1 DeepSeek R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart

Letha Prescott edited this page 4 months ago

Today, we are delighted to announce 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 versions varying from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the designs too.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that uses reinforcement learning to enhance thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key identifying feature is its reinforcement knowing (RL) action, which was used to refine the model's responses beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, meaning it's geared up to break down complicated inquiries and reason through them in a detailed manner. This assisted thinking procedure permits the design to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the industry's attention as a flexible text-generation design that can be incorporated into numerous workflows such as agents, logical reasoning and information 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, allowing efficient reasoning by routing queries to the most pertinent professional "clusters." This method enables the model to concentrate on various problem domains while maintaining overall performance. DeepSeek-R1 needs at least 800 GB of in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking 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 describes a process of training smaller sized, more effective designs to mimic the habits and reasoning patterns of the larger DeepSeek-R1 model, using it as a teacher design.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent hazardous content, and examine models against essential safety criteria. At the time of composing this blog, 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 usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To examine if you have quotas for 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 deploying. To request a limitation increase, create a limitation boost demand and connect to your account group.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to introduce safeguards, prevent harmful content, and evaluate models against key safety requirements. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.

The general circulation involves the following steps: 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 reasoning. After getting the design's output, another guardrail check is used. If the output passes this last check, it's returned as the final result. However, wiki.lafabriquedelalogistique.fr if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas demonstrate reasoning utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

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 doesn't support Converse APIs and other Amazon Bedrock tooling. 2. Filter for yewiki.org DeepSeek as a supplier and select the DeepSeek-R1 model.

The design detail page provides vital details about the model's abilities, rates structure, and execution standards. You can discover detailed use directions, consisting of sample API calls and code snippets for combination. The model supports numerous text generation tasks, consisting of material development, code generation, and concern answering, yewiki.org utilizing its reinforcement discovering optimization and CoT reasoning capabilities. The page likewise consists of implementation alternatives and licensing details to help you start with DeepSeek-R1 in your applications. 3. To begin utilizing DeepSeek-R1, choose Deploy.

You will be triggered to configure the release 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 Variety of circumstances, get in a variety of instances (in between 1-100). 6. For Instance type, select your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. Optionally, you can set up sophisticated security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role approvals, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you might wish to evaluate these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to start utilizing the design.

When the release is total, you can check 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 prompts and change model specifications like temperature level and optimum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For example, content for inference.

This is an excellent method to check out the model's thinking and text generation abilities before integrating it into your applications. The playground provides immediate feedback, helping you understand how the model reacts to different inputs and letting you fine-tune your triggers for optimum results.

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

Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example shows how to carry out inference using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends out a request to create 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 options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient methods: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you pick the approach that finest fits 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, choose Studio in the navigation pane. 2. First-time users will be prompted to develop a domain. 3. On the SageMaker Studio console, pick JumpStart in the navigation pane.

The design web browser shows available designs, with details like the provider name and model capabilities.

4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. Each design card reveals key details, including:

- Model name

• Provider name
• Task category (for example, Text Generation). Bedrock Ready badge (if applicable), indicating that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the model
  
  5. Choose the model card to view the model details page.
  
  The model details page consists of the following details:
  
  - The model name and service provider details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
  
  The About tab consists of important details, such as:
  
  - Model description.
• License details.
• Technical specs.
• Usage standards
  
  Before you release the design, it's advised to review the design details and license terms to validate compatibility with your usage case.
  
  6. Choose Deploy to proceed with deployment.
  
  7. For Endpoint name, use the automatically created name or develop a custom-made one.

8. For example type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, go into the variety of circumstances (default: 1). Selecting suitable circumstances types and counts is vital for cost and performance optimization. Monitor your deployment 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 setups for accuracy. For this model, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to release the model.
    
    The deployment process can take several minutes to finish.
    
    When release is complete, your endpoint status will alter to InService. At this moment, the design is prepared to accept inference requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation is total, you can invoke the model utilizing a SageMaker runtime client and integrate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the needed AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is offered in the Github here. You can clone the note pad and range from SageMaker Studio.
    
    You can run additional 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 create a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
    
    Tidy up
    
    To prevent undesirable charges, finish the steps in this area to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    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, select Marketplace releases.
12. In the Managed releases section, locate the endpoint you wish to erase.
13. Select the endpoint, and on the Actions menu, select Delete.
14. Verify the endpoint details to make certain you're erasing the right implementation: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design you deployed 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 utilizing 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 designs, SageMaker JumpStart pretrained models, 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 helps emerging generative AI business build innovative solutions utilizing AWS services and sped up calculate. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the reasoning performance of large language designs. In his totally free time, Vivek enjoys hiking, watching motion pictures, and trying 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 Science and Bioinformatics.
    
    Jonathan Evans is an Expert Solutions Architect working on generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about building options that assist consumers accelerate their AI journey and unlock organization worth.