Today, we are thrilled to reveal 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‘s first-generation frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled versions of the models as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that utilizes support finding out to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential differentiating feature is its support knowing (RL) step, which was utilized to fine-tune the model's reactions beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, eventually improving both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it's equipped to break down complex queries and factor through them in a detailed manner. This assisted thinking procedure allows the design to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually captured the industry's attention as a flexible text-generation model that can be incorporated into different workflows such as representatives, logical reasoning and data analysis jobs.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion specifications, enabling efficient reasoning by routing inquiries to the most relevant expert “clusters.” This method allows the model to specialize in different issue domains while maintaining total performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more efficient architectures based on 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 sized, more effective models to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher model.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and assess designs against essential security requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you require access to an ml.p5e instance. To examine if you have quotas for forum.pinoo.com.tr P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate 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 releasing. To ask for a limitation increase, develop a limitation increase demand and reach out to your account team.

Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For directions, see Set up permissions to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to introduce safeguards, avoid hazardous material, and assess models against key security criteria. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and model actions deployed 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 produce the guardrail, see the GitHub repo.

The general flow involves the following steps: First, the system gets 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 model for inference. After getting the model'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 intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following areas show reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

1. On the Amazon Bedrock console, select Model brochure under Foundation models in the navigation pane. At the time of composing this post, you can use 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 select the DeepSeek-R1 design.

The design detail page offers necessary details about the model's capabilities, prices structure, and implementation standards. You can find detailed usage directions, consisting of sample API calls and code snippets for combination. The design supports different text generation jobs, including material production, code generation, and question answering, utilizing its reinforcement learning optimization and CoT thinking capabilities. The page also includes implementation alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications. 3. To start utilizing DeepSeek-R1, choose Deploy.

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

When the implementation is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. 8. Choose Open in play area to access an interactive interface where you can experiment with different triggers and change model specifications like temperature and optimum length. When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum results. For instance, material for reasoning.

This is an outstanding way to check out the model's thinking and text generation capabilities before incorporating it into your applications. The playground offers instant feedback, helping you comprehend how the design responds to different inputs and letting you tweak your triggers for optimal outcomes.

You can rapidly test the design in the playground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to carry out inference utilizing a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and 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 produced the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, and sends out a demand to generate text based on a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML options that you can deploy with just a couple of 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.

Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 practical approaches: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both methods to assist you select the technique that best matches your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:

1. On the SageMaker console, pick Studio in the navigation pane. 2. First-time users will be triggered to produce 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 design capabilities.

4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. Each design card shows essential details, including:

- Model name

• Provider name
• Task classification (for example, Text Generation). Bedrock Ready badge (if appropriate), indicating that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the design
  
  5. Choose the model card to see the model details page.
  
  The model details page includes the following details:
  
  - The design name and service provider details. Deploy button to release the design. About and Notebooks tabs with detailed details
  
  The About tab includes crucial details, such as:
  
  - Model description.
• License details.
• Technical specs.
• Usage standards
  
  Before you deploy the design, it's recommended to examine the design details and license terms to verify compatibility with your usage case.
  
  6. Choose Deploy to proceed with release.
  
  7. For Endpoint name, use the automatically generated name or develop a customized one.

1. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
2. For Initial instance count, enter the variety of circumstances (default: 1). Selecting suitable circumstances types and counts is crucial for expense and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency.
3. Review all configurations for accuracy. For this model, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
4. Choose Deploy to deploy the model.
   
   The implementation procedure can take several minutes to finish.
   
   When deployment is complete, your endpoint status will change to InService. At this point, the model is prepared to accept inference demands through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is total, you can invoke the model utilizing a SageMaker runtime customer and integrate it with your applications.
   
   Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
   
   To get started with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS permissions and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for deploying the model is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
   
   You can run additional requests against the predictor:
   
   Implement guardrails and run inference with your SageMaker JumpStart predictor
   
   Similar to Amazon Bedrock, you can also use 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 avoid undesirable charges, finish the actions in this section to clean up your resources.
   
   Delete the Amazon Bedrock Marketplace deployment
   
   If you released the model using Amazon Bedrock Marketplace, complete the following actions:
   
   1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace deployments.
5. In the Managed deployments section, find the endpoint you desire to delete.
6. Select the endpoint, and on the Actions menu, select Delete.
7. Verify the endpoint details to make certain you're erasing the proper implementation: 1. Endpoint name.
8. Model name.
9. Endpoint status
   
   Delete the SageMaker JumpStart predictor
   
   The SageMaker JumpStart design you released will sustain costs 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.
   
   Conclusion
   
   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 now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.
   
   About the Authors
   
   Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative AI companies build innovative services using AWS services and accelerated compute. Currently, he is focused on developing techniques for fine-tuning and enhancing the reasoning performance of large language models. In his leisure time, Vivek enjoys treking, enjoying movies, and trying various foods.
   
   Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
   
   Jonathan Evans is a Professional Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
   
   Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is enthusiastic about constructing options that assist customers accelerate their AI journey and unlock service worth.