Today, we are delighted 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, together with the distilled variations ranging from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative AI ideas on AWS.

In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled versions of the designs as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that utilizes reinforcement learning to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential distinguishing function is its support learning (RL) action, which was utilized to fine-tune the model's responses beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, eventually boosting both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, meaning it's equipped to break down complex questions and factor through them in a detailed manner. This guided reasoning process permits the design to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually caught the market's attention as a versatile text-generation model that can be integrated into different workflows such as representatives, sensible reasoning and information analysis jobs.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion parameters, making it possible for effective reasoning by routing inquiries to the most relevant professional "clusters." This approach enables the design to specialize in various problem domains while maintaining total efficiency. 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 includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and forum.batman.gainedge.org 70B). Distillation refers to a process of training smaller sized, more efficient designs to mimic the habits and reasoning patterns of the larger DeepSeek-R1 design, using it as a teacher design.

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 site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and examine models against key security requirements. 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 produce several guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, you require access to an ml.p5e instance. To examine 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 instance in the AWS Region you are deploying. To ask for a limitation boost, develop a limitation increase demand and reach out to your account group.

Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For guidelines, see Set up approvals to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to introduce safeguards, prevent damaging material, and assess designs against crucial security requirements. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to examine user inputs and model reactions released on Amazon Bedrock Marketplace and larsaluarna.se 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 basic circulation includes 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 receiving the design's output, another guardrail check is used. If the output passes this final check, it's returned as the last 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 phase. The examples showcased in the following sections show 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, total the following steps:

1. On the Amazon Bedrock console, select Model brochure under Foundation designs 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 provider and pick the DeepSeek-R1 design.

The design detail page provides necessary details about the model's capabilities, prices structure, and execution guidelines. You can discover detailed usage directions, including sample API calls and code snippets for combination. The model supports various text generation jobs, consisting of content production, code generation, and concern answering, using its reinforcement learning optimization and CoT reasoning abilities.
The page also consists of release options and licensing details to help you begin with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, select Deploy.

You will be triggered to set up the implementation details for DeepSeek-R1. The design 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 circumstances (in between 1-100).
6. For example type, select your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function consents, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you may want to evaluate 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 evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground.
8. Choose Open in play area to access an interactive interface where you can try out various triggers and adjust design criteria like temperature level and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For example, <｜begin▁of▁sentence｜><｜User｜>content for reasoning<｜Assistant｜>.

This is an outstanding way to explore the design's thinking and text generation abilities before incorporating it into your applications. The playground offers immediate feedback, helping you comprehend how the design responds to various inputs and letting you tweak your prompts for optimal results.

You can quickly evaluate the model in the playground through the UI. However, to invoke the deployed design 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 carry out inference utilizing a deployed 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 develop the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference 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) hub with FMs, integrated algorithms, and prebuilt ML solutions that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs 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 offers two convenient approaches: using the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you select the approach that finest suits your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to deploy DeepSeek-R1 utilizing 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 model browser shows available designs, with details like the supplier name and model abilities.

4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each design card shows key details, including:

- Model name
- Provider name
- Task category (for instance, wiki.vst.hs-furtwangen.de Text Generation).
Bedrock Ready badge (if suitable), indicating that this model can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design

5. Choose the design card to view the design details page.

The design details page includes the following details:

- The design name and company details.
Deploy button to deploy the model.
About and Notebooks tabs with detailed details

The About tab consists of important details, such as:

- Model description.
- License details.
- Technical specifications.
- Usage standards

Before you release the design, it's suggested to evaluate the model details and license terms to validate compatibility with your use case.

6. Choose Deploy to continue with deployment.

7. For Endpoint name, utilize the immediately created name or produce a customized one.
8. For example type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, get in the number of circumstances (default: 1).
Selecting suitable circumstances types and counts is crucial 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 enhanced for sustained traffic and low latency.
10. Review all setups for precision. For this design, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to deploy the model.

The release procedure can take several minutes to finish.

When deployment is total, your endpoint status will alter to InService. At this point, the model is ready to accept reasoning demands 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 total, you can conjure up the design using a SageMaker runtime client and incorporate it with your applications.

Deploy DeepSeek-R1 using the SageMaker Python SDK

To begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS authorizations 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 supplied in the Github here. You can clone the notebook and run from SageMaker Studio.

You can run extra requests against the predictor:

Implement guardrails and run reasoning with your SageMaker JumpStart predictor

Similar to Amazon Bedrock, you can also use 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 displayed in the following code:

Clean up

To prevent unwanted charges, complete the actions in this section to clean up your resources.

Delete the Amazon Bedrock Marketplace implementation

If you released the model using Amazon Bedrock Marketplace, total the following steps:

1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace implementations.
2. In the Managed deployments section, locate the endpoint you want to erase.
3. Select the endpoint, and on the Actions menu, select Delete.
4. Verify the endpoint details to make certain you're erasing the appropriate deployment: 1. Endpoint name.
2. Model name.
3. Endpoint status

Delete the SageMaker JumpStart predictor

The SageMaker JumpStart model you released will sustain costs 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 utilizing 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 designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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 construct ingenious options utilizing AWS services and sped up compute. Currently, he is focused on establishing strategies for fine-tuning and optimizing the reasoning performance of large language models. In his leisure time, Vivek takes pleasure in treking, enjoying films, and 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 a Specialist Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.

Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is enthusiastic about constructing services that assist consumers accelerate their AI journey and unlock company value.