From ba67ddc4439b2c125990cf62693a7409a025a702 Mon Sep 17 00:00:00 2001 From: Austin Favela Date: Sun, 9 Feb 2025 07:55:27 +0400 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..f6fc62c --- /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](https://clousound.com)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative [AI](http://47.92.159.28) ideas 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 too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by [DeepSeek](https://jobstaffs.com) [AI](https://cristianoronaldoclub.com) that utilizes reinforcement finding out to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial identifying function is its reinforcement knowing (RL) step, which was utilized to improve the design's actions beyond the standard pre-training and fine-tuning process. By [incorporating](https://paknoukri.com) RL, DeepSeek-R1 can adjust more successfully to user feedback and goals, eventually improving both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, meaning it's geared up to break down complicated queries and reason through them in a detailed manner. This assisted reasoning process 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 recorded the market's attention as a versatile text-generation model that can be incorporated into various workflows such as agents, sensible thinking and information analysis tasks.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, allowing efficient reasoning by routing queries to the most appropriate expert "clusters." This technique allows the model to concentrate on different problem domains 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 use an ml.p5e.48 [xlarge instance](https://www.trappmasters.com) to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of [GPU memory](https://apps365.jobs).
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DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design 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 efficient models to mimic the habits and [reasoning patterns](http://120.79.211.1733000) of the bigger DeepSeek-R1 design, using it as a teacher model.
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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 advise deploying this design with [guardrails](https://www.dataalafrica.com) in location. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and evaluate designs against essential security criteria. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:VeldaHinds) Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative [AI](http://sl860.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, [pick Amazon](https://rubius-qa-course.northeurope.cloudapp.azure.com) SageMaker, and verify you're using ml.p5e.48 xlarge for [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:ChetHeller473) endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limitation increase, produce a limit increase request 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 proper AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For [garagesale.es](https://www.garagesale.es/author/chandaleong/) guidelines, see Set up approvals to use guardrails for content filtering.
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[Implementing guardrails](http://hammer.x0.to) with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to introduce safeguards, prevent harmful content, and evaluate models against key security requirements. You can carry out precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and model reactions deployed on [Amazon Bedrock](http://bolsatrabajo.cusur.udg.mx) Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the [GitHub repo](https://freeworld.global).
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The general circulation involves the following actions: First, the system gets an input for the design. This input is then processed through the [ApplyGuardrail API](https://admin.gitea.eccic.net). If the input passes the guardrail check, it's sent to the model for inference. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the final 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 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 gives you access to over 100 popular, emerging, and specialized foundation models (FMs) through [Amazon Bedrock](http://xintechs.com3000). To [gain access](https://clousound.com) to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, select Model brochure under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [service provider](https://gitea.winet.space) and pick the DeepSeek-R1 model.
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The design detail page offers important details about the design's capabilities, prices structure, and implementation standards. You can discover detailed usage instructions, including sample API calls and code snippets for combination. The model supports numerous text generation jobs, including material creation, code generation, and question answering, using its reinforcement learning optimization and CoT thinking abilities. +The page likewise includes deployment choices and licensing details to help you start with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, choose Deploy.
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You will be triggered to configure the deployment details for DeepSeek-R1. The design ID will be [pre-populated](https://www.kenpoguy.com). +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, go into a number of [circumstances](https://charmyajob.com) (in between 1-100). +6. For example type, pick your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up [advanced security](https://jobstaffs.com) and infrastructure settings, including virtual personal cloud (VPC) networking, service role authorizations, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) and file encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you might want to evaluate 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 release is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive user interface where you can try out various prompts and change design specifications like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum results. For example, material for inference.
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This is an outstanding method to explore the model's reasoning and text generation capabilities before incorporating it into your applications. The play ground offers instant feedback, assisting you comprehend how the design reacts to various inputs and letting you fine-tune your prompts for optimal results.
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You can quickly check the model in the play ground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you require to get the [endpoint ARN](http://git.airtlab.com3000).
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Run inference using guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference using a released DeepSeek-R1 model through Amazon Bedrock utilizing 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 created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, configures inference specifications, and sends out a request to generate 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](https://dreamtvhd.com) (ML) center with FMs, built-in algorithms, and prebuilt ML solutions that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 hassle-free approaches: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you select the technique that finest matches 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 utilizing SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The browser shows available models, with details like the company name and design abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card reveals crucial details, consisting of:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if appropriate), indicating that this design can be signed up 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 design details page.
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The design [details](http://120.48.7.2503000) page [consists](https://gomyneed.com) of the following details:
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- The design name and supplier details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
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The About tab consists of important details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage guidelines
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Before you deploy the design, it's recommended to examine the model details and license terms to verify compatibility with your use case.
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6. Choose Deploy to proceed with implementation.
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7. For Endpoint name, use the immediately created name or produce a custom-made one. +8. For Instance type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the number of circumstances (default: 1). +Selecting proper circumstances types and counts is important for expense and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, [Real-time inference](http://easyoverseasnp.com) is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for accuracy. For this model, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that [network seclusion](https://braindex.sportivoo.co.uk) remains in location. +11. Choose Deploy to release the design.
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The implementation process can take numerous minutes to complete.
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When implementation is complete, your endpoint status will change to InService. At this moment, the model is prepared to accept inference requests through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is complete, you can invoke the model utilizing a [SageMaker](http://8.140.205.1543000) runtime client and integrate it with your applications.
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Deploy DeepSeek-R1 using the [SageMaker Python](https://gitlab.buaanlsde.cn) SDK
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To get going with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS approvals and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing 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 additional 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a [guardrail utilizing](https://bbs.yhmoli.com) 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 avoid unwanted charges, finish the steps in this area to clean up your resources.
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Delete the Amazon Bedrock [Marketplace](http://24insite.com) deployment
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If you deployed the design utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations. +2. In the Managed implementations area, locate the endpoint you desire to delete. +3. Select the endpoint, and on the [Actions](https://jobportal.kernel.sa) menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the appropriate implementation: 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 released will sustain expenses 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](https://adremcareers.com).
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Conclusion
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In this post, we explored how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and [SageMaker JumpStart](http://123.206.9.273000). Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart [Foundation](https://git2.nas.zggsong.cn5001) Models, Amazon Bedrock Marketplace, and Beginning with [Amazon SageMaker](https://git.dev.hoho.org) 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://gitlab.thesunflowerlab.com) companies build ingenious options utilizing AWS services and accelerated compute. Currently, he is focused on developing methods for fine-tuning and enhancing the inference efficiency of large language models. In his complimentary time, Vivek enjoys hiking, viewing films, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://dcmt.co.kr) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://git.protokolla.fi) [accelerators](https://kandidatez.com) (AWS Neuron). He holds a Bachelor's degree in Computer Science and [Bioinformatics](https://kod.pardus.org.tr).
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://xhandler.com) with the Third-Party Model [Science](https://aladin.tube) group at AWS.
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[Banu Nagasundaram](https://www.florevit.com) leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://117.72.39.125:3000) hub. She is passionate about developing solutions that assist consumers accelerate their [AI](https://www.freeadzforum.com) journey and unlock business value.
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