Today, we are delighted 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 release DeepSeek AI's first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and properly 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 similar actions to release the distilled versions of the designs too.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language design (LLM) developed by DeepSeek AI that utilizes support learning to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key identifying feature is its reinforcement knowing (RL) step, which was utilized to fine-tune the model's responses beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually boosting both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, meaning it's equipped to break down intricate queries and factor through them in a detailed manner. This guided thinking process allows the model to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured responses while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation model that can be integrated into different workflows such as representatives, rational thinking and data interpretation jobs.
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, enabling effective inference by routing inquiries to the most appropriate expert "clusters." This method permits the model to specialize in various issue domains while maintaining total efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the reasoning 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 process of training smaller sized, more effective designs to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher model.
You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and assess models against key security requirements. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative AI applications.
Prerequisites
To release the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and validate 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 releasing. To ask for a limit increase, produce a limitation boost demand and reach out to your account group.
Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For instructions, see Set up authorizations to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to present safeguards, avoid harmful content, and evaluate models against key security requirements. You can implement safety steps for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing 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 out to the model for inference. After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the outcome. 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 happened at the input or output stage. The examples showcased in the following areas show reasoning using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
1. On the Amazon Bedrock console, select 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 doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 model.
The design detail page supplies essential details about the model's capabilities, pricing structure, and execution standards. You can discover detailed usage guidelines, consisting of sample API calls and code snippets for combination. The model supports various text generation jobs, consisting of content creation, code generation, and question answering, using its support finding out optimization and CoT thinking abilities.
The page likewise includes deployment choices and licensing details to help you get going with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, choose Deploy.
You will be prompted to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of circumstances, get in a variety of instances (between 1-100).
6. For example type, choose your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up advanced security and infrastructure settings, including virtual personal cloud (VPC) networking, service function authorizations, and encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you may desire to review these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to begin using the design.
When the deployment is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in play ground to access an interactive interface where you can explore various triggers and adjust design criteria like temperature level and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For instance, material for reasoning.
This is an exceptional method to check out the design's thinking and text generation capabilities before incorporating it into your applications. The play ground offers instant feedback, assisting you comprehend how the model reacts to different inputs and letting you tweak your triggers for optimum outcomes.
You can rapidly check the design in the play ground through the UI. However, to invoke the deployed 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 carry out inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce 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 developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning criteria, and sends out a request to create text based upon a user prompt.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML services 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 information, and deploy them into production using either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart offers two convenient approaches: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you choose the method 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, pick Studio in the navigation pane.
2. First-time users will be triggered to produce a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The design browser displays available designs, with details like the supplier name and design abilities.
4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each model card reveals crucial details, consisting of:
- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if relevant), suggesting that this model can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
5. Choose the design card to view the design details page.
The design details page includes 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 includes important details, such as:
- Model description. - License details.
- Technical requirements.
- Usage standards
Before you release the design, it's recommended to evaluate the model details and license terms to verify compatibility with your usage case.
6. Choose Deploy to continue with .
7. For Endpoint name, utilize the automatically produced name or produce a customized one.
- For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, go into the variety of circumstances (default: 1). Selecting proper circumstances types and counts is essential for cost and performance optimization. Monitor your implementation to change 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 setups for accuracy. For this model, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to release the model.
The deployment procedure can take several minutes to complete.
When deployment is total, your endpoint status will alter to InService. At this point, the model is ready to accept inference requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the deployment is complete, you can conjure up the design using a SageMaker runtime customer and integrate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To start with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS consents 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 offered in the Github here. You can clone the notebook and range from SageMaker Studio.
You can run extra 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 create a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:
Tidy up
To prevent unwanted charges, finish the steps in this section to tidy up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you released the model utilizing Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases. - In the Managed deployments area, locate the endpoint you want to erase.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're deleting the right release: 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 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 begin. For more details, describe 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 build innovative solutions using AWS services and accelerated compute. Currently, he is concentrated on developing techniques for fine-tuning and enhancing the reasoning efficiency of big language designs. In his spare time, Vivek takes pleasure in treking, viewing movies, and trying different cuisines.
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 technology and Bioinformatics.
Jonathan Evans is a Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, bytes-the-dust.com SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about developing services that help consumers accelerate their AI journey and unlock business value.