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Today, we are delighted to announce 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 design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion specifications to build, experiment, and properly 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 comparable actions to deploy the distilled versions of the models also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that uses reinforcement finding out to boost thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential identifying feature is its reinforcement learning (RL) action, which was utilized to improve the model's actions beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually improving both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, meaning it's geared up to break down complicated questions and reason through them in a detailed manner. This directed thinking process permits the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually captured the market's attention as a flexible text-generation design that can be incorporated into various workflows such as representatives, rational thinking and data interpretation tasks.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion criteria, enabling efficient inference by routing questions to the most appropriate specialist "clusters." This method allows the design to concentrate on different issue domains while maintaining overall performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled models 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 process of training smaller, more effective designs to simulate the behavior and thinking patterns of the larger DeepSeek-R1 design, utilizing it as a teacher model.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and examine designs against key safety criteria. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several guardrails tailored to different use cases and use them to the DeepSeek-R1 design, improving 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 circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm 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 limit boost, develop a limit increase demand and reach out to your account team.

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

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, avoid damaging content, and assess models against crucial security criteria. You can execute safety measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.

The general circulation involves the following steps: First, the system receives 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 inference. After getting 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 intervened 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 sections show reasoning utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

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

The design detail page offers necessary details about the model's capabilities, pricing structure, and implementation guidelines. You can find detailed usage directions, including calls and code bits for combination. The design supports numerous text generation jobs, consisting of material development, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT thinking abilities. The page also consists of deployment options and licensing details to help you get going with DeepSeek-R1 in your applications. 3. To start utilizing DeepSeek-R1, choose Deploy.

You will be prompted to set up the deployment 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, enter a variety of instances (in between 1-100). 6. For example type, pick your instance type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. Optionally, you can set up advanced security and facilities settings, consisting of virtual private cloud (VPC) networking, service role approvals, and encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you might wish to review these settings to align with your organization's security and compliance requirements. 7. Choose Deploy to begin utilizing the model.

When the implementation is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area. 8. Choose Open in playground to access an interactive interface where you can explore various prompts and change design parameters like temperature level and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal outcomes. For instance, material for inference.

This is an exceptional method to explore the design's reasoning and text generation abilities before incorporating it into your applications. The play ground offers instant feedback, assisting you comprehend how the model reacts to various inputs and letting you tweak your triggers for optimum outcomes.

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

Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to perform inference using a released 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 actually created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, configures inference specifications, and sends out 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 simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart offers two convenient techniques: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to help you pick the technique that finest fits your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:

1. On the SageMaker console, pick Studio in the navigation pane. 2. First-time users will be triggered to create a domain. 3. On the SageMaker Studio console, choose JumpStart in the navigation pane.

The model web browser shows available models, with details like the supplier name and model capabilities.

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

- Model name

• Provider name
• Task category (for instance, hb9lc.org Text Generation). Bedrock Ready badge (if suitable), indicating that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the model
  
  5. Choose the model card to view the design details page.
  
  The model details page consists of the following details:
  
  - The design name and larsaluarna.se provider details. Deploy button to release the design. About and Notebooks tabs with detailed details
  
  The About tab consists of crucial details, such as:
  
  - Model description.
• License details.
• Technical specifications.
• Usage standards
  
  Before you deploy the design, it's recommended to evaluate the design details and license terms to verify compatibility with your usage case.
  
  6. Choose Deploy to continue with implementation.
  
  7. For Endpoint name, utilize the automatically generated name or produce a custom one.

8. For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the variety of instances (default: 1). Selecting proper instance types and counts is important for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency.
10. Review all setups for precision. For this model, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to deploy the design.
    
    The implementation procedure can take numerous minutes to finish.
    
    When implementation is complete, your endpoint status will alter to InService. At this point, the model is all set to accept inference requests through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is complete, you can invoke the model using a SageMaker runtime client and integrate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To get started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up 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 release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is offered in the Github here. You can clone the note pad and run 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 likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:
    
    Tidy up
    
    To avoid undesirable 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, complete the following actions:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments.
12. In the Managed releases area, find the endpoint you desire to erase.
13. Select the endpoint, and on the Actions menu, pick 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 model you deployed will sustain expenses 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, it-viking.ch we checked out how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, refer to 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 assists emerging generative AI companies build ingenious options using AWS services and sped up compute. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the inference performance of big language designs. In his complimentary time, Vivek takes pleasure in treking, enjoying films, and trying different 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 Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about building solutions that assist consumers accelerate their AI journey and unlock company value.