commit 7692f8660126d057aeb27077dcc3b92532c97e93 Author: darciblanks65 Date: Thu Apr 3 23:02:04 2025 +0000 Update '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..1048a9f --- /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 R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://career.webhelp.pk)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion specifications to build, experiment, and properly scale your generative [AI](http://git.attnserver.com) concepts on AWS.
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In this post, we show how to begin with DeepSeek-R1 on [Amazon Bedrock](http://gpis.kr) Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](http://git.wh-ips.com) that utilizes support learning to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential differentiating function is its support learning (RL) step, which was used to improve the model's actions beyond the standard pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually improving both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, suggesting it's equipped to break down complex queries and factor through them in a detailed way. This directed thinking procedure enables the design to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation model that can be incorporated into numerous workflows such as agents, rational reasoning and data interpretation jobs.
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DeepSeek-R1 uses a Mix of [Experts](http://fujino-mori.com) (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion specifications, making it possible for effective reasoning by routing questions to the most pertinent professional "clusters." This technique permits the design to concentrate on different problem domains while maintaining general effectiveness. 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 circumstances to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 [GPUs offering](https://schoolmein.com) 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 model to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective designs to simulate the behavior and [reasoning patterns](https://followmypic.com) of the bigger DeepSeek-R1 design, utilizing it as an instructor design.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and evaluate models against key safety requirements. At the time of [composing](https://wiki.project1999.com) this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](http://193.105.6.167:3000) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you require access to an ml.p5e instance. To [examine](https://source.lug.org.cn) if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm 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 deploying. To ask for a limit increase, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) produce a [limitation boost](https://ces-emprego.com) demand and reach out to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) [consents](https://mypungi.com) to use Amazon Bedrock Guardrails. For directions, see Establish authorizations to use 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 damaging content, and evaluate designs against key security criteria. You can carry out safety procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to examine user inputs and design actions released 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 basic circulation includes the following steps: 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 inference. After getting the model's output, another guardrail check is used. If the [output passes](https://gitlab.ujaen.es) this final check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following areas show inference 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](https://hektips.com). To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, choose Model catalog under Foundation models in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 model.
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The design detail page offers essential details about the design's abilities, prices structure, and execution guidelines. You can discover detailed usage guidelines, including sample API calls and code snippets for integration. The design supports numerous text generation jobs, consisting of content production, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT reasoning capabilities. +The page likewise consists of implementation alternatives and licensing details to help you get begun with DeepSeek-R1 in your applications. +3. To start 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, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Number of instances, enter a number of instances (between 1-100). +6. For example type, select your instance type. For optimum efficiency with DeepSeek-R1, a [GPU-based](https://career.logictive.solutions) circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can configure sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service function approvals, and encryption settings. For most utilize 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 to begin utilizing the model.
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When the release is total, 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 experiment with different triggers and adjust design specifications like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For example, content for inference.
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This is an [excellent method](http://1.119.152.2304026) to explore the design's reasoning and text generation capabilities before integrating it into your applications. The play ground supplies instant feedback, helping you understand how the design reacts to different inputs and letting you fine-tune your [triggers](https://www.h2hexchange.com) for optimal outcomes.
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You can rapidly evaluate the model in the playground 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 [deployed](https://git.vincents.cn) DeepSeek-R1 endpoint
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The following code example shows how to perform inference using a released DeepSeek-R1 design 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 actually produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning specifications, and sends a demand to generate 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, built-in algorithms, and prebuilt ML options that you can [release](http://worldwidefoodsupplyinc.com) with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production using either the UI or SDK.
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[Deploying](https://www.smfsimple.com) DeepSeek-R1 design through SageMaker JumpStart uses two hassle-free techniques: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you pick the [technique](https://musicplayer.hu) 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, choose Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the SageMaker Studio console, choose JumpStart in the [navigation](https://gitlab.ujaen.es) pane.
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The model internet browser displays available designs, with details like the supplier name and model capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card shows crucial details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this design can be signed up with Amazon Bedrock, enabling you to utilize Amazon [Bedrock](https://www.thempower.co.in) 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 design details page consists of the following details:
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- The design name and company details. +Deploy button to release the model. +About and Notebooks tabs with detailed details
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The About tab consists of essential details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage standards
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Before you deploy the model, it's advised to examine the design details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the instantly created name or develop a custom one. +8. For example [type ΒΈ](http://101.43.135.2349211) pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the variety of instances (default: 1). +Selecting appropriate instance types and counts is essential 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 optimized for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to release the model.
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The implementation procedure can take several minutes to finish.
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When release is complete, your endpoint status will alter to InService. At this point, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) the model is ready to accept reasoning [requests](http://mao2000.com3000) through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is complete, you can invoke the design utilizing 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 [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/3003269) make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the design is provided in the Github here. You can clone the note pad 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 reasoning 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 produce a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:
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Tidy up
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To prevent unwanted charges, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:KarlMungomery92) complete the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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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 models in the navigation pane, pick Marketplace releases. +2. In the Managed deployments area, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, [wavedream.wiki](https://wavedream.wiki/index.php/User:AdeleTreloar) choose Delete. +4. Verify the endpoint details to make certain you're deleting 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 model you released will sustain costs if you leave it [running](http://8.222.216.1843000). Use the following code to delete 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 get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, [Amazon Bedrock](https://www.oradebusiness.eu) Marketplace, and Getting started with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist [Solutions Architect](https://natgeophoto.com) for Inference at AWS. He assists emerging generative [AI](https://almagigster.com) business build innovative options utilizing AWS services and sped up [calculate](https://gogs.artapp.cn). Currently, he is concentrated on developing methods for fine-tuning and enhancing the inference performance of big language designs. In his downtime, Vivek takes pleasure in hiking, seeing motion pictures, and attempting various foods.
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[Niithiyn Vijeaswaran](https://www.tinguj.com) is a Generative [AI](http://code.snapstream.com) Specialist Solutions Architect with the [Third-Party Model](http://supervipshop.net) [Science](https://corerecruitingroup.com) team at AWS. His area of focus is AWS [AI](https://githost.geometrx.com) 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://www.nepaliworker.com) with the Third-Party Model Science team at AWS.
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[Banu Nagasundaram](https://gitea.createk.pe) leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://112.126.100.134:3000) hub. She is passionate about building solutions that assist consumers accelerate their [AI](https://ugit.app) journey and unlock company value.
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