From 1a531a47f4f99df3ef09f1819984a135726c23f0 Mon Sep 17 00:00:00 2001 From: darnellportus9 Date: Thu, 3 Apr 2025 19:26:22 +0000 Subject: [PATCH] Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-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..0f446d2 --- /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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://www.grainfather.de)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative [AI](https://gitea.namsoo-dev.com) concepts on AWS.
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In this post, we demonstrate 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 also.
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[Overview](https://younivix.com) of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://melanatedpeople.net) that utilizes reinforcement learning to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential identifying feature is its support learning (RL) step, which was utilized to fine-tune the design's reactions beyond the [standard pre-training](https://git.tanxhub.com) and tweak procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually improving both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, meaning it's equipped to break down [complicated queries](http://git.jzcure.com3000) and factor through them in a detailed way. This assisted reasoning process permits the design to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while focusing on [interpretability](https://myjobasia.com) and user interaction. With its extensive abilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation model that can be incorporated into numerous workflows such as agents, logical reasoning and information analysis jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion specifications, allowing effective reasoning by routing inquiries to the most appropriate professional "clusters." This approach allows the design to specialize in various problem domains while maintaining overall effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 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 upon popular open models like Qwen (1.5 B, 7B, 14B, and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective models to imitate the habits and thinking patterns of the larger DeepSeek-R1 design, using it as a teacher design.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and [examine designs](https://dev.gajim.org) against essential security criteria. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the [ApplyGuardrail API](https://git.mxr612.top). You can create multiple guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](https://hot-chip.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the [Service Quotas](https://tikplenty.com) [console](http://globalnursingcareers.com) and under AWS Services, pick 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 limitation boost, create a [limit increase](https://sun-clinic.co.il) demand and connect to your account group.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For instructions, see Set up permissions 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 harmful material, and assess models against essential security requirements. You can execute precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This [permits](https://cacklehub.com) you to use guardrails to examine user inputs and design responses deployed on [Amazon Bedrock](https://gitea.gumirov.xyz) Marketplace and [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:MichaelWayn) SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to [develop](https://git.perbanas.id) the guardrail, see the GitHub repo.
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The general circulation includes the following actions: First, the system receives 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 design for reasoning. After receiving the design's output, another guardrail check is used. If the output passes 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 showing 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 foundation models (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](https://remnantstreet.com) catalog under Foundation designs in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to [conjure](https://myteacherspool.com) up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.
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The model detail page offers essential details about the model's abilities, prices structure, and implementation standards. You can discover detailed usage instructions, consisting of sample API calls and code snippets for combination. The model supports various text generation tasks, including content production, code generation, and question answering, using its support discovering optimization and CoT thinking abilities. +The page likewise includes implementation options and licensing details to help 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 configure the [deployment details](https://www.eadvisor.it) for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, enter a variety of circumstances (in between 1-100). +6. For example type, pick your instance type. For optimum efficiency with DeepSeek-R1, a [GPU-based instance](https://wiki.cemu.info) type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service function approvals, and encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you might wish to evaluate these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the deployment is total, you can check 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 various triggers and adjust model parameters 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 reasoning.
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This is an exceptional way to check out the design's thinking and text generation capabilities before incorporating it into your applications. The playground offers instant feedback, assisting you comprehend how the design responds to various inputs and letting you [fine-tune](https://www.shwemusic.com) your prompts for ideal outcomes.
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You can rapidly check the model in the play ground through the UI. However, to invoke the [deployed model](https://jobsspecialists.com) programmatically with any Amazon Bedrock APIs, you need 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 carry out reasoning using a deployed DeepSeek-R1 model through Amazon Bedrock the invoke_model and [raovatonline.org](https://raovatonline.org/author/thaomckillo/) ApplyGuardrail API. You can develop 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 developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures reasoning specifications, and sends out a [request](https://git.uzavr.ru) to [generate text](http://47.108.239.2023001) 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, built-in algorithms, and prebuilt ML services that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 practical techniques: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you choose the approach that best fits 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 pane. +2. First-time users will be triggered to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model web browser displays available models, with details like the service provider name and design abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card reveals crucial details, consisting of:
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[- Model](http://150.158.183.7410080) name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if relevant), indicating that this design can be signed up 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 [design details](https://repo.globalserviceindonesia.co.id) page.
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The design details page consists of the following details:
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- The model name and service provider 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 specs. +- Usage standards
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Before you deploy the design, it's advised to evaluate the model details and license terms to validate 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 generated name or develop a custom-made one. +8. For example type ΒΈ choose an [instance type](https://103.1.12.176) (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the variety of circumstances (default: 1). +Selecting suitable [instance types](https://dztrader.com) and counts is important for cost and performance optimization. Monitor your release to change 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 configurations for accuracy. For this model, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to release the design.
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The deployment procedure can take several minutes to finish.
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When [deployment](https://ready4hr.com) is complete, your endpoint status will change to InService. At this point, the design is prepared to accept reasoning demands through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is complete, you can invoke the model using a [SageMaker runtime](https://git.cno.org.co) client and incorporate it with your applications.
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Deploy DeepSeek-R1 using 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 required AWS consents and environment setup. The following is a [detailed](https://www.wcosmetic.co.kr5012) code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the model is provided in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run additional requests 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
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Clean up
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To prevent unwanted charges, complete the steps in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you released the design utilizing Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace implementations. +2. In the Managed deployments section, locate the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're erasing the right 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 expenses if you leave it running. Use the following code to erase 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 checked out 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 designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a [Lead Specialist](http://www.xyais.com) Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://sudanre.com) companies build innovative services utilizing AWS services and accelerated compute. Currently, he is concentrated on establishing strategies for fine-tuning and enhancing the inference efficiency of large language designs. In his spare time, Vivek takes pleasure in treking, viewing movies, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://git.penwing.org) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://www.florevit.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://leicestercityfansclub.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon [SageMaker](http://101.200.127.153000) JumpStart, SageMaker's artificial intelligence and [generative](https://www.racingfans.com.au) [AI](https://www.telewolves.com) center. She is enthusiastic about building solutions that help customers accelerate their [AI](https://solegeekz.com) journey and unlock service worth.
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