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Today, we are thrilled 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's first-generation frontier model, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative AI concepts on AWS.

In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled variations of the designs too.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that utilizes support discovering to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial differentiating function is its reinforcement knowing (RL) action, which was used to refine the design's actions beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately improving both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, suggesting it's geared up to break down complex queries and reason through them in a detailed way. This guided reasoning procedure allows the design to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation design that can be integrated into different workflows such as agents, sensible reasoning and information interpretation tasks.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion parameters, enabling effective reasoning by routing questions to the most relevant specialist "clusters." This technique enables the design to focus on various problem domains while maintaining general effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning capabilities 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 process of training smaller, more efficient designs to simulate the habits and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher design.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise deploying this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent harmful content, and evaluate models against key safety criteria. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To deploy 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. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To request a limitation increase, produce a limitation boost demand and connect to your account group.

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) permissions to use Amazon Bedrock Guardrails. For directions, see Set up consents to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to present safeguards, prevent hazardous content, and evaluate models against key security criteria. You can implement safety measures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to examine user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.

The basic flow involves 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 to the model for reasoning. After getting the model's output, another guardrail check is used. If the output passes this final 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 occurred at the input or output phase. The examples showcased in the following areas demonstrate reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace offers 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 actions:

1. On the Amazon Bedrock console, choose Model catalog under Foundation designs in the navigation pane. At the time of writing this post, you can use the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 design.

The model detail page offers vital details about the design's capabilities, rates structure, and application guidelines. You can discover detailed use directions, consisting of sample API calls and code snippets for combination. The model supports numerous text generation jobs, consisting of material production, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT reasoning capabilities. The page likewise consists of implementation options and licensing details to help you get going with DeepSeek-R1 in your applications. 3. To start using DeepSeek-R1, select Deploy.

You will be prompted to configure the release details for DeepSeek-R1. The design ID will be pre-populated. 4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). 5. For Variety of circumstances, get in a variety of instances (between 1-100). 6. For Instance type, choose your instance type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can configure sophisticated security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role approvals, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production releases, you may want to examine these settings to align with your company's security and compliance requirements. 7. Choose Deploy to start utilizing the design.

When the implementation is total, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. 8. Choose Open in playground to access an interactive interface where you can experiment with different triggers and adjust design parameters like temperature level and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For instance, material for inference.

This is an outstanding way to check out the model's thinking and text generation capabilities before incorporating it into your applications. The play area supplies instant feedback, helping you comprehend how the model reacts to different inputs and letting you fine-tune your prompts for optimum outcomes.

You can rapidly test the design in the play ground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run inference utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example demonstrates how to carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, [forum.batman.gainedge.org](https://forum.batman.gainedge.org/index.php?action=profile