commit 59048ad68ad5a52d97c176b167223c18a0f5d09d Author: krystlemichals Date: Fri Apr 11 01:38:06 2025 +0300 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..311f884 --- /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 reveal that DeepSeek R1 [distilled Llama](https://git.googoltech.com) and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://187.216.152.151:9999)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions [ranging](http://39.101.134.269800) from 1.5 to 70 billion [criteria](https://ezworkers.com) to build, experiment, and properly scale your generative [AI](https://gitea-working.testrail-staging.com) ideas on AWS.
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In this post, we demonstrate how to start with DeepSeek-R1 on [Amazon Bedrock](http://042.ne.jp) Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the models also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](http://1.94.127.210:3000) that uses support finding out to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial identifying function is its reinforcement knowing (RL) step, which was utilized to improve the model's reactions beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, [eventually boosting](http://git.1473.cn) both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, indicating it's equipped to break down complicated questions and factor through them in a detailed way. This assisted reasoning procedure allows the design to produce more accurate, transparent, and detailed responses. This model combines RL-based [fine-tuning](https://wolvesbaneuo.com) with CoT capabilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation design that can be integrated into numerous workflows such as agents, rational reasoning and data analysis jobs.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion criteria, making it possible for efficient reasoning by routing questions to the most relevant specialist "clusters." This [technique](https://git.dev-store.ru) allows the design to specialize in different issue domains while maintaining general effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance 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 models bring the reasoning abilities 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 efficient models to simulate the behavior and thinking 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, [disgaeawiki.info](https://disgaeawiki.info/index.php/User:FidelPoe58) we advise releasing this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and examine models against essential safety criteria. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails [supports](http://hoteltechnovalley.com) just the ApplyGuardrail API. You can produce multiple guardrails to various use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](https://jobsportal.harleysltd.com) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To [examine](https://investsolutions.org.uk) if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm you're [utilizing](http://39.101.167.1953003) 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 limit boost request and reach out to your account group.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS [Identity](https://hafrikplay.com) and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Establish permissions to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, prevent harmful content, and [examine](https://kandidatez.com) designs against key security requirements. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and design actions released 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 create the guardrail, see the GitHub repo.
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The basic [circulation](http://47.105.104.2043000) 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 model for reasoning. After getting the model's output, another guardrail check is used. If the output passes this last check, it's returned as the last outcome. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or [output phase](http://117.50.220.1918418). 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 [pipewiki.org](https://pipewiki.org/wiki/index.php/User:LindseyNzh) specialized foundation designs (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, select Model catalog under Foundation models 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 select the DeepSeek-R1 model.
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The model detail page [supplies vital](http://clipang.com) [details](https://git.epochteca.com) about the model's capabilities, prices structure, and application standards. You can find detailed usage directions, including sample API calls and code bits for combination. The design supports various text generation jobs, consisting of material creation, code generation, and question answering, utilizing its support learning optimization and CoT reasoning abilities. +The page likewise includes [deployment options](http://soho.ooi.kr) and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To begin [utilizing](https://git.li-yo.ts.net) DeepSeek-R1, choose Deploy.
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You will be prompted to set up the deployment 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 Variety of circumstances, enter a number of circumstances (between 1-100). +6. For Instance type, select your instance type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up advanced security and infrastructure settings, including virtual personal cloud (VPC) networking, service function permissions, and [encryption](https://www.sociopost.co.uk) settings. For a lot of use cases, the default settings will work well. However, for production deployments, you may want to review these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the implementation is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in playground to access an interactive user interface where you can try out different triggers and adjust design specifications like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For example, content for inference.
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This is an outstanding way to check out the model's thinking and text generation capabilities before integrating it into your applications. The playground offers instant feedback, assisting you comprehend how the model reacts to various inputs and letting you tweak your prompts for optimal results.
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You can rapidly test the design in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have actually created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures inference specifications, and sends a demand to produce 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) 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 designs to your use case, with your information, and deploy them into production using either the UI or SDK.
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[Deploying](https://git.j.co.ua) DeepSeek-R1 model through SageMaker JumpStart uses 2 practical methods: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you choose the method that best matches your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design internet browser displays available models, 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. +Each design card reveals essential details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if appropriate), showing that this model can be signed up with Amazon Bedrock, [enabling](https://tribetok.com) you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to view the design details page.
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The model details page consists of the following details:
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- The design name and service provider details. +Deploy button to release the design. +About and [Notebooks tabs](http://118.25.96.1183000) with detailed details
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The About tab includes essential details, such as:
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- Model description. +- License [details](https://gitea.bone6.com). +- Technical requirements. +- Usage guidelines
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Before you release the design, it's suggested to evaluate the [design details](http://47.76.141.283000) and license terms to validate compatibility with your use case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, use the automatically created name or create a customized one. +8. For Instance type ΒΈ pick a [circumstances type](https://git.suthby.org2024) (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the variety of instances (default: 1). +Selecting appropriate [instance types](http://www.xn--9m1b66aq3oyvjvmate.com) and counts is important for expense and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this model, we [highly recommend](https://playvideoo.com) sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. [Choose Deploy](https://autogenie.co.uk) to release the design.
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The release procedure can take numerous minutes to complete.
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When deployment is complete, your endpoint status will change to InService. At this moment, the model is all set to accept inference requests through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is total, you can conjure up the design utilizing 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 going with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the model is offered in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra demands against the predictor:
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[Implement guardrails](https://droidt99.com) and run inference 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 produce a guardrail utilizing 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 undesirable charges, complete the steps in this section to clean up your [resources](http://images.gillion.com.cn).
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Delete the Amazon Bedrock Marketplace deployment
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If you deployed the model utilizing Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace implementations. +2. In the Managed implementations section, find 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 deleting the right release: 1. Endpoint name. +2. Model name. +3. Endpoint status
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Delete the [SageMaker JumpStart](https://genzkenya.co.ke) predictor
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The SageMaker JumpStart design you deployed 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 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker [JumpStart](https://tempjobsindia.in) in SageMaker Studio or Amazon Bedrock Marketplace now to start. 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 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://www.89u89.com) companies develop ingenious services utilizing AWS services and accelerated calculate. Currently, he is focused on [establishing strategies](http://briga-nega.com) for fine-tuning and optimizing the inference efficiency of big language designs. In his leisure time, Vivek delights in hiking, [enjoying](http://git.qwerin.cz) motion pictures, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://kiwiboom.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://159.75.133.67:20080) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](https://git.tesinteractive.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://47.90.83.132:3000) center. She is passionate about developing services that assist customers accelerate their [AI](https://tv.360climatechange.com) journey and unlock organization worth.
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