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

Anastasia Conlan edited this page 2 months ago

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 deploy DeepSeek AI's first-generation frontier design, DeepSeek-R1, together with 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 demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the models also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that uses reinforcement learning to enhance thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing feature is its support knowing (RL) action, which was utilized to refine the design's reactions beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, ultimately boosting both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, meaning it's geared up to break down complex queries and factor through them in a detailed manner. This guided thinking procedure enables the design to produce more accurate, transparent, wavedream.wiki and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation design that can be incorporated into various workflows such as agents, sensible thinking and information analysis jobs.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, enabling effective reasoning by routing queries to the most relevant professional "clusters." This approach allows the design to specialize in various problem domains while maintaining overall performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective models to imitate the habits and reasoning patterns of the larger DeepSeek-R1 design, utilizing 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 deploying this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful 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 only the ApplyGuardrail API. You can create numerous guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, 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, select Amazon SageMaker, and verify you're utilizing 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 deploying. To request a limitation boost, wiki.asexuality.org develop a limitation increase demand and connect to your account team.

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 Set up authorizations to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, avoid hazardous content, and examine designs against essential safety requirements. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and design responses released 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 actions: 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 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 stepped in 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 foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:

1. On the Amazon Bedrock console, select Model brochure under Foundation designs in the navigation pane. At the time of writing this post, you can utilize the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 model.

The design detail page supplies important details about the design's capabilities, prices structure, and execution standards. You can find detailed usage guidelines, consisting of sample API calls and code bits for combination. The design supports numerous text generation tasks, including content development, code generation, and question answering, utilizing its support discovering optimization and archmageriseswiki.com CoT reasoning capabilities. The page also consists of implementation options and licensing details to help you get going with DeepSeek-R1 in your applications. 3. To begin utilizing DeepSeek-R1, pick Deploy.

You will be prompted to configure 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 Variety of circumstances, get in a number of instances (between 1-100). 6. For Instance type, select your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. Optionally, you can set up innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and encryption settings. For many use cases, the default settings will work well. However, for production releases, you may wish to evaluate these settings to align with your organization's security and compliance requirements. 7. Choose Deploy to start using the design.

When the deployment is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. 8. Choose Open in play ground to access an interactive user interface where you can try out different triggers and adjust model criteria like temperature and optimum length. When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal results. For instance, content for reasoning.

This is an excellent way to check out the model's reasoning and text generation abilities before incorporating it into your applications. The play area provides immediate feedback, assisting you comprehend how the model reacts to different inputs and letting you tweak your prompts for optimal results.

You can rapidly test 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 reasoning using a released DeepSeek-R1 design 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 create the guardrail, see the GitHub repo. After you have actually developed the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up inference criteria, 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) center with FMs, built-in algorithms, and prebuilt ML options that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, setiathome.berkeley.edu with your information, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 practical approaches: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you choose the method that best suits your requirements.

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, choose JumpStart in the navigation pane.

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

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

- Model name

• Provider name
• Task classification (for example, Text Generation). Bedrock Ready badge (if relevant), indicating that this design can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the design
  
  5. Choose the model card to view the model details page.
  
  The design details page includes the following details:
  
  - The design name and provider details. Deploy button to release the model. About and Notebooks tabs with detailed details
  
  The About tab includes crucial details, such as:
  
  - Model description.
• License details.
• Technical requirements. guidelines
  
  Before you deploy the model, it's suggested to examine the model details and license terms to confirm compatibility with your usage case.
  
  6. Choose Deploy to continue with deployment.
  
  7. For Endpoint name, use the automatically created name or create a customized one.

8. For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, get in the number of circumstances (default: 1). Selecting appropriate circumstances types and counts is crucial 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 highly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to release the design.
    
    The release process can take numerous minutes to complete.
    
    When release is total, your endpoint status will alter to InService. At this point, the design is ready to accept reasoning demands through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is complete, you can conjure up the model using a SageMaker runtime customer and integrate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up 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 deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is provided in the Github here. You can clone the note pad and range from SageMaker Studio.
    
    You can run additional requests against the predictor:
    
    Implement guardrails and run reasoning with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:
    
    Clean up
    
    To prevent undesirable charges, complete the actions in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you released the model utilizing Amazon Bedrock Marketplace, complete the following actions:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases.
12. In the Managed deployments section, find the endpoint you want to delete.
13. Select the endpoint, and on the Actions menu, pick Delete.
14. Verify the endpoint details to make certain you're deleting the right 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 checked out 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 start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, 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 business develop innovative solutions utilizing AWS services and accelerated calculate. Currently, he is concentrated on developing methods for fine-tuning and optimizing the reasoning performance of large language designs. In his totally free time, Vivek enjoys hiking, seeing motion pictures, and attempting various foods.
    
    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 Science and Bioinformatics.
    
    Jonathan Evans is a Professional Solutions Architect working on generative AI with the Third-Party Model Science team 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 developing options that assist consumers accelerate their AI journey and unlock service worth.