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 deploy DeepSeek AI's first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly 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 release the distilled versions of the designs also.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that uses reinforcement discovering to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key identifying function is its support learning (RL) action, which was used to improve the design's actions beyond the standard pre-training and tweak procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually boosting both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, indicating it's equipped to break down complex questions and reason through them in a detailed manner. This directed thinking procedure enables the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually captured the market's attention as a flexible text-generation design that can be incorporated into various workflows such as representatives, rational reasoning and information analysis tasks.
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion criteria, enabling efficient inference by routing questions to the most pertinent expert "clusters." This method enables the design to concentrate on 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 utilize an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model 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 procedure of training smaller, more effective models to imitate the habits and reasoning patterns of the larger DeepSeek-R1 design, using it as a teacher model.
You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent harmful content, and examine models against essential safety requirements. 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 produce numerous guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative AI applications.
Prerequisites
To release the DeepSeek-R1 design, you require access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify 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 releasing. To request a limitation boost, develop a limitation increase demand 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) approvals to utilize Amazon Bedrock Guardrails. For directions, see Set up permissions to utilize guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to introduce safeguards, prevent harmful content, and examine models against crucial security requirements. You can carry out security steps for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and design reactions 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 create the guardrail, see the GitHub repo.
The general flow includes the following steps: wiki.myamens.com 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 to the design for inference. After receiving the design's output, another guardrail check is applied. If the output passes this last 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 happened 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 offers you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
1. On the Amazon Bedrock console, choose 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 doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a company and select the DeepSeek-R1 design.
The model detail page supplies vital details about the model's abilities, prices structure, and execution guidelines. You can find detailed usage instructions, including sample API calls and code snippets for combination. The design supports numerous text generation tasks, including material creation, code generation, and question answering, using its support learning optimization and CoT reasoning abilities.
The page likewise consists of release choices and licensing details to help you get going with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose 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, get in a number of instances (between 1-100).
6. For example type, pick your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up innovative security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service role permissions, and file encryption settings. For most use cases, the default settings will work well. However, for production releases, you may desire to examine these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to begin using the model.
When the implementation is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground.
8. Choose Open in playground to access an interactive interface where you can try out various prompts and change model criteria like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For instance, content for inference.
This is an excellent way to explore the model's thinking and text generation abilities before integrating it into your applications. The play ground supplies instant feedback, assisting you understand how the model reacts to numerous inputs and letting you fine-tune your prompts for optimum results.
You can rapidly test the model in the play ground through the UI. However, to invoke the programmatically with any Amazon Bedrock APIs, you require 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 released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create 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 customer, sets up reasoning criteria, and sends out a request to generate 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 services that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use 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: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you select the method that finest fits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:
1. On the SageMaker console, choose 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 navigation pane.
The model web browser shows available models, with details like the service provider name and model abilities.
4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each design card reveals crucial details, consisting of:
- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if appropriate), showing that this design can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the design
5. Choose the design card to see the model details page.
The design details page includes the following details:
- The design name and service provider details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
The About tab includes essential details, such as:
- Model description. - License details.
- Technical specifications.
- Usage standards
Before you release the design, it's suggested to review the design details and license terms to validate compatibility with your usage case.
6. Choose Deploy to proceed with implementation.
7. For Endpoint name, use the instantly generated name or create a custom-made one.
- For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, get in the number of circumstances (default: 1). Selecting proper circumstances types and counts is vital for expense and efficiency optimization. Monitor your implementation 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 setups for precision. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
- Choose Deploy to release the design.
The implementation procedure can take several minutes to complete.
When deployment is complete, your endpoint status will change to InService. At this point, the model is ready to accept inference requests through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the release is total, you can invoke the design utilizing a SageMaker runtime customer and integrate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To get started with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the required AWS approvals 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 requests against the predictor:
Implement guardrails and run reasoning 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 utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
Tidy up
To prevent unwanted charges, complete the steps in this section to tidy up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you released the model using Amazon Bedrock Marketplace, total the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations. - In the Managed deployments section, locate the endpoint you wish to erase.
- Select the endpoint, and on the Actions menu, pick 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 model 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 get begun. For more details, describe 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 helps emerging generative AI companies construct innovative services using AWS services and sped up calculate. Currently, he is focused on developing methods for fine-tuning and enhancing the inference efficiency of big language designs. In his spare time, Vivek delights in treking, viewing movies, and attempting different foods.
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 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 passionate about constructing services that help customers accelerate their AI journey and unlock service worth.