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 versions ranging from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative AI concepts on AWS.
In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the designs too.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language design (LLM) developed by DeepSeek AI that uses reinforcement discovering to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key distinguishing feature is its reinforcement knowing (RL) action, which was used to refine the design's reactions beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually improving both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, indicating it's geared up to break down complicated inquiries and reason through them in a detailed manner. This guided thinking procedure allows the model to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually captured the market's attention as a flexible text-generation design that can be integrated into various workflows such as agents, sensible reasoning and information analysis jobs.
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion parameters, enabling efficient inference by routing queries to the most pertinent expert "clusters." This technique enables the design to concentrate on different issue domains while maintaining general efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the reasoning 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 describes a procedure of training smaller, more effective designs to mimic the behavior and reasoning patterns of the larger DeepSeek-R1 design, using it as a teacher model.
You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and examine models against crucial safety requirements. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative AI applications.
Prerequisites
To release the DeepSeek-R1 design, you need access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're using 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 boost, produce a limit boost demand and connect 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 authorizations to utilize guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to present safeguards, prevent damaging material, and assess designs against key security criteria. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and design responses 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 create the guardrail, see the GitHub repo.
The basic flow involves the following steps: First, the system gets an input for the design. 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 last check, it's returned as the outcome. 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 took place at the input or output phase. The examples showcased in the following sections show inference using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace offers 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, choose Model brochure under Foundation models in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 model.
The design detail page offers important details about the model's abilities, pricing structure, and execution guidelines. You can discover detailed usage directions, including sample API calls and code snippets for combination. The model supports different text generation jobs, including material creation, code generation, and question answering, using its support discovering optimization and CoT thinking capabilities.
The page also consists of release alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, select Deploy.
You will be triggered to configure the release 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 Variety of circumstances, enter a number of instances (between 1-100).
6. For example type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can set up advanced security and facilities settings, including virtual private cloud (VPC) networking, service role permissions, and file encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you might desire to examine these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start using the model.
When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in play ground to access an interactive interface where you can explore various prompts and adjust design criteria like temperature level and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For instance, material for reasoning.
This is an outstanding method to check out the model's thinking and text generation abilities before incorporating it into your applications. The play ground offers instant feedback, assisting you understand how the model reacts to numerous inputs and letting you tweak your triggers for optimum outcomes.
You can quickly test the design in the play area through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
The following code example demonstrates how to carry out inference utilizing a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and 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 created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up reasoning 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 release with just 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 hassle-free techniques: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to assist you choose the technique that finest suits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps 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 produce a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
The design browser displays available models, with details like the provider name and model capabilities.
4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each model card reveals crucial details, consisting of:
- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if appropriate), indicating that this model can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to up the design
5. Choose the design card to see the model details page.
The model details page includes the following details:
- The design name and company details. Deploy button to release the design. About and Notebooks tabs with detailed details
The About tab consists of crucial details, such as:
- Model description. - License details.
- Technical specifications.
- Usage standards
Before you release the model, it's suggested to examine the design details and license terms to confirm compatibility with your usage case.
6. Choose Deploy to proceed with release.
7. For Endpoint name, utilize the immediately generated name or produce a customized one.
- For example type ¸ choose an instance type (default: ml.p5e.48 xlarge).
- For Initial instance count, go into the number of instances (default: 1). Selecting suitable instance types and counts is essential for expense and performance optimization. Monitor your implementation 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.
- Review all setups for precision. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to release the model.
The release procedure can take a number of minutes to finish.
When implementation is complete, your endpoint status will alter to InService. At this point, the design is ready to accept inference demands through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is total, you can conjure up the model utilizing a SageMaker runtime client and incorporate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for inference programmatically. The code for deploying 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 also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or setiathome.berkeley.edu the API, and execute it as revealed in the following code:
Tidy up
To avoid unwanted charges, complete the steps in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace deployment
If you deployed the model utilizing Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations. - In the Managed implementations section, find the endpoint you want to delete.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're deleting the right release: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model 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 deploy the DeepSeek-R1 design 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 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 build ingenious solutions utilizing AWS services and sped up compute. Currently, he is concentrated on developing strategies for fine-tuning and enhancing the reasoning efficiency of large language models. In his downtime, Vivek takes pleasure in hiking, enjoying motion pictures, and trying various cuisines.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group 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 group at AWS.
Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about constructing services that help clients accelerate their AI journey and unlock business value.