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

Adela Eisenhauer edited this page 2 weeks ago

Today, we are delighted 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 model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative AI concepts on AWS.

In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled versions of the models also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that utilizes support learning to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial differentiating feature is its reinforcement knowing (RL) step, which was utilized to fine-tune the design's responses beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually improving both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, meaning it's geared up to break down complicated inquiries and reason through them in a detailed way. This assisted reasoning process enables the design to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation model that can be incorporated into different workflows such as agents, logical reasoning and data interpretation jobs.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion parameters, allowing effective inference by routing inquiries to the most pertinent professional "clusters." This method allows the model to concentrate on different issue domains while maintaining general effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more effective designs to imitate the habits and reasoning patterns of the larger DeepSeek-R1 model, using it as an instructor model.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and assess designs against essential safety requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To inspect 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 releasing. To request a limit increase, produce a limitation boost demand and connect to your account group.

Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) approvals to use 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 damaging material, and examine models against key safety criteria. You can carry out security measures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.

The basic 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 design for inference. After receiving the design's output, another guardrail check is used. If the output passes this last check, it's returned as the last outcome. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following areas demonstrate reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives 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 catalog under Foundation models in the navigation pane. At the time of composing this post, you can use the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 design.

The model detail page offers important details about the model's capabilities, rates structure, and implementation standards. You can discover detailed use instructions, consisting of sample API calls and code snippets for integration. The design supports different text generation tasks, including material production, code generation, and concern answering, it-viking.ch utilizing its support discovering optimization and CoT reasoning abilities. The page likewise consists of release alternatives and licensing details to assist you get begun with DeepSeek-R1 in your applications. 3. To begin using 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, get in an endpoint name (in between 1-50 alphanumeric characters). 5. For Number of instances, go into a variety of circumstances (in between 1-100). 6. For Instance type, pick your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. Optionally, you can configure advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For many utilize cases, the default settings will work well. However, for production releases, you may wish to examine these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to start using the design.

When the deployment is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. 8. Choose Open in playground to access an interactive user interface where you can try out various prompts and adjust model parameters like temperature level and optimum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal outcomes. For instance, material for reasoning.

This is an excellent way to check out the model's reasoning and text generation capabilities before integrating it into your applications. The play ground provides immediate feedback, helping you understand how the model responds to various inputs and letting you fine-tune your prompts for optimum outcomes.

You can quickly check the model in the playground 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 released DeepSeek-R1 endpoint

The following code example shows how to carry out reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends a request to generate text based upon a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML services 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 information, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 convenient approaches: utilizing the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you select the method that finest suits 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, select Studio in the navigation pane. 2. First-time users will be prompted to develop a domain. 3. On the SageMaker Studio console, select JumpStart in the navigation pane.

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

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

- Model name

• Provider name
• Task classification (for example, Text Generation). Bedrock Ready badge (if appropriate), suggesting that this model can be signed up with Amazon Bedrock, permitting you to utilize 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 model name and provider details. Deploy button to release the model. About and Notebooks tabs with detailed details
  
  The About tab consists of essential details, such as:
  
  - Model description.
• License details.
• Technical requirements.
• Usage guidelines
  
  Before you release the model, it's suggested to evaluate the model details and license terms to validate compatibility with your usage case.
  
  6. Choose Deploy to continue with deployment.
  
  7. For Endpoint name, utilize the immediately created name or produce a custom-made one.

8. For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, go into the variety of instances (default: 1). Selecting proper 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 enhanced for sustained traffic and low latency.
10. Review all configurations for precision. For this model, we highly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to release the design.
    
    The deployment process can take several minutes to complete.
    
    When release is complete, your will alter to InService. At this moment, the model is all set to accept reasoning demands through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will display relevant 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 begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is supplied 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 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 revealed in the following code:
    
    Clean up
    
    To avoid unwanted charges, complete the actions in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you deployed the model using Amazon Bedrock Marketplace, total the following actions:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace releases.
12. In the Managed deployments area, locate the endpoint you desire to delete.
13. Select the endpoint, and on the Actions menu, select Delete.
14. Verify the endpoint details to make certain you're erasing the right deployment: 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 want to stop sustaining charges. For more details, see Delete Endpoints and wiki.snooze-hotelsoftware.de Resources.
    
    Conclusion
    
    In this post, we checked out how you can access and deploy the DeepSeek-R1 model 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 models, SageMaker JumpStart pretrained models, 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 helps emerging generative AI companies build innovative services utilizing AWS services and accelerated calculate. Currently, he is focused on developing methods for fine-tuning and optimizing the reasoning performance of big language designs. In his spare time, Vivek delights in treking, watching motion pictures, and attempting different foods.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team 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 Specialist 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 hub. She is passionate about developing services that assist consumers accelerate their AI journey and unlock business worth.