From 02eebeb6182b54526cfeb6d4c3062604491dacd2 Mon Sep 17 00:00:00 2001 From: dellgilroy263 Date: Fri, 7 Feb 2025 11:50:14 +0800 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..89da7af --- /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 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://enitajobs.com)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](https://www.jobcheckinn.com) ideas on AWS.
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In this post, [disgaeawiki.info](https://disgaeawiki.info/index.php/User:KelleG0472) we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled versions of the designs also.
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[Overview](https://git.nullstate.net) of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](https://www.ausfocus.net) that uses reinforcement learning to improve thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key distinguishing function is its reinforcement learning (RL) step, which was used to fine-tune the model's responses beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually boosting both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, suggesting it's geared up to break down intricate questions and factor through them in a detailed manner. This guided reasoning procedure allows the design to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually recorded the industry's attention as a flexible text-generation model that can be integrated into numerous workflows such as agents, rational thinking and data interpretation jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion criteria, making it possible for efficient reasoning by routing questions to the most relevant professional "clusters." This method allows the model to focus on different issue domains while maintaining general performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for [reasoning](https://www.cartoonistnetwork.com). In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model to more efficient architectures based on popular 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 designs to [imitate](https://gamberonmusic.com) the habits and thinking patterns of the larger DeepSeek-R1 design, using it as an instructor design.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, [prevent hazardous](http://tanpoposc.com) material, and assess designs against crucial security requirements. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative [AI](https://talentsplendor.com) applications.
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Prerequisites
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To [release](https://git.rt-academy.ru) the DeepSeek-R1 model, you require 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 SageMaker, and validate you're utilizing ml.p5e.48 xlarge for [endpoint usage](https://git.schdbr.de). Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limit increase, [develop](https://complete-jobs.co.uk) a limit boost demand and connect to your account team.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For directions, see Establish permissions to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, avoid harmful material, and examine models against crucial safety criteria. You can carry out security measures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to [examine](http://94.224.160.697990) user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a [guardrail](http://101.231.37.1708087) using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the [GitHub repo](https://gitlab-dev.yzone01.com).
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The general flow involves the following actions: 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 model for reasoning. After getting the design's output, another guardrail check is used. If the output passes this last check, it's returned as the final outcome. 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 happened at the input or output stage. The examples showcased in the following sections demonstrate reasoning using 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 foundation designs (FMs) through Amazon Bedrock. 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 catalog under Foundation models in the navigation pane. +At the time of writing this post, you can use 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 supplier and choose the DeepSeek-R1 model.
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The design detail page supplies important details about the model's capabilities, pricing structure, and implementation guidelines. You can discover detailed use directions, including sample API calls and code bits for integration. The model supports numerous text generation tasks, including material production, code generation, and question answering, utilizing its reinforcement learning optimization and CoT thinking capabilities. +The page also includes release alternatives and licensing details to help you start with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be prompted to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, enter a [variety](https://avajustinmedianetwork.com) of circumstances (in between 1-100). +6. For Instance type, choose your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service function permissions, and encryption settings. For [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=995691) the majority of 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 begin using the design.
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When the release is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive interface where you can try out various prompts and change design criteria like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For example, content for reasoning.
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This is an excellent way to check out the design's thinking and text generation abilities before [integrating](http://182.92.196.181) it into your applications. The play ground supplies immediate feedback, helping you understand how the design responds to numerous inputs and letting you tweak your triggers for ideal outcomes.
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You can rapidly test 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 reasoning utilizing guardrails with the [deployed](http://bryggeriklubben.se) 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 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 [developed](http://118.195.204.2528080) the guardrail, use the following code to execute guardrails. The script initializes the bedrock_[runtime](https://work-ofie.com) client, sets up [reasoning](http://n-f-l.jp) specifications, and sends out a request to generate text based on 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, integrated algorithms, and prebuilt ML solutions that you can deploy 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 utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 hassle-free approaches: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you choose the method that finest fits your needs.
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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, pick Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model web browser displays available models, with details like the supplier name and model abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card reveals key details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if appropriate), showing that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the model
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5. Choose the model card to view the design details page.
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The model details page includes the following details:
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- The model name and company details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab includes [crucial](https://cats.wiki) 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 model details and license terms to confirm 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 instantly created name or create a customized one. +8. For example type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the number of circumstances (default: 1). +Selecting proper circumstances types and counts is [essential](https://gitee.mmote.ru) for cost and performance optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low [latency](https://sublimejobs.co.za). +10. Review all configurations for precision. For this model, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +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 release is complete, your [endpoint status](https://gitlab.cranecloud.io) will alter to [InService](http://94.224.160.697990). At this point, the design is prepared to accept reasoning [demands](http://yhxcloud.com12213) through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and [status details](https://video.etowns.ir). When the release is complete, you can conjure up the design utilizing a SageMaker runtime client and incorporate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS authorizations 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 releasing the design is [offered](http://gitlab.qu-in.com) 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 reasoning 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. You can develop a using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Clean up
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To prevent unwanted charges, finish the actions in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you deployed the design using 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 implementations section, locate the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, [pick Delete](https://nuswar.com). +4. Verify the endpoint details to make certain you're deleting the appropriate release: 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 model you released will sustain costs 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 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](https://git.rootfinlay.co.uk) or Amazon Bedrock Marketplace now to get begun. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, [SageMaker JumpStart](https://121.36.226.23) pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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](http://git.airtlab.com:3000) companies build innovative services utilizing AWS services and sped up compute. Currently, he is focused on establishing techniques for fine-tuning and optimizing the inference efficiency of big language models. In his spare time, Vivek delights in treking, seeing motion pictures, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://bvbborussiadortmundfansclub.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://truejob.co) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://git.brass.host) with the [Third-Party Model](https://groups.chat) [Science team](https://git.buzhishi.com14433) at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=995449) Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://test.manishrijal.com.np) hub. She is passionate about constructing options that help customers accelerate their [AI](http://kanghexin.work:3000) journey and unlock organization value.
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