Today, we are thrilled 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, along with the distilled variations varying from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative AI concepts on AWS.
In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the models as well.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that utilizes reinforcement finding out to enhance thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key distinguishing feature is its support knowing (RL) action, which was utilized to fine-tune the design's reactions beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, eventually boosting both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, meaning it's geared up to break down complex questions and factor through them in a detailed way. This assisted reasoning process enables the model to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually recorded the industry's attention as a flexible text-generation model that can be integrated into numerous workflows such as agents, sensible reasoning and information interpretation jobs.
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion specifications, making it possible for efficient inference by routing inquiries to the most pertinent expert "clusters." This technique permits the design to focus on different issue domains while maintaining general effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for bytes-the-dust.com inference. In this post, we will use an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective designs to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor design.
You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and examine designs against key security criteria. 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 develop numerous guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across 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, choose 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 instance in the AWS Region you are deploying. To request a limit increase, produce a limitation increase request and reach out to your account group.
Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For directions, see Establish permissions to use guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to introduce safeguards, prevent hazardous material, and evaluate models against crucial safety requirements. You can implement precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
The general circulation includes 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 to the model for inference. After getting the model's output, another guardrail check is applied. If the output passes this last 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 happened at the input or output phase. The examples showcased in the following areas show inference using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace gives 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 brochure under Foundation models in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 design.
The model detail page provides vital details about the design's capabilities, prices structure, and implementation standards. You can discover detailed use guidelines, including sample API calls and code snippets for integration. The model supports different text generation jobs, including content production, code generation, and question answering, using its support discovering optimization and CoT reasoning abilities.
The page likewise consists of deployment alternatives and licensing details to help you begin with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, choose Deploy.
You will be prompted to configure the release details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of circumstances, get in a variety of instances (between 1-100).
6. For Instance type, choose your instance type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can configure sophisticated security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function consents, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production implementations, you may wish to examine these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to begin using the model.
When the deployment is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in play ground to access an interactive user interface where you can experiment with various triggers and adjust model criteria like temperature level and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For example, content for inference.
This is an exceptional way to check out the design's thinking and text generation capabilities before incorporating it into your applications. The playground supplies instant feedback, helping you understand how the design responds to numerous inputs and letting you fine-tune your triggers for optimal results.
You can rapidly check the model in the play area through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
The following code example shows how to carry out inference utilizing a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create 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 developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends out a request to produce text based upon 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 solutions that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 practical approaches: using the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you pick the method that best fits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following to release DeepSeek-R1 using SageMaker JumpStart:
1. On the SageMaker console, choose Studio in the navigation pane.
2. First-time users will be prompted to produce a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
The model internet browser shows available designs, with details like the service provider name and model abilities.
4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each design card shows essential details, consisting of:
- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if appropriate), showing that this model can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the design
5. Choose the design card to view the design details page.
The model details page includes the following details:
- The model name and supplier details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
The About tab includes crucial details, such as:
- Model description. - License details.
- Technical specifications.
- Usage standards
Before you release the design, it's suggested to review the model details and license terms to validate compatibility with your use case.
6. Choose Deploy to continue with deployment.
7. For Endpoint name, use the immediately produced name or develop a custom one.
- For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, go into the number of instances (default: 1). Selecting suitable instance types and counts is essential for expense and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency.
- Review all setups for precision. For this design, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
- Choose Deploy to release the design.
The deployment procedure can take numerous minutes to complete.
When deployment is complete, your endpoint status will alter to InService. At this point, the model is ready to accept reasoning requests through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is complete, you can invoke the design utilizing a SageMaker runtime client 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 install the SageMaker Python SDK and make certain you have the required AWS authorizations and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied 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 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 utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
Clean up
To avoid undesirable charges, complete the actions in this section to clean 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 designs in the navigation pane, select Marketplace implementations. - In the Managed releases section, locate the endpoint you wish to delete.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're deleting the correct deployment: 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 get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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 business construct innovative services using AWS services and sped up calculate. Currently, he is focused on developing techniques for fine-tuning and enhancing the reasoning efficiency of large language designs. In his leisure time, Vivek enjoys hiking, watching movies, and trying different 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 technology and Bioinformatics.
Jonathan Evans is an Expert 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 developing services that help consumers accelerate their AI journey and unlock business worth.