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 versions ranging from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative AI ideas on AWS.

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

Overview of DeepSeek-R1

DeepSeek-R1 is a large language design (LLM) developed by DeepSeek AI that utilizes support finding out to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential identifying function is its reinforcement knowing (RL) step, which was utilized to fine-tune the model's responses beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately enhancing both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, indicating it's geared up to break down complex inquiries and reason through them in a detailed manner. This directed thinking process permits the model to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation design that can be incorporated into different workflows such as agents, rational reasoning and information analysis tasks.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion parameters, making it possible for efficient reasoning by routing questions to the most pertinent specialist "clusters." This approach permits the design to specialize in different problem domains while maintaining total effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking abilities 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, more efficient designs to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 design, using it as a teacher design.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and examine designs against key safety criteria. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, 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, select Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limit boost, create a limitation increase request and connect to your account team.

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 utilize Amazon Bedrock Guardrails. For instructions, see Establish consents to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, prevent harmful material, and evaluate models against crucial security requirements. You can execute safety procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and model reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.

The basic circulation involves the following actions: 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 model for reasoning. After receiving the model'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 showing the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas demonstrate inference using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:

1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a company and select the DeepSeek-R1 design.

The model detail page supplies essential details about the design's capabilities, rates structure, and application guidelines. You can find detailed usage directions, consisting of sample API calls and code snippets for combination. The design supports different text generation tasks, consisting of material production, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT reasoning abilities.
The page likewise includes deployment alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, choose Deploy.

You will be prompted to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
5. For Number of instances, enter a number of circumstances (in between 1-100).
6. For example type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can configure innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you may wish to evaluate these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to begin using the design.

When the release is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in play ground to access an interactive interface where you can explore various prompts and change design specifications like temperature level and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For example, <｜begin▁of▁sentence｜><｜User｜>content for inference<｜Assistant｜>.

This is an outstanding method to check out the model's reasoning and text generation capabilities before integrating it into your applications. The play ground provides instant feedback, helping you comprehend how the model responds to different inputs and letting you tweak your prompts for optimal outcomes.

You can rapidly 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 reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to perform reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create 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 customer, configures inference parameters, and sends out a demand to create text based on a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML solutions that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 hassle-free methods: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you select the technique that finest suits your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following steps 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 create a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.

The model browser shows available models, with details like the supplier name and model abilities.

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

- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if relevant), showing that this model can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the model

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

The model details page includes the following details:

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

The About tab includes crucial details, such as:

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

Before you deploy the model, it's advised to examine the model details and license terms to confirm compatibility with your usage case.

6. Choose Deploy to proceed with implementation.

7. For Endpoint name, use the immediately produced name or create a custom one.
8. For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the variety of instances (default: 1).
Selecting appropriate instance types and links.gtanet.com.br counts is essential for expense and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency.
10. Review all setups for accuracy. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to release the design.

The implementation procedure can take numerous minutes to finish.

When implementation is complete, your endpoint status will change to InService. At this moment, the design is prepared to accept inference demands through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is complete, you can conjure up the design utilizing a SageMaker runtime client and integrate it with your applications.

Deploy DeepSeek-R1 using the SageMaker Python SDK

To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for deploying the model is offered in the Github here. You can clone the notebook and range from SageMaker Studio.

You can run extra demands against the predictor:

Implement guardrails and run inference with your SageMaker JumpStart predictor

Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:

Tidy up

To avoid undesirable charges, complete the steps in this section to tidy 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 releases.
2. In the Managed releases section, locate the endpoint you wish to erase.
3. Select the endpoint, and on the Actions menu, choose Delete.
4. Verify the endpoint details to make certain you're erasing the appropriate release: 1. Endpoint name.
2. Model name.
3. 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 delete 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 wavedream.wiki 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 Getting begun 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 ingenious options utilizing AWS services and sped up compute. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning performance of large language designs. In his spare time, Vivek takes pleasure in treking, seeing movies, larsaluarna.se 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 Science and Bioinformatics.

Jonathan Evans is an Expert Solutions Architect dealing with 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 developing services that help customers accelerate their AI journey and unlock service worth.