Today, we are thrilled to reveal 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, along with the distilled versions varying from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative AI concepts on AWS.
In this post, we show how to get going 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 big language model (LLM) developed by DeepSeek AI that uses reinforcement learning to boost thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key differentiating feature is its reinforcement learning (RL) step, which was utilized to refine the model's actions beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, indicating it's geared up to break down intricate queries and factor through them in a detailed way. This directed thinking procedure enables the design to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation design that can be integrated into various workflows such as representatives, rational thinking and information interpretation tasks.
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion specifications, enabling effective inference by routing questions to the most pertinent expert "clusters." This approach permits the design to focus on different issue domains while maintaining overall performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more effective architectures based on 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 habits and thinking patterns of the bigger DeepSeek-R1 model, 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 model, we recommend deploying this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging content, and assess models against essential safety criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative AI applications.
Prerequisites
To release 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, select 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 deploying. To request a limitation increase, develop a limit boost request and reach out to your .
Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For directions, see Set up permissions to use guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to present safeguards, avoid damaging content, and assess designs against essential security criteria. You can implement safety measures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and model reactions released 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 create the guardrail, see the GitHub repo.
The basic flow involves 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 inference. After getting the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the final 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 stage. The examples showcased in the following areas demonstrate reasoning using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation models (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 brochure under Foundation designs in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to invoke the design. 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 model detail page provides essential details about the design's abilities, prices structure, and implementation guidelines. You can discover detailed usage guidelines, consisting of sample API calls and code snippets for combination. The design supports numerous text generation tasks, consisting of material development, code generation, and question answering, using its support learning optimization and CoT thinking capabilities.
The page also includes implementation choices and licensing details to help you get going with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, pick Deploy.
You will be prompted to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, enter 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, pick your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up sophisticated security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role consents, and encryption settings. For a lot of use cases, the default settings will work well. However, for production implementations, you might want to review 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 complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in play area to access an interactive interface where you can try out different prompts and change design parameters like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal results. For example, material for reasoning.
This is an outstanding method to check out the design's thinking and text generation capabilities before integrating it into your applications. The play ground supplies instant feedback, assisting you understand how the design reacts to different inputs and letting you tweak your triggers for ideal results.
You can quickly check the design in the play ground 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 utilizing guardrails with the released DeepSeek-R1 endpoint
The following code example shows how to perform reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop 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 created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends out a request to produce text based upon a user timely.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in 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 use case, with your data, and deploy them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 hassle-free methods: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you choose the technique that finest suits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to deploy DeepSeek-R1 using SageMaker JumpStart:
1. On the SageMaker console, choose Studio in the navigation pane.
2. First-time users will be prompted to develop a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
The model browser shows available designs, with details like the service provider name and model capabilities.
4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each design card reveals key details, including:
- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if applicable), suggesting that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the design
5. Choose the model 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 includes crucial details, such as:
- Model description. - License details.
- Technical specifications.
- Usage guidelines
Before you release the model, it's advised to examine the model details and license terms to verify compatibility with your usage case.
6. Choose Deploy to proceed with release.
7. For Endpoint name, use the automatically created name or develop a custom one.
- For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, get in the variety of instances (default: 1). Selecting suitable instance types and counts is essential for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency.
- Review all setups for precision. For this model, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to release the model.
The deployment process can take numerous minutes to complete.
When implementation is complete, your endpoint status will change to InService. At this moment, the design is all set to accept reasoning demands through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is total, you can invoke the model using a SageMaker runtime client and higgledy-piggledy.xyz 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 require to install the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and use 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 demands against the predictor:
Implement guardrails and run inference with your SageMaker JumpStart predictor
Similar to Amazon Bedrock, you can also 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 displayed in the following code:
Tidy up
To avoid undesirable charges, complete the actions in this section to tidy up your resources.
Delete the Amazon Bedrock Marketplace release
If you released the design using Amazon Bedrock Marketplace, total the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace implementations. - In the Managed deployments section, find the endpoint you desire to delete.
- Select the endpoint, and on the Actions menu, select Delete.
- Verify the endpoint details to make certain you're erasing the proper implementation: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart design you deployed will sustain costs if you leave it running. Use the following code to delete the endpoint if you wish 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 model utilizing 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 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 companies construct ingenious options utilizing AWS services and sped up calculate. Currently, he is concentrated on establishing strategies for fine-tuning and enhancing the reasoning efficiency of big language designs. In his spare time, Vivek delights in treking, enjoying motion pictures, and attempting different 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 group at AWS.
Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about building solutions that assist customers accelerate their AI journey and unlock service value.