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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 release DeepSeek AI's first-generation frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we show how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that uses support learning to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial identifying feature is its support knowing (RL) action, which was utilized to improve the model's reactions beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, eventually enhancing both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, indicating it's geared up to break down intricate queries and reason through them in a detailed manner. This directed thinking procedure allows the model to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to create structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation model that can be incorporated into different workflows such as agents, logical thinking and data analysis tasks.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion specifications, allowing efficient reasoning by routing inquiries to the most relevant expert "clusters." This technique permits the design to concentrate on various problem domains while maintaining general efficiency. DeepSeek-R1 requires at least 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 abilities of the main R1 model to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective designs to imitate the behavior and reasoning patterns of the larger DeepSeek-R1 model, using it as an instructor design.

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this model with guardrails in place. In this blog site, we will utilize Amazon Guardrails to present safeguards, avoid damaging material, and assess models against essential safety requirements. 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 several guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint usage. 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, produce a limitation increase demand and connect to your account team.

Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For guidelines, see Set up authorizations to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, prevent harmful content, and assess designs against essential safety requirements. You can implement safety measures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and model reactions 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 create the guardrail, see the GitHub repo.

The general circulation involves the following actions: 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 to the design for inference. After getting the model'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 stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections show inference utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:

1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane. At the time of writing 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 supplier and pick the DeepSeek-R1 design.

The design detail page provides important details about the model's abilities, rates structure, and implementation guidelines. You can find detailed usage directions, including sample API calls and code bits for combination. The model supports different text generation jobs, including material development, code generation, and concern answering, utilizing its reinforcement learning optimization and CoT reasoning abilities. The page likewise consists of release options and licensing details to assist you begin with DeepSeek-R1 in your applications. 3. To begin utilizing DeepSeek-R1, choose Deploy.

You will be prompted to configure the implementation 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 variety of instances (between 1-100). 6. For example type, pick your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can set up sophisticated security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you may wish to review these settings to align with your company's security and compliance requirements. 7. Choose Deploy to start utilizing the model.

When the deployment is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. 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 level and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For instance, material for reasoning.

This is an outstanding way to explore the model's reasoning and text generation abilities before integrating it into your applications. The play ground provides instant feedback, helping you understand how the design reacts to numerous inputs and letting you tweak your prompts for optimal results.

You can quickly check the model in the play ground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.

Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference criteria, and sends out a demand to produce text based on a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can release with just a few 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.

Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 practical approaches: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you select the approach that best suits your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to deploy DeepSeek-R1 using SageMaker JumpStart:

1. On the SageMaker console, select Studio in the navigation pane. 2. First-time users will be prompted to develop a domain. 3. On the SageMaker Studio console, select JumpStart in the navigation pane.

The model browser displays available designs, with details like the supplier name and model capabilities.

4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. Each model card shows crucial details, including:

- Model name

• Provider name
• Task classification (for instance, gratisafhalen.be Text Generation). Bedrock Ready badge (if relevant), showing that this model can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model
  
  5. Choose the design card to view the design details page.
  
  The model details page consists of the following details:
  
  - The model name and provider details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
  
  The About tab consists of essential details, such as:
  
  - Model description.
• License details.
• Technical requirements.
• Usage guidelines
  
  Before you deploy the design, it's recommended to evaluate the model details and license terms to validate compatibility with your use case.
  
  6. Choose Deploy to continue with release.
  
  7. For Endpoint name, use the immediately created name or create a custom one.

8. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the number of circumstances (default: 1). Selecting suitable instance types and counts is important for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency.
10. Review all configurations for precision. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to release the model.
    
    The deployment procedure can take numerous minutes to finish.
    
    When implementation is total, your endpoint status will alter to InService. At this point, the model is all set to accept inference requests through the endpoint. You can keep track of the release progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is total, you can conjure up the model utilizing a SageMaker runtime customer and incorporate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is provided in the Github here. You can clone the note pad and range from SageMaker Studio.
    
    You can run extra demands against the predictor:
    
    Implement guardrails and run inference with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
    
    Clean up
    
    To prevent unwanted charges, finish the actions in this area to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you released the design using Amazon Bedrock Marketplace, complete the following actions:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace implementations.
12. In the Managed implementations area, find the endpoint you want to erase.
13. Select the endpoint, and on the Actions menu, pick Delete.
14. Verify the endpoint details to make certain you're deleting the correct implementation: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart model you released will sustain costs 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 Resources.
    
    Conclusion
    
    In this post, we explored how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. 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 Starting with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative AI business build innovative solutions utilizing AWS services and sped up calculate. Currently, he is focused on establishing techniques for fine-tuning and optimizing the inference performance of big language models. In his free time, Vivek enjoys treking, viewing films, and attempting various cuisines.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
    
    Jonathan Evans is a Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science team at AWS.
    
    Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about constructing options that assist clients accelerate their AI journey and unlock organization worth.