Today, we are thrilled 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 release DeepSeek AI's first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative AI ideas on AWS.
In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the designs also.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that uses reinforcement finding out to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential differentiating function is its support knowing (RL) action, which was used to improve the design's actions beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually enhancing both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, indicating it's equipped to break down complicated questions and factor through them in a detailed manner. This guided reasoning process enables the design to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has caught the market's attention as a versatile text-generation design that can be integrated into numerous workflows such as representatives, rational thinking and information analysis tasks.
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion parameters, making it possible for effective inference by routing queries to the most pertinent specialist "clusters." This method allows the model to focus on various issue domains while maintaining total 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 circumstances to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design to more efficient 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 efficient designs to simulate the behavior and thinking 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 model, we advise releasing this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and assess designs against essential security requirements. At the time of composing this blog site, 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 apply 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 instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm 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 increase request and connect to your account team.
Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For guidelines, see Set up consents to utilize guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to introduce safeguards, prevent damaging content, and evaluate models against crucial safety criteria. You can carry out precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and design responses 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 to the design for reasoning. After receiving the design'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 at the input or output phase. The examples showcased in the following sections 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 structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
1. On the Amazon Bedrock console, choose Model catalog under Foundation models in the navigation pane.
At the time of writing 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 provider and choose the DeepSeek-R1 model.
The design detail page offers essential details about the model's abilities, rates structure, and application standards. You can find detailed use directions, including sample API calls and code bits for integration. The model supports numerous text generation tasks, including material production, code generation, and question answering, using its reinforcement learning optimization and CoT thinking abilities.
The page also includes implementation choices and licensing details to help you begin 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 (between 1-50 alphanumeric characters).
5. For Variety of circumstances, get in a number of instances (in between 1-100).
6. For example type, select your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can set up sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service function consents, and file encryption settings. For many utilize 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 utilizing the design.
When the implementation is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground.
8. Choose Open in play ground to access an interactive user interface where you can experiment with 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 design template for optimal outcomes. For example, material for inference.
This is an outstanding way to check out the model's thinking and text generation abilities before integrating it into your applications. The playground offers instant feedback, assisting you comprehend how the model reacts to various inputs and letting you fine-tune your triggers for optimal outcomes.
You can quickly test the design in the playground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
The following code example shows how to perform inference using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and bytes-the-dust.com ApplyGuardrail API. You can develop 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 created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up reasoning criteria, and sends a request to generate text based on 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 options that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and deploy them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart provides two convenient techniques: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you select the technique that best matches 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 prompted to create a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The model web browser shows available models, with details like the provider name and model capabilities.
4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each model card shows essential details, including:
- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if suitable), showing that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the model
5. Choose the model card to view the model details page.
The design details page includes the following details:
- The design name and service 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 guidelines
Before you release the model, it's suggested to review the model details and license terms to confirm compatibility with your use case.
6. Choose Deploy to continue with deployment.
7. For Endpoint name, utilize the automatically produced name or produce a custom one.
- For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, enter the number of instances (default: 1). Selecting appropriate circumstances types and counts is crucial for cost and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency.
- Review all setups for accuracy. For this design, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
- Choose Deploy to release the design.
The implementation procedure can take several minutes to finish.
When implementation is complete, your endpoint status will change to InService. At this moment, the model is all set to accept reasoning demands through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is complete, you can invoke the model utilizing 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 install the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the design is provided in the Github here. You can clone the notebook and run 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create 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 steps in this area to clean up your resources.
Delete the Amazon Bedrock Marketplace release
If you deployed the design using Amazon Bedrock Marketplace, total the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments. - In the Managed implementations area, find the endpoint you desire to delete.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're erasing the correct implementation: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart design you deployed will sustain expenses 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 explored 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 going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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 helps emerging generative AI companies build ingenious options utilizing AWS services and accelerated calculate. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the reasoning efficiency of big language designs. In his downtime, Vivek enjoys hiking, seeing movies, and attempting various 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 technology and Bioinformatics.
Jonathan Evans is a Professional Solutions Architect dealing with 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 constructing services that assist clients accelerate their AI journey and unlock company value.