Today, we are excited to announce 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, together with the distilled variations ranging from 1.5 to 70 billion criteria to construct, experiment, and properly scale your generative AI concepts on AWS.
In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the models too.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language design (LLM) established by DeepSeek AI that uses reinforcement learning to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying function is its reinforcement learning (RL) action, which was used to fine-tune the design's actions beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, eventually improving both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, meaning it's equipped to break down complicated queries and reason through them in a detailed way. This guided thinking procedure permits the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has captured the market's attention as a flexible text-generation design that can be integrated into numerous workflows such as agents, rational reasoning and data analysis tasks.
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, making it possible for effective inference by routing questions to the most relevant expert "clusters." This technique enables the design to specialize in various issue domains while maintaining overall performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance 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 design 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 effective models to mimic the behavior 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 model, we recommend deploying this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and examine models against key security criteria. 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 produce numerous guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To check if you have quotas for P5e, bytes-the-dust.com open the Service Quotas console and archmageriseswiki.com under AWS Services, choose Amazon SageMaker, and confirm you're utilizing 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 deploying. To request a limitation boost, produce a limitation 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 right AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For instructions, see Set up consents to utilize guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to introduce safeguards, prevent hazardous material, and evaluate designs against key safety requirements. You can implement security procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and model actions released 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 basic circulation involves 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 inference. After receiving the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas show reasoning utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace gives 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 actions:
1. On the Amazon Bedrock console, pick Model brochure under Foundation models in the navigation pane.
At the time of writing this post, you can utilize 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 supplier and pick the DeepSeek-R1 model.
The model detail page supplies essential details about the design's abilities, rates structure, and application standards. You can discover detailed usage guidelines, consisting of sample API calls and code snippets for combination. The model supports various text generation tasks, including material production, code generation, and question answering, using its reinforcement discovering optimization and CoT thinking abilities.
The page likewise includes implementation options and licensing details to assist you start with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, pick Deploy.
You will be triggered to set up the release 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 optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up advanced security and facilities settings, including virtual personal cloud (VPC) networking, service role approvals, and encryption settings. For most utilize cases, the default settings will work well. However, for production releases, you might want to examine these settings to align with your company's security and compliance requirements.
7. Choose Deploy to begin using the model.
When the deployment is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in play area to access an interactive user interface where you can explore different triggers and adjust model parameters like temperature level and maximum 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 way to check out the design's reasoning and text generation abilities before integrating it into your applications. The play area offers instant feedback, helping you understand how the model reacts to numerous inputs and letting you fine-tune your prompts for ideal outcomes.
You can rapidly evaluate the design in the playground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run inference using guardrails with the deployed DeepSeek-R1 endpoint
The following code example demonstrates how to carry out inference utilizing a DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends a request to generate text based on a user prompt.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in 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 release them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 hassle-free techniques: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to help you choose the approach that finest suits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:
1. On the SageMaker console, choose Studio in the navigation pane.
2. First-time users will be triggered to produce a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
The design browser shows available models, with details like the supplier name and design abilities.
4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each model card shows crucial details, consisting of:
- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if applicable), suggesting that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model
5. Choose the model card to view the design details page.
The model details page includes the following details:
- The model name and supplier 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 design, it's recommended to review the design details and license terms to validate compatibility with your usage case.
6. Choose Deploy to continue with implementation.
7. For Endpoint name, utilize the instantly created name or produce a custom-made one.
- For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, get in the number of instances (default: 1). Selecting appropriate circumstances types and counts is important for cost and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency.
- Review all configurations for precision. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
- Choose Deploy to deploy the design.
The release procedure can take a number of minutes to complete.
When release is complete, your endpoint status will change to InService. At this point, the model is prepared to accept reasoning requests through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is complete, you can invoke the model using 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 essential AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning 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 additional demands against the predictor:
Implement guardrails and run reasoning with your SageMaker JumpStart predictor
Similar to Amazon Bedrock, you can likewise use 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 displayed in the following code:
Clean up
To avoid unwanted charges, finish the steps in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace release
If you deployed the design utilizing Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases. - In the Managed deployments area, locate the endpoint you wish to delete.
- Select the endpoint, and on the Actions menu, pick Delete.
- Verify the endpoint details to make certain you're deleting the correct implementation: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart design you released 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 checked out 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 get started. 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 helps emerging generative AI business build ingenious services using AWS services and sped up compute. Currently, he is focused on developing strategies for fine-tuning and enhancing the reasoning efficiency of big language designs. In his complimentary time, Vivek delights in hiking, enjoying films, and attempting 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 Science 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 product, engineering, and strategic collaborations for wavedream.wiki Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about constructing services that assist clients accelerate their AI journey and unlock company worth.