From c02554787d3193f599b0f41c338c8c6e8b09cb16 Mon Sep 17 00:00:00 2001 From: barbmistry8768 Date: Sun, 9 Feb 2025 07:42:20 +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..9cb056b --- /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 designs are available through Amazon Bedrock Marketplace and [gratisafhalen.be](https://gratisafhalen.be/author/danarawson/) Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://source.coderefinery.org)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](http://106.14.65.137) ideas on AWS.
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In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release 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) developed by DeepSeek [AI](http://zhandj.top:3000) that uses reinforcement finding out to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A [crucial distinguishing](https://warleaks.net) function is its reinforcement knowing (RL) action, which was utilized to refine the model's actions beyond the standard pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and [wiki.myamens.com](http://wiki.myamens.com/index.php/User:AndreHeiden8) goals, eventually enhancing both significance and clearness. In addition, DeepSeek-R1 uses a [chain-of-thought](https://git.thewebally.com) (CoT) approach, suggesting it's equipped to break down complex inquiries and factor through them in a detailed manner. This directed reasoning procedure [permits](https://lensez.info) the model to produce more precise, transparent, and detailed responses. This design integrates RL-based [fine-tuning](https://source.coderefinery.org) with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation model that can be integrated into various workflows such as agents, sensible thinking and information analysis jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The [MoE architecture](https://www.tiger-teas.com) permits activation of 37 billion specifications, enabling efficient inference by routing inquiries to the most relevant professional "clusters." This method allows the design to specialize in various [issue domains](https://doum.cn) while maintaining overall effectiveness. 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 model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking [capabilities](https://nodlik.com) 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 effective models to imitate the behavior and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as an [instructor model](https://krazzykross.com).
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) we advise releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and assess designs against key 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](https://rightlane.beparian.com). You can [develop numerous](https://git.cyu.fr) 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://git.dev-store.xyz) [applications](http://62.178.96.1923000).
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're utilizing 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 request a limitation boost, produce a limitation increase demand and connect to your account team.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock [Guardrails](https://heovktgame.club). For [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) instructions, see Set up consents to utilize guardrails for content filtering.
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Implementing guardrails with the [ApplyGuardrail](https://git.kairoscope.net) API
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Amazon Bedrock Guardrails permits you to present safeguards, prevent damaging material, and evaluate designs against crucial safety criteria. You can execute security procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon [Bedrock console](https://www.e-vinil.ro) or the API. For the example code to develop the guardrail, see the GitHub repo.
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The basic flow 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 getting the design's output, another guardrail check is used. If the output passes this last check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is [returned indicating](https://78.47.96.1613000) the nature of the intervention and whether it happened at the input or output phase. 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 offers you access to over 100 popular, emerging, and specialized structure models (FMs) through [Amazon Bedrock](http://www.thynkjobs.com). To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane. +At the time of [writing](https://doum.cn) this post, you can utilize 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 company and pick the DeepSeek-R1 model.
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The design detail page provides vital details about the model's capabilities, pricing structure, and execution standards. You can discover detailed usage instructions, including sample API calls and code bits for integration. The model supports [numerous text](https://origintraffic.com) generation jobs, including content development, code generation, and question answering, utilizing its support finding out optimization and CoT thinking capabilities. +The page likewise consists of deployment choices and licensing [details](https://pittsburghtribune.org) to help you get begun with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
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You will be triggered 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 of instances (between 1-100). +6. For example type, select your instance type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service function approvals, and encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you might desire to examine 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 complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive user interface where you can explore different triggers and adjust design specifications like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal results. For instance, content for reasoning.
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This is an excellent method to explore the design's reasoning and text generation abilities before incorporating it into your applications. The playground supplies immediate feedback, [helping](http://43.136.17.1423000) you understand how the design reacts to various inputs and letting you fine-tune your prompts for ideal outcomes.
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You can rapidly evaluate the design in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to [execute guardrails](https://git.wisder.net). The script initializes the bedrock_runtime customer, configures inference criteria, and sends a demand to produce text based upon 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, integrated algorithms, and prebuilt ML services that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 convenient methods: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you choose the approach that best fits your requirements.
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Deploy DeepSeek-R1 through [SageMaker JumpStart](https://worship.com.ng) UI
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Complete the following actions to release 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 prompted to create a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model browser shows available designs, with details like the supplier name and design capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card shows crucial details, including:
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- Model name +- Provider name +- Task category (for example, [wavedream.wiki](https://wavedream.wiki/index.php/User:MargieMakin668) Text Generation). +Bedrock Ready badge (if relevant), [indicating](http://120.26.79.179) that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the [design card](http://13.228.87.95) to view the design details page.
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The model details page [consists](https://iamzoyah.com) of the following details:
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- The model name and supplier 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](https://saathiyo.com) specs. +- Usage standards
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Before you deploy the design, it's suggested to review the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, utilize the immediately produced name or create a custom-made one. +8. For Instance type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For [Initial](https://git.genowisdom.cn) instance count, get in the variety of circumstances (default: 1). +Selecting proper circumstances types and counts is crucial for expense and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=995691) Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we highly recommend [sticking](http://182.230.209.608418) to SageMaker JumpStart default settings and making certain that network seclusion remains in [location](https://git.molokoin.ru). +11. Choose Deploy to deploy the model.
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The implementation process can take numerous minutes to complete.
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When deployment is complete, your endpoint status will change to InService. At this point, the design is all set to accept reasoning requests through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will show relevant metrics and . When the release is complete, you can invoke the model using 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 using 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 utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is supplied in the Github here. You can clone the note pad 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the [Amazon Bedrock](https://167.172.148.934433) console or the API, and implement it as shown in the following code:
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Clean up
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To prevent unwanted charges, finish the steps in this area to clean up your resources.
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Delete the Amazon Bedrock [Marketplace](https://tribetok.com) release
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If you released the model 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, choose Marketplace releases. +2. In the Managed releases section, locate the endpoint you want to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the proper 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 design you deployed will sustain expenses if you leave it running. Use the following code to erase the endpoint if you wish to stop sustaining charges. For more details, see Delete Endpoints and [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:MichelleHarmer9) 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 or Amazon Bedrock [Marketplace](https://www.nenboy.com29283) 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 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](https://fleerty.com) business develop innovative options utilizing AWS services and sped up compute. Currently, he is focused on developing strategies for fine-tuning and optimizing the inference efficiency of large language models. In his spare time, Vivek delights in hiking, viewing movies, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.maisondurecrutementafrique.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://okk-shop.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://talktalky.com) with the Third-Party Model Science group 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://www.execafrica.com) hub. She is passionate about building options that help customers accelerate their [AI](http://59.57.4.66:3000) journey and unlock business value.
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