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 deploy DeepSeek AI's first-generation frontier design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly 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 steps to deploy the distilled versions of the models too.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that utilizes support learning to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential distinguishing function is its reinforcement knowing (RL) step, which was used to refine the model's reactions beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, eventually improving both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, indicating it's geared up to break down intricate questions and reason through them in a detailed way. This directed thinking process permits the model to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation model that can be incorporated into different workflows such as representatives, rational thinking and data analysis jobs.
DeepSeek-R1 uses 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 expert "clusters." This method enables the design to concentrate on various problem domains while maintaining general efficiency. DeepSeek-R1 requires at least 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 includes 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 on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more efficient designs to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor model.
You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and evaluate models against crucial security requirements. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to various use cases and apply 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 require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify you're using 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 deploying. To ask for a limitation increase, produce a limit increase demand and connect 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 use Amazon Bedrock Guardrails. For guidelines, see Set up permissions to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to introduce safeguards, prevent damaging material, and examine models against essential security requirements. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and model reactions released 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 produce the guardrail, see the GitHub repo.
The basic circulation includes the following steps: 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 design for reasoning. After receiving the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the outcome. 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 happened at the input or output stage. The examples showcased in the following sections demonstrate inference utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace offers 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, choose Model brochure under Foundation designs in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and select the DeepSeek-R1 model.
The design detail page supplies essential details about the model's capabilities, rates structure, and application standards. You can discover detailed usage instructions, including sample API calls and code bits for combination. The model supports various text generation jobs, including material development, code generation, and concern answering, utilizing its support discovering optimization and CoT thinking capabilities.
The page also includes deployment choices and licensing details to help you start with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, .
You will be prompted 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 instances, enter a number of instances (between 1-100).
6. For Instance type, pick 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 advanced security and infrastructure settings, setiathome.berkeley.edu including virtual private cloud (VPC) networking, service role authorizations, and encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you may wish to review these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to begin using the design.
When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground.
8. Choose Open in playground to access an interactive interface where you can explore various triggers and change design specifications like temperature level and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For example, material for reasoning.
This is an outstanding way to check out the model's reasoning and text generation abilities before integrating it into your applications. The playground offers instant feedback, helping you understand how the model reacts to different inputs and letting you tweak your prompts for optimum outcomes.
You can quickly evaluate the model in the playground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
The following code example shows how to perform inference utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends 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 options that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 convenient methods: using the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to help you pick the method that best suits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to release DeepSeek-R1 using 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 design internet browser displays available designs, with details like the supplier name and design abilities.
4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each design card shows key details, consisting of:
- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if suitable), indicating that this model can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the model
5. Choose the model card to see the model details page.
The model details page includes the following details:
- The design name and service provider details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
The About tab consists of essential details, such as:
- Model description. - License details.
- Technical specs.
- Usage guidelines
Before you deploy the model, it's suggested to evaluate the design details and license terms to validate compatibility with your use case.
6. Choose Deploy to proceed with implementation.
7. For Endpoint name, utilize the automatically produced name or produce a custom one.
- For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, enter the variety of instances (default: 1). Selecting appropriate circumstances types and counts is essential for cost and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency.
- Review all configurations for precision. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
- Choose Deploy to deploy the model.
The deployment process can take numerous minutes to finish.
When implementation is complete, your endpoint status will change to InService. At this moment, the model is all set to accept reasoning demands through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will show relevant 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 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS permissions 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 releasing the design is supplied in the Github here. You can clone the note pad and range 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 using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
Tidy up
To avoid undesirable charges, finish the steps in this section to tidy up your resources.
Delete the Amazon Bedrock Marketplace release
If you deployed the model utilizing Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations. - In the Managed releases section, locate the endpoint you wish to delete.
- Select the endpoint, and on the Actions menu, select Delete.
- Verify the endpoint details to make certain you're erasing the appropriate implementation: 1. Endpoint name.
- Model name.
- 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 checked out how you can access and release the DeepSeek-R1 design 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 going 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 innovative solutions using AWS services and sped up calculate. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the inference performance of big language models. In his leisure time, Vivek delights in hiking, seeing films, and trying various 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 Science and Bioinformatics.
Jonathan Evans is an Expert Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about developing services that assist customers accelerate their AI journey and unlock organization value.