Today, we are delighted 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, along with the distilled variations varying from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative AI concepts on AWS.
In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the designs as well.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that utilizes reinforcement finding out to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial identifying function is its support knowing (RL) action, which was used to fine-tune the model's reactions beyond the standard pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, eventually improving both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, implying it's equipped to break down complicated queries and reason through them in a detailed way. This assisted thinking procedure allows the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT abilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has recorded the market's attention as a versatile text-generation model that can be incorporated into numerous workflows such as representatives, logical thinking and information interpretation tasks.
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion specifications, enabling efficient inference by routing inquiries to the most pertinent professional "clusters." This approach enables the design to specialize in various issue domains while maintaining general efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge includes 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 effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient models to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 design, using it as an instructor 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 place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging material, and examine designs against key safety criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative AI applications.
Prerequisites
To release the DeepSeek-R1 design, 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, wiki.lafabriquedelalogistique.fr select Amazon SageMaker, and validate you're utilizing 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 deploying. To request a limit increase, produce a limitation increase demand and reach out 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) consents to utilize Amazon Bedrock Guardrails. For instructions, see Establish consents to utilize guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to present safeguards, prevent damaging material, and examine designs against crucial safety criteria. You can execute security measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and model reactions 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 gets an input for the design. 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 final result. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred 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 models (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 models in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 model.
The design detail page provides vital details about the model's capabilities, pricing structure, and implementation standards. You can discover detailed usage guidelines, consisting of sample API calls and code bits for integration. The model supports various text generation tasks, consisting of content creation, code generation, and concern answering, using its support discovering optimization and CoT thinking abilities.
The page also consists of implementation choices and licensing details to help you get started with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, choose Deploy.
You will be triggered to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of circumstances, go into a number of instances (in between 1-100).
6. For Instance type, choose your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can set up advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function authorizations, and encryption settings. For the majority of utilize cases, the default settings will work well. However, for production implementations, you might wish to evaluate these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to begin utilizing the design.
When the deployment 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 user interface where you can try out different prompts and change design specifications like temperature and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For instance, material for reasoning.
This is an excellent method to explore the design's reasoning and text generation abilities before integrating it into your applications. The playground provides immediate feedback, assisting you understand how the model responds to numerous inputs and letting you tweak your prompts for optimum outcomes.
You can rapidly check the design in the play area through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
The following code example shows how to carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, and sends out a request to produce text based on a user timely.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML solutions that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and deploy them into production using either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart uses two convenient methods: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you select the approach that best suits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:
1. On the SageMaker console, choose 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 model internet browser displays available designs, with details like the supplier name and design capabilities.
4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each model card shows key details, consisting of:
- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if suitable), indicating that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the design
5. Choose the model card to view the design details page.
The design details page includes the following details:
- The design name and supplier details. Deploy button to release the model. About and Notebooks tabs with detailed details
The About tab includes essential details, such as:
- Model description. - License details.
- Technical specs.
- Usage standards
Before you deploy the model, it's recommended to review the model details and license terms to validate compatibility with your use case.
6. Choose Deploy to proceed with implementation.
7. For Endpoint name, use the instantly produced name or create a custom-made one.
- For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, enter the number of circumstances (default: 1). Selecting appropriate circumstances types and counts is vital for expense and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for pediascape.science sustained traffic and low latency.
- Review all setups for precision. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
- Choose Deploy to release the model.
The implementation process can take a number of minutes to complete.
When deployment is complete, your endpoint status will change to InService. At this moment, the design is ready to accept inference 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 deployment is complete, you can conjure up the design using a SageMaker runtime client and integrate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To start with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for reasoning 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 likewise 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 displayed in the following code:
Tidy up
To prevent undesirable charges, complete the steps in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace deployment
If you released the design using Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations. - In the Managed implementations area, find the endpoint you want to delete.
- Select the endpoint, and on the Actions menu, select Delete.
- Verify the endpoint details to make certain you're deleting the appropriate implementation: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model you released will sustain expenses if you leave it running. Use the following code to delete the endpoint if you desire to stop sustaining charges. For more details, see Delete Endpoints and Resources.
Conclusion
In this post, we explored how you can access and deploy the DeepSeek-R1 model using 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 designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.
About the Authors
Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging AI companies develop innovative options utilizing AWS services and accelerated compute. Currently, he is focused on establishing methods for fine-tuning and enhancing the reasoning efficiency of big language designs. In his totally free time, Vivek takes pleasure in hiking, seeing motion pictures, and attempting different 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 an Expert Solutions Architect working on generative AI with the Third-Party Model Science group at AWS.
Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about developing options that assist clients accelerate their AI journey and unlock business worth.