Today, we are excited 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 model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative AI concepts on AWS.
In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled versions of the designs too.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language design (LLM) established by DeepSeek AI that utilizes reinforcement finding out to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential identifying feature is its reinforcement learning (RL) step, which was utilized to fine-tune the model's responses beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually improving both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, meaning it's equipped to break down intricate questions and factor through them in a detailed way. This guided reasoning process allows the design to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation design that can be integrated into various workflows such as agents, sensible reasoning and data analysis tasks.
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion parameters, enabling efficient reasoning by routing questions to the most pertinent specialist "clusters." This approach enables 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 instance to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 model to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient designs to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher design.
You can release DeepSeek-R1 design 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 site, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and examine designs against crucial safety criteria. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 model, 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 utilizing 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, develop a limitation increase request 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 content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to introduce safeguards, avoid damaging material, setiathome.berkeley.edu and assess designs against crucial security requirements. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
The general circulation includes the following actions: 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 out to the model for inference. After receiving 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 took place at the input or output phase. The examples showcased in the following sections demonstrate inference using 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 steps:
1. On the Amazon Bedrock console, select 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 doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a company and pick the DeepSeek-R1 model.
The design detail page offers vital details about the design's capabilities, prices structure, and execution guidelines. You can find detailed use directions, including sample API calls and code snippets for integration. The model supports different text generation jobs, consisting of content creation, code generation, and question answering, utilizing its support finding out optimization and CoT thinking abilities.
The page likewise includes deployment options and licensing details to assist you start with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, select Deploy.
You will be prompted to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of instances, enter a variety of instances (in between 1-100).
6. For Instance type, select your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure advanced security and facilities settings, including virtual personal cloud (VPC) networking, service function consents, and file encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you may want to evaluate these settings to align with your company's security and compliance requirements.
7. Choose Deploy to begin using the design.
When the deployment is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in play area to access an interactive user interface where you can explore different triggers and change design criteria like temperature level and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal results. For instance, content for reasoning.
This is an exceptional method to check out the model's reasoning and text generation abilities before incorporating it into your applications. The playground offers immediate feedback, helping you understand how the model reacts to different inputs and letting you tweak your prompts for optimum results.
You can rapidly test the design in the playground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
The following code example shows how to carry out inference utilizing a deployed 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 create the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends a demand to produce 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 options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and deploy them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 hassle-free techniques: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you pick the method that finest suits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:
1. On the SageMaker console, choose Studio in the navigation pane.
2. First-time users will be triggered to develop a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
The model browser displays available models, with details like the provider name and model capabilities.
4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each design card shows essential details, consisting of:
- Model name
- Provider name
- Task category (for instance, 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 design
5. Choose the design card to view the model details page.
The design details page includes the following details:
- The model name and supplier details. Deploy button to release the model. About and Notebooks tabs with detailed details
The About tab includes crucial details, such as:
- Model description. - License details. - Technical requirements.
- Usage guidelines
Before you deploy the design, it's advised to evaluate the and license terms to verify compatibility with your usage case.
6. Choose Deploy to continue with release.
7. For Endpoint name, use the immediately produced name or develop a custom one.
- For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, get in the variety of circumstances (default: 1). Selecting proper instance types and counts is vital for expense and efficiency optimization. Monitor your release 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 highly suggest 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 deployment is complete, your endpoint status will change to InService. At this point, the model is prepared to accept reasoning demands through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is complete, you can conjure up the model using a SageMaker runtime customer and integrate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the necessary AWS permissions and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied in the Github here. You can clone the notebook 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
Clean up
To avoid unwanted charges, complete the actions in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you released the model utilizing Amazon Bedrock Marketplace, total the following actions:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace releases. - In the Managed implementations section, find the endpoint you want to erase.
- Select the endpoint, and on the Actions menu, pick Delete.
- Verify the endpoint details to make certain you're deleting the proper release: 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 want 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 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 models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.
About the Authors
Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI business develop ingenious solutions using AWS services and sped up compute. Currently, he is focused on developing techniques for fine-tuning and optimizing the reasoning performance of big language models. In his leisure time, Vivek enjoys treking, viewing films, 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 technology 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 item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about developing services that help customers accelerate their AI journey and unlock company worth.