commit fd997bc48c88d92957563cb13064d48c8fd782a4 Author: robertcockrell Date: Thu May 29 17:35:57 2025 +0000 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..c2e1382 --- /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 deploy DeepSeek [AI](https://rosaparks-ci.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion criteria to construct, [yewiki.org](https://www.yewiki.org/User:MaisieRoldan5) experiment, and properly scale your generative [AI](https://somkenjobs.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 similar 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 large language design (LLM) established by DeepSeek [AI](https://surgiteams.com) that uses support finding out to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying function is its reinforcement learning (RL) step, which was utilized to improve the design's actions beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually enhancing both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's geared up to break down complicated queries and factor through them in a detailed manner. This directed thinking procedure enables the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually captured the market's attention as a flexible text-generation design that can be incorporated into numerous workflows such as representatives, sensible reasoning and information interpretation jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion parameters, allowing effective [reasoning](http://221.229.103.5563010) by routing queries to the most relevant expert "clusters." This method permits the model to focus on different issue domains while [maintaining](http://185.254.95.2413000) total performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge comes with 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 efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more efficient designs to imitate the habits and [reasoning patterns](https://ruofei.vip) of the larger DeepSeek-R1 model, using 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 model, we recommend deploying this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful content, and evaluate designs against key safety [requirements](https://asesordocente.com). At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, [Bedrock Guardrails](https://voyostars.com) supports only the ApplyGuardrail API. You can create numerous [guardrails tailored](https://www.youmanitarian.com) to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](https://ruraltv.co.za) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas [console](https://app.joy-match.com) and under AWS Services, choose Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limitation increase, create a limit increase demand and connect to your account group.
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Because you will be [deploying](https://funitube.com) this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon [Bedrock Guardrails](https://gitlab.xfce.org). For guidelines, see Establish permissions to use guardrails for material filtering.
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[Implementing](https://www.jobseeker.my) guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to present safeguards, avoid damaging content, and examine models against essential safety criteria. You can execute precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and design responses 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 create the guardrail, see the GitHub repo.
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The general circulation involves the following actions: 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 inference. After receiving the model's output, another guardrail check is [applied](https://virtualoffice.com.ng). If the output passes this final check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following sections demonstrate 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 foundation](https://labs.hellowelcome.org) models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, pick Model catalog under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the [InvokeModel API](http://47.114.187.1113000) to [conjure](https://premiergitea.online3000) up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 model.
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The model detail page supplies necessary details about the design's capabilities, [pricing](https://pojelaime.net) structure, and execution guidelines. You can find detailed usage instructions, consisting of [sample API](http://39.108.93.0) calls and code bits for combination. The design supports numerous text generation tasks, consisting of content development, code generation, and question answering, using its support finding out optimization and CoT reasoning capabilities. +The page also consists of deployment choices and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, choose Deploy.
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You will be prompted to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For [Variety](https://www.remotejobz.de) of circumstances, enter a number of circumstances (in between 1-100). +6. For Instance type, choose your instance type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure sophisticated security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function authorizations, and [encryption](https://sing.ibible.hk) settings. For many use cases, the default settings will work well. However, for releases, you may wish to review these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the deployment is total, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive interface where you can experiment with different prompts and change model criteria like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal results. For instance, content for reasoning.
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This is an excellent method to explore the design's thinking and text generation abilities before integrating it into your applications. The play area supplies instant feedback, helping you [comprehend](http://poscotech.co.kr) how the model reacts to various inputs and letting you tweak your triggers for optimum results.
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You can rapidly test the design in the play area through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning using a released DeepSeek-R1 design through Amazon Bedrock using 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. After you have produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference criteria, and sends out a request to produce 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) center with FMs, built-in algorithms, and prebuilt ML [solutions](https://messengerkivu.com) that you can deploy 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 using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 convenient techniques: utilizing the instinctive SageMaker JumpStart UI or [gratisafhalen.be](https://gratisafhalen.be/author/vonniehagai/) executing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you pick the approach that best fits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:KelliFontenot56) pick Studio in the navigation pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design browser displays available designs, with details like the supplier name and model capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 [design card](http://gitlab.ifsbank.com.cn). +Each design card reveals key details, consisting of:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if suitable), [indicating](http://forum.altaycoins.com) that this design can be signed up with Amazon Bedrock, enabling you to use [Amazon Bedrock](https://jobsdirect.lk) APIs to invoke the model
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5. Choose the design card to view the design details page.
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The design details page includes the following details:
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- The model name and company details. +Deploy button to release the design. +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 requirements. +- Usage standards
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Before you deploy the design, it's advised to evaluate the design details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the immediately generated name or create a custom-made one. +8. For Instance type ΒΈ pick an [instance type](https://deprezyon.com) (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of instances (default: 1). +Selecting appropriate circumstances types and counts is important for [wiki.myamens.com](http://wiki.myamens.com/index.php/User:DelbertVallery) expense and efficiency optimization. Monitor your implementation to change these [settings](https://www.pakalljobz.com) as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low [latency](http://metis.lti.cs.cmu.edu8023). +10. Review all configurations for precision. For this design, we highly suggest adhering 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 release process can take numerous minutes to complete.
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When deployment is complete, your endpoint status will alter to InService. At this point, the model is all set to accept inference requests through the endpoint. You can keep track of the release progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is complete, you can invoke the design utilizing a SageMaker runtime client and integrate 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 require to install the [SageMaker Python](http://bolling-afb.rackons.com) SDK and make certain you have the needed AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for [inference programmatically](http://git.chuangxin1.com). The code for releasing the design is supplied in the Github here. You can clone the notebook 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 reasoning 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 develop a guardrail using 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 prevent undesirable charges, finish the steps in this section to clean 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 steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases. +2. In the Managed implementations area, locate the endpoint you want to erase. +3. Select the endpoint, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1323555) and on the Actions menu, select 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 deployed will sustain costs if you leave it running. Use the following code to delete the endpoint if you want 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 deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, refer to Use Amazon Bedrock [tooling](https://www.personal-social.com) 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 helps emerging generative [AI](https://kol-jobs.com) companies develop ingenious options using AWS services and sped up compute. Currently, he is concentrated on establishing strategies for fine-tuning and enhancing the reasoning performance of large language designs. In his spare time, Vivek delights in treking, enjoying movies, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://somo.global) Specialist Solutions Architect with the [Third-Party Model](http://motojic.com) Science group at AWS. His location of focus is AWS [AI](https://209rocks.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 [AI](https://10mektep-ns.edu.kz) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.lewis.id) hub. She is enthusiastic about building services that help consumers accelerate their [AI](https://kaamdekho.co.in) journey and unlock organization worth.
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