From afdce9f751054dd8242e7b33824ab455b54dd77a Mon Sep 17 00:00:00 2001 From: lolitajeffery Date: Sun, 16 Feb 2025 10:09:48 +0200 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..22161a2 --- /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 and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://takesavillage.club)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative [AI](https://www.tvcommercialad.com) concepts on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled versions of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](https://funnyutube.com) that utilizes reinforcement learning to improve thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential distinguishing function is its reinforcement learning (RL) step, which was used to improve the design's reactions beyond the standard pre-training and fine-tuning process. By [integrating](https://dalilak.live) RL, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:LesleyWatkin4) DeepSeek-R1 can adjust better to user feedback and objectives, eventually enhancing both relevance and clarity. In addition, DeepSeek-R1 employs a [chain-of-thought](http://git.aimslab.cn3000) (CoT) technique, [suggesting](http://media.clear2work.com.au) it's geared up to break down [complex queries](https://4stour.com) and reason through them in a detailed way. This directed reasoning process enables the design to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has recorded the industry's attention as a flexible text-generation design that can be incorporated into numerous workflows such as agents, logical reasoning and information interpretation jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion criteria, enabling effective inference by routing inquiries to the most appropriate specialist "clusters." This approach enables the design to specialize in various issue domains while maintaining total performance. DeepSeek-R1 needs 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](https://git.aaronmanning.net) 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design to more [effective architectures](http://111.230.115.1083000) based upon [popular](http://gitlab.together.social) 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 effective models to simulate the habits and thinking patterns of the larger DeepSeek-R1 model, using it as an instructor model.
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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 recommend releasing this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and examine models against essential security criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several [guardrails tailored](https://play.sarkiniyazdir.com) to various use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative [AI](http://zhandj.top:3000) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are [releasing](https://gitea.oio.cat). To ask for a limit boost, develop a limitation increase demand and reach out to your account team.
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Because you will be [deploying](https://kiwiboom.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. For instructions, see Set up approvals to [utilize guardrails](https://aladin.tube) 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 material, and assess designs against crucial safety criteria. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This [enables](http://www.topverse.world3000) you to use guardrails to assess 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 develop the guardrail, see the [GitHub repo](https://dispatchexpertscudo.org.uk).
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The basic 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 out to the design for reasoning. After receiving the model's output, another guardrail check is used. If the output passes this final check, it's [returned](http://chichichichichi.top9000) as the last result. However, if either the input or output is stepped in by the guardrail, a message is [returned indicating](https://gitea.ruwii.com) the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following areas show inference 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 structure 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, pick Model brochure under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 model.
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The model detail page supplies essential details about the design's abilities, rates structure, and [execution guidelines](https://surreycreepcatchers.ca). You can discover detailed usage directions, consisting of sample API calls and code bits for combination. The design supports numerous text generation jobs, consisting of content production, code generation, and concern answering, using its reinforcement finding out optimization and CoT thinking capabilities. +The page also includes implementation alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, choose Deploy.
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You will be triggered to set up the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an [endpoint](https://naijascreen.com) name (between 1-50 alphanumeric characters). +5. For Number of instances, go into a number of circumstances (between 1-100). +6. For example type, pick your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service function permissions, and file encryption settings. For most use cases, the default settings will work well. However, for production deployments, you may want to evaluate these settings to align with your organization's security and compliance requirements. +7. [Choose Deploy](https://git.easytelecoms.fr) to start utilizing the model.
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When the release is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in playground to access an interactive user interface where you can try out various triggers and change model parameters like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For instance, material for reasoning.
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This is an outstanding method to check out the model's thinking and text generation abilities before incorporating it into your applications. The play ground provides instant feedback, helping you comprehend how the design reacts to different inputs and letting you fine-tune your triggers for ideal results.
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You can rapidly check the design in the play ground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have actually developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures reasoning specifications, and sends out a demand 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) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, 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 implementing programmatically through the SageMaker Python SDK. Let's explore both to assist you select 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 steps to release DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the [navigation](http://gitlab.sybiji.com) pane. +2. First-time users will be prompted to develop 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 company name and design capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each [design card](http://worldjob.xsrv.jp) shows key details, including:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if applicable), indicating that this model can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to see the model details page.
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The model details page consists of the following details:
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- The model name and company details. +Deploy button to release the model. +About and Notebooks tabs with detailed details
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The About tab includes crucial details, such as:
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[- Model](http://103.197.204.1623025) description. +- License details. +- Technical specs. +- Usage standards
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Before you release the design, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2684771) 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 proceed with deployment.
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7. For Endpoint name, use the automatically generated name or develop a customized one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of circumstances (default: 1). +Selecting appropriate circumstances types and counts is vital for cost and efficiency optimization. Monitor your deployment 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 for precision. For this model, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the design.
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The [release procedure](https://git.jackyu.cn) can take a number of minutes to complete.
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When deployment is total, your [endpoint status](https://www.themart.co.kr) will alter to [InService](https://stationeers-wiki.com). At this point, the model is prepared to accept reasoning demands through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is total, you can conjure up the model utilizing a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To begin 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 and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for [deploying](http://elevarsi.it) 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 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 use the ApplyGuardrail API with your SageMaker [JumpStart predictor](https://gitea.gconex.com). You can develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:
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Tidy up
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To avoid undesirable charges, complete the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the model utilizing Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, [yewiki.org](https://www.yewiki.org/User:MaisieRoldan5) pick Marketplace releases. +2. In the [Managed releases](https://hyg.w-websoft.co.kr) area, locate the endpoint you wish to delete. +3. Select the endpoint, and on the [Actions](https://video.clicktruths.com) menu, [select Delete](https://jobs.quvah.com). +4. Verify the endpoint details to make certain you're erasing the correct deployment: 1. Endpoint name. +2. Model name. +3. Endpoint status
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Delete the [SageMaker JumpStart](https://nse.ai) predictor
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The SageMaker JumpStart model you released 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 checked out 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, describe Use Amazon Bedrock tooling with [Amazon SageMaker](https://git.juxiong.net) JumpStart designs, 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 is a Lead Specialist Solutions Architect for Inference at AWS. He [helps emerging](https://notewave.online) generative [AI](https://www.onlywam.tv) business build innovative services using AWS services and sped up compute. Currently, he is focused on developing methods for fine-tuning and optimizing the reasoning performance of large language models. In his downtime, [wiki.whenparked.com](https://wiki.whenparked.com/User:MarylynClick) Vivek enjoys hiking, viewing films, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://socialpix.club) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://www.hydrionlab.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://ashawo.club) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.saphir.one) center. She is passionate about developing solutions that help customers accelerate their [AI](https://git.laser.di.unimi.it) journey and unlock service worth.
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