commit c0777f6ac38df0283ff93380b5d7eff23486781f Author: floy09j426181 Date: Sun Apr 13 08:06:58 2025 +0200 Add '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..0232325 --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,37 @@ +
Today, we are excited 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://social.netverseventures.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion specifications to develop, experiment, and [responsibly scale](https://newvideos.com) your generative [AI](https://24frameshub.com) ideas on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled versions of the designs as well.
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[Overview](https://inspirationlift.com) of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](https://library.kemu.ac.ke) that uses support discovering to boost thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial identifying function is its support learning (RL) step, which was utilized to refine the design's actions beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately enhancing both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, suggesting it's equipped to break down complex questions and factor through them in a detailed manner. This directed reasoning 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 recorded the market's attention as a versatile text-generation design that can be incorporated into different workflows such as representatives, sensible thinking 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 parameters, allowing efficient inference by routing inquiries to the most appropriate professional "clusters." This method allows the model to specialize in various problem domains while maintaining total effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking capabilities 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 sized, more efficient models to [simulate](https://infinirealm.com) the behavior and reasoning patterns of the bigger DeepSeek-R1 design, 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 advise deploying this model with [guardrails](https://church.ibible.hk) in place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent harmful content, and examine models against essential security criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](http://8.142.36.79:3000) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick 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 releasing. To request a limit increase, develop a limit increase demand and connect to your account group.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For guidelines, see Set up permissions to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, avoid damaging content, and assess models against key safety requirements. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
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The [basic flow](https://cyberdefenseprofessionals.com) includes the following actions: First, the system receives 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 applied. If the output passes this final check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the [intervention](https://blogville.in.net) and whether it occurred at the input or output stage. The examples showcased in the following areas [demonstrate inference](https://posthaos.ru) 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. 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 catalog under Foundation designs in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to conjure up the model. It does not 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 supplies vital details about the model's abilities, rates structure, and [implementation guidelines](http://gbtk.com). You can discover detailed usage instructions, consisting of sample API calls and code snippets for integration. The design supports different text generation jobs, consisting of material development, code generation, and concern answering, using its support finding out optimization and CoT reasoning capabilities. +The page likewise includes implementation alternatives and licensing [details](https://social.vetmil.com.br) 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 triggered to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For [Endpoint](http://swwwwiki.coresv.net) name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Number of instances, get in a number of instances (between 1-100). +6. For Instance type, choose your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance 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 role approvals, and file encryption settings. For the [majority](https://gitlab.ui.ac.id) of use cases, the default settings will work well. However, for production releases, 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 implementation is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive user interface where you can try out different triggers and change model criteria like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For example, material for inference.
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This is an outstanding method to explore the design's reasoning and text generation abilities before incorporating it into your applications. The play area provides instant feedback, assisting you comprehend how the design responds to various inputs and letting you tweak your [prompts](https://aubameyangclub.com) for optimum outcomes.
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You can [rapidly check](https://hyptechie.com) the model in the [play ground](https://drshirvany.ir) through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the [endpoint ARN](http://8.136.197.2303000).
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Run inference using guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up inference criteria, and sends a demand to produce 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](http://66.112.209.23000) (ML) center with FMs, built-in algorithms, and prebuilt ML services that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 convenient techniques: [utilizing](https://chat-oo.com) the user-friendly SageMaker JumpStart UI or [forum.batman.gainedge.org](https://forum.batman.gainedge.org/index.php?action=profile \ No newline at end of file