deployment is total, your endpoint status will alter to InService. At this point, the design is all set to accept reasoning demands through the endpoint. You can keep track of the implementation development on the SageMaker console [Endpoints](https://chhng.com) page, which will display appropriate metrics and status details. When the [deployment](http://47.104.234.8512080) is complete, you can invoke the design using a SageMaker runtime client and integrate 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 need to set up the SageMaker Python SDK and make certain you have the necessary AWS permissions and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is provided in the Github here. You can clone the note pad and run 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 also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing 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 avoid undesirable 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 the model utilizing Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases. +2. In the Managed releases area, find the endpoint you want to delete. +3. Select the endpoint, and on the Actions menu, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:MacFalls93386606) choose Delete. +4. Verify the endpoint details to make certain you're erasing the right 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 design you [released](https://test.bsocial.buzz) 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 out how you can access and release the DeepSeek-R1 design 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 designs, SageMaker JumpStart pretrained models, 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 for Inference at AWS. He assists emerging generative [AI](http://47.93.156.192:7006) business develop ingenious services using AWS services and sped up compute. Currently, he is concentrated on establishing strategies for fine-tuning and [optimizing](https://www.jobs.prynext.com) the reasoning efficiency of big language models. In his leisure time, Vivek delights in treking, viewing motion pictures, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://newsfast.online) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://bertlierecruitment.co.za) 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](http://39.105.129.229:3000) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://plus-tube.ru) hub. She is enthusiastic about building options that assist consumers accelerate their [AI](https://www.celest-interim.fr) journey and unlock organization value.
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