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 model, DeepSeek-R1, together 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 show how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the models also.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that uses reinforcement learning to improve thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial identifying function is its reinforcement knowing (RL) step, which was used to refine the design's responses beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually improving both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, suggesting it's equipped to break down intricate questions and factor through them in a detailed way. This directed reasoning procedure enables the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation design that can be integrated into different workflows such as agents, logical reasoning and information analysis jobs.
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient reasoning by routing inquiries to the most appropriate expert "clusters." This approach permits the design to specialize in different issue domains while maintaining general efficiency. 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 model. 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 designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective models to mimic the behavior and thinking patterns of the larger DeepSeek-R1 design, using it as an instructor model.
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 location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and assess models against essential security criteria. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 design, you require access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're using 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 releasing. To request a limitation increase, produce a limit increase demand and reach out to your account team.
Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Establish approvals to utilize guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to introduce safeguards, avoid damaging content, and assess models against key safety requirements. You can implement precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and model reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using 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 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 to the design for reasoning. After getting the design'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 occurred at the input or output stage. The examples showcased in the following areas 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 designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
1. On the Amazon Bedrock console, select Model brochure under Foundation designs in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a company and select the DeepSeek-R1 design.
The model detail page provides important details about the model's abilities, prices structure, and execution guidelines. You can discover detailed usage directions, consisting of sample API calls and code snippets for integration. The model supports numerous text generation tasks, consisting of material creation, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT thinking capabilities.
The page also 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 prompted to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of circumstances, get in a variety of instances (in between 1-100).
6. For example type, pick your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure innovative security and facilities settings, consisting of virtual private cloud (VPC) networking, service function approvals, and file encryption settings. For wiki.dulovic.tech many use cases, the default settings will work well. However, for production releases, you might want to evaluate these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the model.
When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground.
8. Choose Open in play ground to access an interactive interface where you can experiment with various triggers and adjust design specifications like temperature level and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For instance, content for inference.
This is an outstanding method to check out the model's thinking and text generation abilities before integrating it into your applications. The play ground offers instant feedback, assisting you understand how the model reacts to various inputs and letting you fine-tune your prompts for optimal results.
You can rapidly test the model in the play ground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
The following code example shows how to perform inference using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, configures inference specifications, and sends a request to produce text based upon a user prompt.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML solutions that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained to your usage case, with your data, and deploy them into production using either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 practical techniques: using the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both approaches to help you choose the approach that finest fits 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, 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 web browser displays available designs, with details like the company name and model abilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each design card reveals key details, consisting of:
- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if relevant), suggesting that this model can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the model
5. Choose the design card to view the design details page.
The model details page consists of the following details:
- The model name and supplier details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
The About tab consists of important details, such as:
- Model description. - License details.
- Technical specs.
- Usage guidelines
Before you release the model, it's suggested to review the design details and license terms to verify compatibility with your use case.
6. Choose Deploy to proceed with implementation.
7. For Endpoint name, utilize the immediately produced name or create a custom-made one.
- For example type ¸ choose an instance type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, enter the variety of instances (default: 1). Selecting suitable circumstances types and counts is vital for cost and performance optimization. Monitor your implementation 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.
- Review all setups for accuracy. For this model, we highly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to deploy the design.
The release process can take numerous minutes to finish.
When implementation is complete, your endpoint status will alter to InService. At this moment, the design is all set to accept inference demands through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the deployment is total, you can conjure up the design utilizing a SageMaker runtime customer 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 make certain you have the necessary AWS authorizations 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 deploying the model is supplied in the Github here. You can clone the note pad and run from SageMaker Studio.
You can run extra requests 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 produce a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
Clean up
To prevent undesirable charges, finish the actions in this area to tidy up your resources.
Delete the Amazon Bedrock Marketplace release
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, pick Marketplace implementations. - In the Managed deployments section, locate the endpoint you wish to erase.
- Select the endpoint, and on the Actions menu, select Delete.
- Verify the endpoint details to make certain you're deleting the appropriate implementation: 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 delete the endpoint if you desire 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 using 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 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 business build innovative options utilizing AWS services and sped up compute. Currently, he is focused on establishing strategies for fine-tuning and optimizing the inference efficiency of big language models. In his downtime, Vivek takes pleasure in hiking, watching motion pictures, and trying various cuisines.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
Jonathan Evans is a Professional Solutions Architect working on generative AI with the Third-Party Model Science team 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 developing services that help consumers accelerate their AI journey and unlock business worth.