Today, we are thrilled to reveal 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 model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative AI ideas on AWS.

In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that utilizes support learning to improve thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial differentiating feature is its reinforcement learning (RL) action, which was utilized to improve the model's reactions beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually improving both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, implying it's geared up to break down complex queries and factor through them in a detailed way. This directed reasoning process allows the model to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has captured the market's attention as a flexible text-generation model that can be incorporated into different workflows such as representatives, logical reasoning and information analysis jobs.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion criteria, making it possible for effective reasoning by routing inquiries to the most appropriate specialist "clusters." This method allows the design to specialize in different problem domains while maintaining general efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more effective architectures based on popular open models like Qwen (1.5 B, wiki.snooze-hotelsoftware.de 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more effective designs to imitate the behavior and bytes-the-dust.com reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as an instructor design.

You can release DeepSeek-R1 design 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, prevent harmful material, and examine designs against essential safety criteria. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint use. 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, produce a limit boost demand and connect to your account group.

Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For pipewiki.org guidelines, see Establish approvals to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, prevent hazardous 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 apply guardrails to examine user inputs and design actions deployed 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 involves the following actions: First, the system gets 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 design's output, another guardrail check is applied. If the output passes this final check, it's returned as the final result. However, if either the input or output is intervened 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 demonstrate reasoning utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides 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 actions:

1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane.
At the time of writing 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 provider and select the DeepSeek-R1 design.

The design detail page provides essential details about the model's abilities, pricing structure, and application guidelines. You can find detailed usage guidelines, consisting of sample API calls and code snippets for combination. The design supports various text generation tasks, including material creation, code generation, and question answering, utilizing its reinforcement learning optimization and CoT thinking abilities.
The page likewise includes deployment choices and licensing details to help you get started with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, select Deploy.

You will be prompted to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters).
5. For Number of circumstances, go into a number of circumstances (in between 1-100).
6. For example type, select your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, service function permissions, and encryption settings. For many use cases, the default settings will work well. However, for production releases, you might wish to evaluate these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start utilizing the model.

When the implementation is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in play area to access an interactive interface where you can experiment with various prompts and adjust model parameters like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For example, <｜begin▁of▁sentence｜><｜User｜>content for reasoning<｜Assistant｜>.

This is an outstanding way to explore the model's reasoning and text generation abilities before integrating it into your applications. The play ground provides immediate feedback, helping you comprehend how the model reacts to numerous inputs and letting you tweak your triggers for optimum results.

You can rapidly test the design 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 demonstrates how to perform inference using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop 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 produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends a demand to generate 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 options that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 convenient approaches: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you select the approach that best matches your requirements.

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 create a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.

The model web browser displays available designs, with details like the provider name and design capabilities.

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

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

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

The model details page includes the following details:

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

The About tab consists of important details, such as:

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

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

6. Choose Deploy to continue with deployment.

7. For Endpoint name, utilize the instantly generated name or develop a custom-made one.
8. For example type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the number of circumstances (default: 1).
Selecting suitable circumstances types and counts is crucial for expense and efficiency optimization. Monitor your release to change 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 design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to deploy the design.

The release procedure can take numerous minutes to finish.

When deployment is complete, your endpoint status will alter to InService. At this point, the model is ready to accept inference requests through the endpoint. You can keep track of the release progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is total, you can invoke the design utilizing a SageMaker runtime client and integrate it with your applications.

Deploy DeepSeek-R1 utilizing the SageMaker Python SDK

To get begun with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and yewiki.org make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that demonstrates 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 notebook and run from SageMaker Studio.

You can run additional demands against the predictor:

Implement guardrails and run inference with your SageMaker JumpStart predictor

Similar to Amazon Bedrock, you can likewise 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 shown in the following code:

Tidy up

To prevent undesirable charges, finish the steps in this area to clean up your resources.

Delete the Amazon Bedrock Marketplace release

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

1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases.
2. In the Managed implementations section, locate the endpoint you desire 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 correct release: 1. Endpoint name.
2. Model name.
3. Endpoint status

Delete the SageMaker JumpStart predictor

The SageMaker JumpStart design you released 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 checked out 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 models, 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 generative AI companies develop innovative services using AWS services and sped up compute. Currently, he is focused on developing techniques for fine-tuning and enhancing the inference efficiency of large language models. In his spare time, Vivek takes pleasure in hiking, viewing movies, and trying different foods.

Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area 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 team at AWS.

Banu Nagasundaram leads item, engineering, pediascape.science and garagesale.es strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about developing options that assist customers accelerate their AI journey and unlock service value.