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

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

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that uses reinforcement learning to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial differentiating feature is its support learning (RL) action, which was used to improve the model's responses beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, pipewiki.org eventually boosting both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's geared up to break down complex questions and reason through them in a detailed manner. This guided reasoning procedure allows the design to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually captured the market's attention as a flexible text-generation model that can be integrated into various workflows such as representatives, sensible reasoning and information analysis jobs.

DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion specifications, making it possible for effective reasoning by routing queries to the most pertinent professional "clusters." This method allows the model to focus on different problem domains while maintaining general performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances 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 model to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more effective designs to simulate the habits and reasoning patterns of the larger DeepSeek-R1 model, using it as an instructor design.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and evaluate designs against essential security criteria. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To deploy 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, choose 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 instance in the AWS Region you are releasing. To ask for a limitation increase, produce a limit boost demand and reach out to your account team.

Because you will be deploying this model 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 instructions, see Set up permissions to utilize guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to introduce safeguards, avoid hazardous content, and examine designs against essential security requirements. You can execute safety measures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.

The basic flow involves the following actions: 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 out to the model for reasoning. 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 intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections 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 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, pick Model catalog under Foundation designs in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 design.

The design detail page supplies important details about the model's abilities, pricing structure, and implementation standards. You can discover detailed use directions, including sample API calls and code snippets for integration. The model supports different text generation tasks, consisting of content production, code generation, and question answering, using its support discovering optimization and CoT thinking abilities.
The page likewise consists of implementation options and licensing details to assist you begin with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, pick Deploy.

You will be triggered to configure the release details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of instances, enter a number of instances (in between 1-100).
6. For Instance type, choose your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service role permissions, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you may wish to review these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to begin using the design.

When the deployment is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in playground to access an interactive user interface where you can explore various triggers and change model parameters like temperature and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For instance, <｜begin▁of▁sentence｜><｜User｜>content for reasoning<｜Assistant｜>.

This is an outstanding way to explore the design's thinking and text generation capabilities before integrating it into your applications. The play ground offers immediate feedback, assisting you understand how the model reacts to various inputs and letting you tweak your prompts for optimal outcomes.

You can quickly check the design in the play area 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 shows how to carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and wiki.dulovic.tech ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, and sends 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 services that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and deploy them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient approaches: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you select the method that best matches 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, select Studio in the navigation pane.
2. First-time users will be prompted to create a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.

The model internet browser displays available designs, with details like the supplier name and design abilities.

4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each design card shows essential details, consisting of:

- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if relevant), suggesting that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the model

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

The model details page consists of the following details:

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

The About tab includes essential details, such as:

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

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

6. Choose Deploy to continue with implementation.

7. For Endpoint name, use the instantly generated name or create a customized one.
8. For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the number of circumstances (default: 1).
Selecting suitable instance types and counts is vital for expense and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency.
10. 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.
11. Choose Deploy to deploy the design.

The release process can take several minutes to finish.

When release is complete, your endpoint status will alter to InService. At this moment, the model is prepared to accept reasoning requests through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is complete, you can conjure up the design using a SageMaker runtime client and integrate it with your applications.

Deploy DeepSeek-R1 using the SageMaker Python SDK

To start 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 consents and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is offered in the Github here. You can clone the notebook and range from SageMaker Studio.

You can run extra requests 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 create a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:

Tidy up

To avoid unwanted charges, complete the steps in this area to clean up your resources.

Delete the Amazon Bedrock Marketplace implementation

If you released the design using Amazon Bedrock Marketplace, complete the following steps:

1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases.
2. In the Managed releases section, find the endpoint you want to delete.
3. Select the endpoint, and on the Actions menu, select Delete.
4. Verify the endpoint details to make certain you're erasing the proper 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 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 release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started with Amazon SageMaker JumpStart.

About the Authors

Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative AI companies develop ingenious options utilizing AWS services and accelerated compute. Currently, he is focused on establishing techniques for fine-tuning and optimizing the inference performance of big language designs. In his free time, gratisafhalen.be Vivek enjoys treking, seeing motion pictures, and attempting various cuisines.

Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group 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 a Specialist Solutions Architect working on generative AI with the Third-Party Model Science group at AWS.

Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about constructing solutions that help consumers accelerate their AI journey and unlock organization value.