Today, we are delighted to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and JumpStart. With this launch, you can now release DeepSeek AI's first-generation frontier design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion specifications to build, experiment, and properly scale your generative AI concepts on AWS.
In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled variations of the models as well.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that uses reinforcement finding out to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial differentiating function is its support learning (RL) step, which was utilized to refine the design's reactions beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, suggesting it's geared up to break down complex questions and factor through them in a detailed way. This assisted reasoning procedure 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 actions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation design that can be incorporated into various workflows such as representatives, logical reasoning and information interpretation jobs.
DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, allowing effective inference by routing questions to the most appropriate expert "clusters." This approach enables the model to specialize in various problem domains while maintaining total efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 design to more efficient architectures based upon 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 sized, more efficient models to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 model, 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 advise releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid damaging material, and assess models against crucial security criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To examine 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 use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To request a limitation increase, produce a limit boost request and reach out to your account team.
Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For instructions, see Set up permissions to utilize guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to introduce safeguards, avoid hazardous material, and examine designs against crucial security criteria. You can implement safety procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and model actions 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 create the guardrail, see the GitHub repo.
The general flow includes the following actions: 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 model 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 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 happened at the input or output stage. The examples showcased in the following areas show reasoning utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace provides 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, pick Model catalog under Foundation models in the navigation pane.
At the time of composing 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 company and choose the DeepSeek-R1 design.
The design detail page provides necessary details about the model's capabilities, pricing structure, and implementation standards. You can find detailed usage directions, including sample API calls and code bits for integration. The model supports various text generation tasks, including material development, code generation, and concern answering, utilizing its support finding out optimization and CoT reasoning capabilities.
The page also consists of implementation options and licensing details to help you begin with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, select Deploy.
You will be prompted to configure the implementation 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 instances (between 1-100).
6. For Instance type, pick your instance type. For ideal 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, consisting of virtual personal cloud (VPC) networking, service role permissions, and encryption settings. For many utilize cases, the default settings will work well. However, for production releases, you may want to review these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start using the design.
When the release is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in play ground to access an interactive user interface where you can try out different prompts and adjust model specifications like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For instance, material for inference.
This is an excellent way to explore the design's reasoning and text generation abilities before integrating it into your applications. The play area offers instant feedback, assisting you understand how the design reacts to numerous inputs and letting you fine-tune your prompts for optimum outcomes.
You can rapidly evaluate the model in the play area through the UI. However, to conjure up the released design 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 demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create 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 produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference parameters, and sends out a request to produce 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 solutions that you can deploy with simply a few 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 design through SageMaker JumpStart provides two convenient techniques: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you pick the approach that finest suits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:
1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be triggered to develop a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The model internet browser displays available designs, with details like the supplier name and model abilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each design card reveals essential details, including:
- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if relevant), suggesting that this model can be registered with Amazon Bedrock, allowing 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 consists of the following details:
- The design name and provider details. Deploy button to release the design. About and archmageriseswiki.com Notebooks tabs with detailed details
The About tab consists of important details, such as:
- Model description. - License details.
- Technical specifications.
- Usage guidelines
Before you deploy the model, it's recommended to review the model details and license terms to confirm compatibility with your usage case.
6. Choose Deploy to continue with release.
7. For Endpoint name, use the automatically produced name or create a customized one.
- For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, get in the number of instances (default: 1). Selecting proper instance types and counts is important 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 precision. For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to deploy the design.
The deployment procedure can take a number of minutes to complete.
When deployment is total, your endpoint status will change to InService. At this moment, the design is all set to accept inference demands through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the release is complete, you can invoke the model using a SageMaker runtime customer and incorporate 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 need to set up the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is offered in the Github here. You can clone the note pad and run 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 implement it as shown in the following code:
Clean up
To avoid undesirable charges, complete the steps in this area to clean up your resources.
Delete the Amazon Bedrock Marketplace deployment
If you released the model using Amazon Bedrock Marketplace, complete the following actions:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace deployments. - In the Managed releases section, find 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 deleting the appropriate deployment: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model you released will sustain costs 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 explored 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 begin. 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 begun 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 build ingenious options utilizing AWS services and accelerated calculate. Currently, he is focused on developing strategies for fine-tuning and enhancing the reasoning performance of big language designs. In his spare time, Vivek takes pleasure in treking, seeing films, and attempting various 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 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 tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is enthusiastic about constructing solutions that help customers accelerate their AI journey and unlock company worth.