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 model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative AI concepts on AWS.
In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled versions of the designs too.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that utilizes reinforcement learning to boost reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial identifying function is its support knowing (RL) action, which was utilized to fine-tune the design's actions beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually enhancing both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, indicating it's geared up to break down complicated queries and reason through them in a detailed way. This guided reasoning process enables the model to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT abilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the market's attention as a versatile text-generation design that can be integrated into numerous workflows such as agents, sensible reasoning and data interpretation tasks.
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, enabling efficient reasoning by routing queries to the most relevant expert "clusters." This method permits the design to concentrate on various problem domains while maintaining general effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to release 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 design to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective designs to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 design, 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 model, we recommend deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and evaluate designs against crucial safety criteria. At the time of writing this blog, for DeepSeek-R1 releases 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 model, improving user experiences and standardizing safety controls throughout your generative AI applications.
Prerequisites
To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limitation increase, develop a limitation increase request and reach out to your account group.
Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For instructions, see Establish permissions to utilize guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to present safeguards, prevent hazardous material, and assess models against essential security requirements. You can execute safety measures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and model reactions released 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 develop the guardrail, see the GitHub repo.
The general flow 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, trademarketclassifieds.com it's sent out to the design for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this final check, raovatonline.org it's returned as the result. 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 phase. The examples showcased in the following sections 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 structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
1. On the Amazon Bedrock console, choose Model brochure under Foundation designs in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 model.
The design detail page provides important details about the model's abilities, rates structure, and execution guidelines. You can discover detailed use guidelines, including sample API calls and code snippets for combination. The model supports different text generation tasks, consisting of material creation, code generation, and concern answering, utilizing its support learning optimization and CoT thinking capabilities.
The page also includes release choices and licensing details to assist you start with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, pick Deploy.
You will be triggered to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters).
5. For Number of instances, get in a number of instances (in between 1-100).
6. For example type, select your circumstances type. For optimum 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, including virtual personal cloud (VPC) networking, service role approvals, and encryption settings. For many use cases, the default settings will work well. However, for production releases, you might want to examine these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start utilizing the design.
When the deployment is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in playground to access an interactive interface where you can experiment with different prompts and change design specifications like temperature and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal outcomes. For instance, material for reasoning.
This is an excellent method to explore the model's reasoning and text generation abilities before incorporating it into your applications. The playground supplies immediate feedback, helping you comprehend how the model reacts to different inputs and letting you tweak your triggers for ideal results.
You can rapidly test the model in the play ground through the UI. However, to conjure up the programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run inference using guardrails with the released DeepSeek-R1 endpoint
The following code example shows how to perform inference utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, and sends out a request to generate text based upon a user timely.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML solutions that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 practical techniques: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to assist you pick the technique that best fits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:
1. On the SageMaker console, pick Studio in the navigation pane.
2. First-time users will be prompted to develop a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
The design web browser displays available designs, with details like the service provider name and model capabilities.
4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each model card shows key details, including:
- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if applicable), indicating that this design can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
5. Choose the design card to view the design details page.
The design details page includes the following details:
- The model name and company details. Deploy button to release the model. About and Notebooks tabs with detailed details
The About tab includes important details, such as:
- Model description. - License details.
- Technical specifications.
- Usage standards
Before you release the design, it's recommended to review the design details and license terms to confirm compatibility with your usage case.
6. Choose Deploy to continue with implementation.
7. For Endpoint name, utilize the automatically produced name or engel-und-waisen.de produce a custom one.
- For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge).
- For Initial instance count, get in the variety of instances (default: 1). Selecting appropriate instance types and counts is essential for expense and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency.
- Review all setups for accuracy. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
- Choose Deploy to deploy the design.
The implementation process can take several minutes to complete.
When release is total, your endpoint status will alter to InService. At this point, the design is all set to accept inference requests through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is complete, 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 get going with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that shows 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 range from SageMaker Studio.
You can run extra demands against the predictor:
Implement guardrails and run reasoning with your SageMaker JumpStart predictor
Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and execute it as revealed in the following code:
Tidy up
To avoid unwanted charges, complete the actions in this area to tidy up your resources.
Delete the Amazon Bedrock Marketplace release
If you released the model utilizing Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases. - In the Managed deployments area, locate the endpoint you want to erase.
- Select the endpoint, and on the Actions menu, select Delete.
- Verify the endpoint details to make certain you're deleting the right release: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model you deployed will sustain costs 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 deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. 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 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 business develop innovative services using AWS services and sped up compute. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the reasoning efficiency of large language designs. In his spare time, Vivek enjoys treking, viewing motion pictures, and trying various 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 technology and Bioinformatics.
Jonathan Evans is a Professional Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about building services that help customers accelerate their AI journey and unlock service value.