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 release DeepSeek AI’s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion criteria to build, 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 versions of the models as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that uses support finding out to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential identifying function is its support learning (RL) action, which was utilized to fine-tune the model’s responses beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, eventually boosting both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, meaning it’s geared up to break down complex questions and factor through them in a detailed manner. This assisted reasoning process permits the model to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has caught the market’s attention as a versatile text-generation design that can be integrated into different workflows such as agents, rational reasoning and data interpretation jobs.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion specifications, enabling efficient inference by routing queries to the most relevant specialist “clusters.” This approach allows the model to concentrate on various issue domains while maintaining overall effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design 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 sized, more effective models to mimic the habits and thinking patterns of the larger DeepSeek-R1 model, utilizing it as an instructor design.

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 site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and examine models against key security requirements. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, enhancing 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 instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, surgiteams.com and verify you’re using 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 boost, develop a limit boost request and reach out to your account group.

Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For directions, see Establish approvals to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to introduce safeguards, avoid damaging material, and evaluate designs against crucial security criteria. You can execute precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or wiki.snooze-hotelsoftware.de the API. For the example code to create 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 reasoning. After receiving the model’s output, another guardrail check is used. If the output passes this final check, it’s returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas show inference 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 composing this post, you can use the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling.

1. Filter for DeepSeek as a company and select the DeepSeek-R1 model.
   
   The design detail page offers vital details about the design’s abilities, yewiki.org pricing structure, and application guidelines. You can find detailed usage guidelines, consisting of sample API calls and code snippets for combination. The design supports different text generation jobs, including content production, code generation, and concern answering, utilizing its support learning optimization and CoT thinking capabilities. The page also consists of release choices and licensing details to help you get started with DeepSeek-R1 in your applications.
2. To begin utilizing DeepSeek-R1, select Deploy.
   
   You will be prompted to configure the release details for DeepSeek-R1. The design ID will be pre-populated.
3. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters).
4. For Variety of instances, go into a variety of instances (between 1-100).
5. For Instance type, choose 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 facilities settings, including virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production releases, you may wish to review these settings to line up with your organization’s security and compliance requirements.
6. Choose Deploy to start utilizing the model.
   
   When the release is total, you can evaluate DeepSeek-R1’s capabilities straight in the Amazon Bedrock play area.
7. Choose Open in play area to access an interactive user interface where you can explore different prompts and change model criteria like temperature level and optimum length. When utilizing R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat template for optimum results. For instance, content for inference.
   
   This is an excellent method to check out the design’s reasoning and text generation capabilities before integrating it into your applications. The play ground offers instant feedback, helping you understand how the model reacts to various inputs and letting you tweak your for optimal outcomes.
   
   You can quickly test the model in the play ground through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
   
   Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
   
   The following code example shows how to carry out reasoning using a deployed DeepSeek-R1 model 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 create the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning specifications, and sends a request 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 services that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and release them into production using either the UI or SDK.
   
   Deploying DeepSeek-R1 design through SageMaker JumpStart provides two hassle-free approaches: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let’s check out both approaches to assist you select the method that best fits your requirements.
   
   Deploy DeepSeek-R1 through SageMaker JumpStart UI
   
   Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:
   
   1. On the SageMaker console, select Studio in the navigation pane.
8. First-time users will be triggered to develop a domain.
9. On the SageMaker Studio console, select JumpStart in the navigation pane.
   
   The model browser shows available designs, with details like the company name and design capabilities.
   
   4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. Each design card shows essential details, consisting of:
   
   - Model name
10. Provider name
11. Task classification (for instance, Text Generation). Bedrock Ready badge (if relevant), indicating that this model can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the design
    
    5. Choose the model card to see the model details page.
    
    The design details page consists of the following details:
    
    - The design name and company details. Deploy button to deploy the model. About and gratisafhalen.be Notebooks tabs with detailed details
    
    The About tab consists of essential details, such as:
    
    - Model description.
12. License details.
13. Technical requirements.
14. Usage guidelines
    
    Before you deploy the design, it’s suggested to examine the design details and license terms to confirm compatibility with your usage case.
    
    6. Choose Deploy to continue with deployment.
    
    7. For Endpoint name, use the instantly produced name or develop a customized one.
15. For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
16. For Initial circumstances count, get in the number of instances (default: 1). Selecting suitable circumstances types and counts is important for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency.
17. Review all setups for accuracy. For this design, engel-und-waisen.de we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
18. Choose Deploy to deploy the design.
    
    The implementation procedure can take several minutes to finish.
    
    When deployment is total, your endpoint status will change to InService. At this moment, the model is ready to accept reasoning demands through the endpoint. You can keep track of the release progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the deployment is total, you can invoke the design using a SageMaker runtime client and integrate it with your applications.
    
    Deploy DeepSeek-R1 utilizing 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 required AWS consents 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 deploying the design is offered in the Github here. You can clone the notebook 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
    
    Clean up
    
    To avoid unwanted charges, finish the steps in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace implementation
    
    If you released the model utilizing Amazon Bedrock Marketplace, complete the following steps:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace implementations.
19. In the Managed releases section, locate the endpoint you want to delete.
20. Select the endpoint, and on the Actions menu, choose Delete.
21. Verify the endpoint details to make certain you’re deleting the appropriate deployment: 1. Endpoint name.
22. Model name.
23. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    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 Resources.
    
    Conclusion
    
    In this post, surgiteams.com we explored how you can access and release the DeepSeek-R1 model 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 models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI companies build ingenious solutions utilizing AWS services and sped up compute. Currently, he is concentrated on developing techniques for fine-tuning and optimizing the inference efficiency of large language designs. In his leisure time, Vivek enjoys treking, watching movies, and attempting different 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 technology 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 collaborations for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI hub. She is passionate about constructing services that help clients accelerate their AI journey and unlock business value.