commit f1531eb3db09d7bef74fa51a6e53c6499a710a28 Author: nemsavannah035 Date: Sun Apr 6 09:13:04 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..fc30429 --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are thrilled to reveal that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and [Amazon SageMaker](https://chosenflex.com) JumpStart. With this launch, you can now release DeepSeek [AI](https://streaming.expedientevirtual.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative [AI](http://118.89.58.19:3000) [concepts](https://suomalaistajalkapalloa.com) on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled versions of the models also.
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[Overview](https://www.shopes.nl) of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](https://cheere.org) that utilizes reinforcement discovering to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential distinguishing feature is its support knowing (RL) action, which was utilized to fine-tune the design's responses beyond the basic pre-training and [tweak process](http://39.99.134.1658123). By including RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, suggesting it's geared up to break down complex queries and factor through them in a detailed manner. This directed reasoning process permits the design to produce more precise, transparent, and detailed answers. This [model integrates](https://elmerbits.com) RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation model that can be integrated into different workflows such as representatives, rational reasoning and data interpretation jobs.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion specifications, enabling efficient inference by [routing questions](https://hugoooo.com) to the most appropriate expert "clusters." This method enables the model to specialize in different issue domains while maintaining total [performance](https://git.brass.host). DeepSeek-R1 requires at least 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 model. ml.p5e.48 xlarge comes with 8 Nvidia H200 [GPUs offering](https://miderde.de) 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more [effective models](https://addify.ae) to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher model.
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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 recommend deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and examine designs against crucial [security requirements](https://fototik.com). At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to various use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](http://playtube.ythomas.fr) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:DenishaHolyfield) under AWS Services, [pick Amazon](https://elmerbits.com) SageMaker, and verify you're utilizing ml.p5e.48 xlarge for [endpoint usage](https://voggisper.com). Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To request a limit boost, produce a [limitation increase](https://nodlik.com) demand and reach out to your account group.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For instructions, see Set up [consents](https://legatobooks.com) to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, avoid hazardous content, and evaluate designs against key security requirements. You can execute security measures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop 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 includes 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 model for reasoning. After receiving the design's output, another guardrail check is used. If the output passes this final check, it's returned as the result. 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 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 offers you access to over 100 popular, emerging, and specialized foundation models (FMs) through [Amazon Bedrock](http://www.radioavang.org). To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, choose Model brochure under [Foundation models](https://social.web2rise.com) in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:NigelMei567) DeepSeek as a company and select the DeepSeek-R1 design.
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The model detail page provides vital details about the model's capabilities, prices structure, and application standards. You can find detailed usage instructions, including sample API calls and code snippets for integration. The design supports different text generation tasks, consisting of material development, code generation, and question answering, utilizing its support discovering optimization and CoT reasoning capabilities. +The page also includes release alternatives and [licensing](https://fototik.com) [details](https://www.mpowerplacement.com) to help you start with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, select 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, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, get in a number of instances (in between 1-100). +6. For Instance type, pick your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function permissions, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you may wish to review these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the implementation is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play area to access an interactive user interface where you can try out various prompts and change model specifications like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For example, content for reasoning.
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This is an exceptional method to explore the model's thinking and text generation abilities before incorporating it into your [applications](http://www.hxgc-tech.com3000). The play area provides immediate feedback, helping you understand how the model responds to different inputs and letting you fine-tune your prompts for ideal results.
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You can quickly evaluate the design in the playground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference using a deployed DeepSeek-R1 model 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 actually created the guardrail, [pediascape.science](https://pediascape.science/wiki/User:StevieSimos301) utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, configures inference parameters, and sends a request to produce 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) center 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](https://holisticrecruiters.uk) designs to your use case, with your data, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 hassle-free approaches: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you pick the approach that finest suits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following [actions](https://mediawiki1263.00web.net) to release DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the SageMaker Studio console, [select JumpStart](https://grailinsurance.co.ke) in the navigation pane.
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The model internet browser shows available models, with details like the supplier name and design abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card shows crucial details, including:
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- Model name +- Provider name +- Task category (for instance, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11926756) Text Generation). +Bedrock Ready badge (if applicable), showing that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to [conjure](http://git.njrzwl.cn3000) up the model
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5. Choose the model card to see 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 [service provider](http://mangofarm.kr) details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of crucial details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage standards
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Before you release the model, it's recommended to review the design details and license terms to verify compatibility with your usage case.
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6. [Choose Deploy](http://1.117.194.11510080) to continue with implementation.
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7. For Endpoint name, [utilize](http://caxapok.space) the [automatically generated](https://git.obo.cash) name or create a custom one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of instances (default: 1). +Selecting proper instance types and counts is vital for [surgiteams.com](https://surgiteams.com/index.php/User:RileyRosenberg) expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is [selected](https://www.sintramovextrema.com.br) by default. This is enhanced for [sustained traffic](https://gogs.kakaranet.com) and low latency. +10. Review all [configurations](https://gitea.scalz.cloud) for precision. For this design, we highly advise adhering to SageMaker JumpStart default settings and [89u89.com](https://www.89u89.com/author/gildacavine/) making certain that network isolation remains in location. +11. Choose Deploy to deploy the design.
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The deployment process can take several minutes to complete.
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When deployment is total, your endpoint status will change to [InService](https://www.majalat2030.com). At this point, the design is prepared to accept inference demands through the endpoint. You can monitor the implementation progress on the [SageMaker](https://gitlabdemo.zhongliangong.com) console Endpoints page, which will display appropriate metrics and status details. When the deployment is complete, you can conjure up the design using a SageMaker runtime customer 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 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS approvals and [environment](https://vsbg.info) 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](https://gitlab-heg.sh1.hidora.com) in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra 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 also use the ApplyGuardrail API with your 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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Tidy up
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To prevent undesirable charges, finish the actions in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you released the design utilizing Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments. +2. In the Managed releases area, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing 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 design you deployed will [sustain expenses](https://lifestagescs.com) 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 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 going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker [JumpStart Foundation](https://musixx.smart-und-nett.de) Models, Amazon Bedrock Marketplace, and Getting begun 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](https://3flow.se) companies build ingenious services utilizing AWS services and sped up calculate. Currently, he is focused on developing strategies for fine-tuning and [enhancing](http://122.51.46.213) the inference efficiency of large language models. In his complimentary time, Vivek enjoys treking, enjoying motion pictures, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.klaverjob.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His [location](http://kandan.net) of focus is AWS [AI](http://35.207.205.18:3000) 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](http://212.64.10.162:7030) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.fightdynasty.com) center. She is passionate about constructing services that help clients accelerate their [AI](http://betim.rackons.com) journey and unlock business worth.
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