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

Aiden Parkin edited this page 1 month ago

Today, we are delighted 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, together with the distilled versions varying from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative AI concepts on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a large language design (LLM) established by DeepSeek AI that utilizes reinforcement learning to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial identifying function is its reinforcement learning (RL) step, which was used to refine the design's responses beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, ultimately enhancing both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, indicating it's geared up to break down intricate queries and factor through them in a detailed manner. This directed thinking process enables the design to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to produce structured responses while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as representatives, logical reasoning and information analysis jobs.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, enabling effective reasoning by routing inquiries to the most appropriate expert "clusters." This method allows the design to concentrate on different problem domains while maintaining general efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model to more effective 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 sized, more effective designs to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 design, using it as a teacher model.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid damaging material, and examine designs against key safety criteria. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, you require 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 using 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 ask for a limitation increase, create a limitation boost demand and connect 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) authorizations to use Amazon Bedrock Guardrails. For directions, see Establish authorizations to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails permits you to present safeguards, prevent harmful content, and evaluate designs against crucial safety criteria. You can execute security for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.

The general flow includes 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 getting the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened 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 areas 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 models (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 invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 design.

The design detail page supplies necessary details about the model's abilities, rates structure, and execution standards. You can discover detailed usage guidelines, consisting of sample API calls and code bits for combination. The design supports various text generation tasks, consisting of content production, code generation, and concern answering, using its support discovering optimization and CoT thinking capabilities. The page likewise includes release options and licensing details to assist you get going with DeepSeek-R1 in your applications. 3. To start utilizing DeepSeek-R1, pick Deploy.

You will be prompted to set up the implementation 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 circumstances, get in a number of instances (between 1-100). 6. For Instance type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. Optionally, you can set up innovative security and infrastructure settings, including virtual private cloud (VPC) networking, service function authorizations, and encryption settings. For many utilize cases, the default settings will work well. However, for production releases, you might wish to examine these settings to align with your company's security and compliance requirements. 7. Choose Deploy to begin using the design.

When the release is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. 8. Choose Open in play area to access an interactive interface where you can experiment with various prompts and change model criteria like temperature level and optimum length. When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For instance, material for inference.

This is an exceptional way to explore the design's thinking and text generation abilities before integrating it into your applications. The play ground provides instant feedback, assisting you comprehend how the model responds to different inputs and letting you fine-tune your triggers for optimal results.

You can rapidly check the model 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 using guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock using 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 produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, setiathome.berkeley.edu and sends a request to produce text based upon a user timely.

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 couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart uses two practical methods: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you choose 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, 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 internet browser displays available models, with details like the company name and model capabilities.

4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. Each design card reveals essential details, consisting of:

- Model name

• Provider name
• Task category (for instance, Text Generation). Bedrock Ready badge (if applicable), indicating that this model can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the design
  
  5. Choose the design card to view the design details page.
  
  The model details page consists of the following details:
  
  - The model name and service provider details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
  
  The About tab consists of crucial details, such as:
  
  - Model description.
• License details.
• Technical requirements.
• Usage guidelines
  
  Before you release the design, it's advised to examine the design details and license terms to verify compatibility with your usage case.
  
  6. Choose Deploy to proceed with deployment.
  
  7. For Endpoint name, use the instantly produced name or develop a custom one.

8. For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the number of circumstances (default: 1). Selecting appropriate instance types and counts is crucial for expense 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 setups for precision. For this model, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to release the design.
    
    The implementation process can take a number of minutes to finish.
    
    When implementation is complete, your endpoint status will alter to InService. At this point, the design is all set to accept inference requests through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is total, you can invoke the model using a SageMaker runtime customer and incorporate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To get going with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.
    
    You can run extra requests 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:
    
    Tidy up
    
    To avoid undesirable charges, complete the steps in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you deployed the design utilizing 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 section, find the endpoint you wish to erase.
13. Select the endpoint, and on the Actions menu, choose Delete.
14. Verify the endpoint details to make certain you're erasing the correct implementation: 1. Endpoint name.
15. Model name.
16. 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 desire 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 utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. 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 Starting with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI business develop ingenious solutions using AWS services and sped up compute. Currently, he is focused on establishing strategies for fine-tuning and enhancing the inference efficiency of big language models. In his leisure time, Vivek takes pleasure in treking, enjoying movies, and attempting different cuisines.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area 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 team at AWS.
    
    Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about constructing solutions that assist customers accelerate their AI journey and unlock business worth.