Today, we are thrilled 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 parameters to construct, experiment, and responsibly scale your generative AI concepts on AWS.
In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled versions of the designs as well.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that utilizes support discovering to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key differentiating feature is its reinforcement knowing (RL) step, which was used to fine-tune the design's reactions beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, ultimately enhancing both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, suggesting it's geared up to break down intricate inquiries and reason through them in a detailed manner. This guided reasoning process enables the model to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to create structured responses while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the industry's attention as a flexible text-generation model that can be integrated into different workflows such as representatives, 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 enables activation of 37 billion criteria, enabling efficient reasoning by routing queries to the most appropriate expert "clusters." This approach enables the model to specialize in different problem domains while maintaining general performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 design to more effective architectures based on 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 sized, more effective models to imitate the habits and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor design.
You can release DeepSeek-R1 model 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 site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and examine models against key security requirements. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative AI applications.
Prerequisites
To release 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, choose Amazon SageMaker, and verify you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limit increase, produce a limit increase request and connect to your account group.
Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For guidelines, see Set up consents to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to present safeguards, avoid hazardous content, and evaluate models against essential safety criteria. You can carry out precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.
The general flow involves the following steps: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for reasoning. After getting 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 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 phase. The examples showcased in the following sections show inference utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized structure models (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 brochure 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 service provider and pick the DeepSeek-R1 design.
The model detail page offers vital details about the model's capabilities, pricing structure, and implementation standards. You can discover detailed use directions, consisting of sample API calls and code snippets for combination. The design supports different text generation tasks, consisting of content production, code generation, and concern answering, using its support learning optimization and CoT thinking capabilities.
The page likewise consists of release choices and systemcheck-wiki.de licensing details to help you begin with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, select Deploy.
You will be prompted to configure the deployment 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, get in a number of instances (between 1-100).
6. For Instance type, choose your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can set up sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role authorizations, and encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you may wish to examine these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the model.
When the deployment is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in play ground to access an interactive interface where you can try out different prompts and change model specifications like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For example, content for inference.
This is an excellent method to explore the model's thinking and text generation capabilities before integrating it into your applications. The play ground supplies immediate feedback, assisting you comprehend how the model reacts to different inputs and letting you tweak your prompts for optimal results.
You can rapidly evaluate the model in the play ground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run reasoning using guardrails with the released DeepSeek-R1 endpoint
The following code example shows how to perform reasoning using a released DeepSeek-R1 model 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 created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, and sends a demand 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 simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and release them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 convenient approaches: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both methods to help you select the method that finest fits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:
1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be triggered to develop a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
The design browser shows available models, with details like the provider name and design capabilities.
4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each model card shows key details, including:
- 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, enabling you to use Amazon Bedrock APIs to invoke the design
5. Choose the model card to view the model details page.
The model details page consists of the following details:
- The model name and company details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
The About tab consists of essential details, such as:
- Model description. - License details.
- Technical specs.
- Usage guidelines
Before you release the model, it's advised to evaluate the model details and license terms to verify compatibility with your usage case.
6. Choose Deploy to proceed with implementation.
7. For Endpoint name, utilize the automatically produced name or produce a custom one.
- For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, enter the variety of circumstances (default: 1). Selecting appropriate instance types and counts is crucial for expense and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency.
- Review all setups for accuracy. For this design, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
- Choose Deploy to deploy the model.
The deployment process can take numerous minutes to finish.
When implementation is total, your endpoint status will alter to InService. At this moment, the design is ready to accept reasoning requests through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, it-viking.ch which will display relevant metrics and status details. When the release is total, you can invoke the model utilizing a SageMaker runtime client and incorporate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need 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 release and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is offered in the Github here. You can clone the notebook and range 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 create a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:
Tidy up
To avoid unwanted charges, complete the actions in this section to tidy up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you released the model utilizing Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon console, under Foundation designs in the navigation pane, pick Marketplace releases. - In the Managed releases area, locate 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 right deployment: 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 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, 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 assists emerging generative AI companies build innovative options using AWS services and accelerated compute. Currently, he is concentrated on establishing strategies for fine-tuning and enhancing the reasoning performance of large language models. In his leisure time, Vivek delights in treking, seeing movies, and engel-und-waisen.de trying various cuisines.
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 an Expert Solutions Architect working on 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 building solutions that assist clients accelerate their AI journey and unlock business worth.