From e5752dcda09af7b170892189e2f9b584eab09fb0 Mon Sep 17 00:00:00 2001 From: justine295941 Date: Sun, 9 Feb 2025 07:56:34 +0300 Subject: [PATCH] Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart --- ...tplace And Amazon SageMaker JumpStart.-.md | 93 +++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md 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..0720a6f --- /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 announce 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](https://git.valami.giize.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](http://109.195.52.92:3000) concepts 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 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) [developed](https://www.menacopt.com) by DeepSeek [AI](https://privamaxsecurity.co.ke) that utilizes support learning to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key identifying [feature](https://sso-ingos.ru) is its support knowing (RL) action, which was used to refine the model's actions beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and goals, ultimately enhancing both relevance and [clearness](http://122.51.230.863000). In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, [implying](https://jobsingulf.com) it's [equipped](http://175.24.176.23000) to break down intricate inquiries and factor through them in a detailed way. This assisted thinking procedure enables the design to produce more accurate, transparent, and detailed responses. This design integrates [RL-based](https://dev.ncot.uk) fine-tuning with CoT abilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually captured the industry's attention as a flexible text-generation design that can be integrated into various workflows such as representatives, logical reasoning and information analysis tasks.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) [architecture](http://175.6.124.2503100) and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion specifications, [allowing efficient](https://ibs3457.com) inference by routing inquiries to the most appropriate expert "clusters." This method permits the design to concentrate on different [issue domains](https://tjoobloom.com) while maintaining overall 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 includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 design to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective designs to simulate the habits and thinking patterns of the larger DeepSeek-R1 model, utilizing it as a teacher design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent damaging content, and evaluate designs against essential security requirements. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, improving user experiences and [standardizing security](https://plane3t.soka.ac.jp) controls throughout your generative [AI](https://dreamtube.congero.club) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate you're using 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 releasing. To ask for a limit increase, produce a limitation boost request and connect to your account team.
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Because you will be [releasing](https://www.istorya.net) this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For guidelines, see Establish approvals to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, prevent damaging content, and examine designs against key security criteria. You can execute security procedures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and model reactions 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 produce the guardrail, see the GitHub repo.
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The general circulation involves the following steps: First, the system gets 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 model for inference. After getting the model'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 stepped in by the guardrail, a message is [returned suggesting](https://jobs.sudburychamber.ca) the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following areas show reasoning using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock [Marketplace](http://101.42.248.1083000) offers you access to over 100 popular, emerging, and specialized structure 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 designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for [DeepSeek](http://101.43.112.1073000) as a supplier and select the DeepSeek-R1 design.
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The design detail page supplies important details about the design's abilities, prices structure, and implementation guidelines. You can discover detailed use instructions, consisting of sample API calls and code bits for integration. The model supports different text generation tasks, consisting of content production, code generation, and concern answering, utilizing its reinforcement finding out [optimization](http://orcz.com) and CoT reasoning capabilities. +The page also consists of [implementation choices](https://git.micahmoore.io) and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, select Deploy.
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You will be prompted to configure the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an [endpoint](https://www.scikey.ai) name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, get in a number of circumstances (between 1-100). +6. For Instance type, choose your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up advanced security and facilities settings, consisting of virtual private cloud (VPC) networking, service role consents, and encryption settings. For most use cases, the default settings will work well. However, for production implementations, you may wish to review these settings to line up with your organization's security and compliance requirements. +7. [Choose Deploy](https://stnav.com) to start using the design.
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When the implementation is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive interface where you can try out various triggers and adjust design specifications like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal results. For instance, content for inference.
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This is an outstanding way to check out the model's thinking and text generation capabilities before incorporating it into your applications. The play ground provides instant feedback, assisting you understand how the model responds to various inputs and letting you tweak your triggers for ideal outcomes.
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You can quickly [evaluate](http://www.grainfather.global) the model in the [play ground](http://git.jaxc.cn) through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning using guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning parameters, and sends a request 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) hub with FMs, built-in algorithms, and prebuilt ML services that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production using either the UI or [larsaluarna.se](http://www.larsaluarna.se/index.php/User:FinnDarbonne4) SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 convenient approaches: using the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both approaches to help you choose the approach that best suits 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 using 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, pick JumpStart in the navigation pane.
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The model browser shows available designs, with details like the provider name and design capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card reveals [crucial](https://oerdigamers.info) details, consisting of:
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- Model name +[- Provider](https://cvmira.com) name +- Task classification (for example, Text Generation). +[Bedrock Ready](https://studiostilesandtotalfitness.com) badge (if appropriate), suggesting that this design can be signed up with Amazon Bedrock, allowing you to use [Amazon Bedrock](http://makerjia.cn3000) APIs to invoke the design
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5. Choose the model card to view the model details page.
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The design details page consists of the following details:
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- The design name and company details. +Deploy button to deploy the design. +About and [Notebooks tabs](http://163.66.95.1883001) with detailed details
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The About tab consists of [crucial](http://httelecom.com.cn3000) details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage standards
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Before you release the design, it's advised to examine the design details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, use the automatically produced name or develop a custom-made one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the variety of circumstances (default: 1). +Selecting appropriate instance types and counts is crucial for cost and efficiency optimization. Monitor your release to adjust 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 [configurations](https://media.motorsync.co.uk) for [precision](http://182.92.196.181). For this model, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the design.
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The deployment process can take a number of minutes to complete.
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When deployment is total, your endpoint status will change to InService. At this moment, the model is prepared to accept inference requests through the [endpoint](https://git.panggame.com). You can monitor the release development on the SageMaker console Endpoints page, which will display [pertinent metrics](https://www.yohaig.ng) and status details. When the implementation is total, you can conjure up 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 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS [consents](https://git.pandaminer.com) and environment setup. The following is a detailed code example that [demonstrates](https://avicii.blog) how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run extra demands 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 likewise use 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](https://www.lshserver.com3000) in the following code:
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Clean up
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To prevent unwanted charges, finish the steps in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you released the model using Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under [Foundation](https://gitea.namsoo-dev.com) models in the navigation pane, select Marketplace implementations. +2. In the Managed releases section, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, [pick Delete](https://sapjobsindia.com). +4. Verify the endpoint details to make certain you're deleting the appropriate deployment: 1. Endpoint name. +2. Model name. +3. [Endpoint](https://git.alenygam.com) status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart design you deployed will sustain expenses 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 utilizing 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 models, SageMaker JumpStart pretrained designs, 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](https://wikibase.imfd.cl) is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](http://xiaomaapp.top:3000) companies construct ingenious options utilizing AWS services and accelerated calculate. Currently, he is concentrated on establishing strategies for fine-tuning and optimizing the inference performance of big language models. In his downtime, Vivek delights in hiking, watching movies, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.ndule.site) [Specialist Solutions](http://git.idiosys.co.uk) Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://a21347410b.iask.in:8500) accelerators (AWS Neuron). He holds a [Bachelor's degree](https://spaceballs-nrw.de) in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://www.ch-valence-pro.fr) with the Third-Party Model Science group 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](http://49.50.103.174) hub. She is passionate about that assist consumers accelerate their [AI](https://git.torrents-csv.com) journey and unlock organization value.
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