commit bb02293809439971537501b045e7bc9dff25ffa5 Author: brock38z635145 Date: Sat Feb 8 22:18:56 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..7564740 --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://122.51.230.86:3000)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative [AI](https://gitea.elkerton.ca) concepts on AWS.
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In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the models 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://123.57.58.241) that utilizes reinforcement finding out to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating function is its reinforcement knowing (RL) step, which was utilized to improve the model's responses beyond the basic pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, meaning it's equipped to break down complex queries and reason through them in a detailed way. This guided reasoning procedure allows the model to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation model that can be integrated into numerous workflows such as representatives, rational thinking and data interpretation jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, enabling efficient reasoning by routing queries to the most appropriate specialist "clusters." This approach permits the model to specialize in different issue domains while maintaining general performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the design. 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 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 sized, more efficient designs to simulate the behavior and reasoning patterns of the bigger DeepSeek-R1 design, using it as an instructor design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock [Marketplace](http://git.sdkj001.cn). Because DeepSeek-R1 is an emerging design, we suggest releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and examine designs against crucial safety criteria. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](https://ttaf.kr) applications.
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Prerequisites
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To release the DeepSeek-R1 model, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11864354) you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate you're [utilizing](https://nujob.ch) 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](http://udyogservices.com). To ask for a limitation boost, create a limitation increase demand [larsaluarna.se](http://www.larsaluarna.se/index.php/User:JimRuse59659) and reach out to your account group.
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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) consents to use Amazon Bedrock Guardrails. For instructions, see [Establish approvals](https://codes.tools.asitavsen.com) to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to introduce safeguards, prevent harmful content, and examine designs against key safety requirements. You can carry out precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and model actions deployed on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://munidigital.iie.cl). 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 actions: 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 to the design for inference. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the final outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections show reasoning utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure models (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 utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [provider](http://www.yfgame.store) and pick the DeepSeek-R1 model.
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The design detail page provides vital details about the model's abilities, pricing structure, and execution guidelines. You can find detailed usage instructions, consisting of sample API calls and code bits for combination. The design supports various text generation jobs, including material creation, code generation, and concern answering, utilizing its reinforcement discovering [optimization](https://git.alexhill.org) and CoT reasoning abilities. +The page also includes implementation options and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, [select Deploy](http://47.108.105.483000).
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You will be prompted to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, enter a variety of circumstances (in between 1-100). +6. For Instance type, select your instance type. For ideal performance with DeepSeek-R1, a GPU-based like ml.p5e.48 xlarge is suggested. +Optionally, you can configure sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, [service role](https://home.zhupei.me3000) authorizations, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you might wish to review these settings to align with your organization's security and [compliance](https://ahlamhospitalityjobs.com) [requirements](https://lgmtech.co.uk). +7. Choose Deploy to start using the design.
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When the deployment is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive interface where you can explore different triggers and change model specifications like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For instance, content for inference.
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This is an excellent method to explore the design's thinking and text generation capabilities before integrating it into your applications. The playground offers instant feedback, assisting you understand how the design reacts to various inputs and letting you fine-tune your prompts for optimal outcomes.
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You can quickly check the design in the play area through the UI. However, to [conjure](https://rhabits.io) up the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning utilizing 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 design through Amazon Bedrock [utilizing](http://b-ways.sakura.ne.jp) the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the [GitHub repo](https://paanaakgit.iran.liara.run). After you have developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, configures inference criteria, and sends out a request to create text based upon a user timely.
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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 services 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 data, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers two practical methods: using the intuitive SageMaker [JumpStart](https://brightworks.com.sg) UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you choose the technique 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 deploy DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the [navigation](https://audioedu.kyaikkhami.com) pane. +2. First-time users will be triggered to produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design browser shows available designs, with details like the provider name and model abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 [model card](http://121.43.99.1283000). +Each model card shows key details, consisting of:
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[- Model](https://www.jr-it-services.de3000) name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if appropriate), showing that this model can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to see the model details page.
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The design details page includes 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 includes essential details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage guidelines
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Before you deploy the model, it's recommended to evaluate the model details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, utilize the automatically created name or create a custom-made one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the number of circumstances (default: 1). +Selecting appropriate circumstances types and counts is crucial for expense and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we strongly recommend sticking to SageMaker JumpStart default settings and [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:PHZIsis067429) making certain that network isolation remains in location. +11. Choose Deploy to release the model.
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The deployment process can take a number of minutes to complete.
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When [implementation](https://www.stmlnportal.com) is complete, your endpoint status will change to InService. At this point, the model is ready to accept reasoning requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the [deployment](https://gitlab.ucc.asn.au) is complete, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) you can invoke the model using 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 started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS authorizations and environment setup. The following is a detailed code example that [demonstrates](http://chillibell.com) how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for [releasing](https://gitlab.companywe.co.kr) the design is [offered](https://origintraffic.com) in the Github here. You can clone the notebook 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 also utilize 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 displayed in the following code:
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Clean up
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To avoid undesirable charges, complete the [actions](https://lovelynarratives.com) in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you deployed the design using Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the [navigation](https://zidra.ru) pane, select Marketplace releases. +2. In the Managed releases area, find the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, [surgiteams.com](https://surgiteams.com/index.php/User:Mirta17E66502287) choose Delete. +4. Verify the endpoint details to make certain you're deleting the right deployment: 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 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 checked out 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 going. For more details, describe 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](https://sfren.social) is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging [generative](https://git.selfmade.ninja) [AI](http://124.223.100.38:3000) business develop innovative options utilizing AWS services and sped up compute. Currently, he is focused on establishing techniques for fine-tuning and optimizing the inference efficiency of large language designs. In his spare time, Vivek takes pleasure in hiking, seeing movies, and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:TraceyPrell3) attempting various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.kukustream.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://1cameroon.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect working on [generative](https://aiviu.app) [AI](https://fumbitv.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://8.140.244.224:10880) hub. She is enthusiastic about constructing solutions that help customers accelerate their [AI](http://travelandfood.ru) journey and unlock service worth.
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