Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen models 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, in addition to the distilled versions varying from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative AI concepts on AWS.
In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the designs also.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that utilizes support learning to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial identifying function is its support learning (RL) step, which was used to fine-tune the design's reactions beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, ultimately enhancing both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, suggesting it's geared up to break down complicated questions and factor through them in a detailed way. This assisted reasoning procedure permits the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation design that can be integrated into various workflows such as agents, logical reasoning and data analysis tasks.
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, making it possible for efficient inference by routing inquiries to the most relevant specialist "clusters." This method enables the design to concentrate on different problem domains while maintaining general performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design 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 procedure of training smaller, more effective designs to imitate the habits and reasoning patterns of the larger DeepSeek-R1 design, using it as an instructor design.
You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and evaluate designs against crucial safety requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to different use 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 design, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm you're utilizing 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 limit increase, engel-und-waisen.de produce a limit increase request and connect 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) approvals to utilize Amazon Bedrock Guardrails. For instructions, see Set up authorizations to utilize guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to introduce safeguards, prevent damaging material, and examine designs against essential security requirements. You can carry out safety measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and design reactions released 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 basic flow includes the following steps: First, the system receives 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 design 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 last result. 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 took place at the input or output phase. The examples showcased in the following sections demonstrate inference utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
1. On the Amazon Bedrock console, choose Model catalog 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 does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 model.
The model detail page supplies vital details about the model's capabilities, prices structure, and application guidelines. You can discover detailed use instructions, consisting of sample API calls and code snippets for combination. The model supports different text generation tasks, including content development, code generation, and concern answering, using its reinforcement discovering optimization and CoT thinking capabilities.
The page also consists of implementation choices and licensing details to help you begin with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, pick Deploy.
You will be prompted to set up the release 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 Number of circumstances, get in a number of instances (in between 1-100).
6. For Instance type, choose your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can configure sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service function consents, and encryption settings. For most use cases, the default settings will work well. However, for production deployments, you may want to examine these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to start utilizing the design.
When the implementation is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground.
8. Choose Open in playground to access an interactive user interface where you can explore various triggers and adjust design parameters like temperature and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For example, material for inference.
This is an outstanding way to explore the model's thinking and text generation abilities before integrating it into your applications. The play ground provides instant feedback, wavedream.wiki assisting you understand how the model reacts to different inputs and letting you tweak your prompts for ideal results.
You can quickly test the design in the play area through the UI. However, to invoke the released design 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 demonstrates how to carry out inference using a deployed DeepSeek-R1 model 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 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 client, configures reasoning parameters, and sends out a demand to generate text based on a user prompt.
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 few 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 provides two practical approaches: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to help you select the approach that finest suits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps to release DeepSeek-R1 using 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 .
The design web browser shows 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 instance, Text Generation).
Bedrock Ready badge (if appropriate), indicating that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design
5. Choose the design card to view the model details page.
The model details page consists of the following details:
- The model name and service provider details. Deploy button to release the design. About and Notebooks tabs with detailed details
The About tab includes crucial details, such as:
- Model description. - License details.
- Technical specifications.
- Usage standards
Before you release the design, it's suggested to examine the model details and license terms to confirm compatibility with your use case.
6. Choose Deploy to continue with implementation.
7. For Endpoint name, utilize the immediately generated name or develop a custom one.
- For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, go into the variety of instances (default: 1). Selecting suitable instance types and counts is essential for expense and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency.
- Review all configurations for accuracy. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to deploy the model.
The implementation process can take numerous minutes to complete.
When implementation is total, your endpoint status will alter to InService. At this moment, the model is prepared to accept inference requests through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the deployment is total, you can invoke the model using a SageMaker runtime customer and incorporate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To get going with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the required AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is offered in the Github here. You can clone the note pad and range 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:
Clean up
To avoid unwanted charges, finish the steps in this area to tidy up your resources.
Delete the Amazon Bedrock Marketplace release
If you deployed the model utilizing Amazon Bedrock Marketplace, total the following actions:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace implementations. - In the Managed deployments area, locate 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 erasing the correct 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 want 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 model 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 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 business develop innovative services using AWS services and sped up calculate. Currently, he is concentrated on developing methods for fine-tuning and optimizing the inference efficiency of big language models. In his downtime, Vivek enjoys hiking, mediawiki.hcah.in watching films, and trying different foods.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group 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 group at AWS.
Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about constructing services that help clients accelerate their AI journey and unlock company worth.