Today, we are thrilled 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, surgiteams.com DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative AI concepts on AWS.
In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the designs as well.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that uses support discovering to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying function is its support knowing (RL) action, which was used to improve the beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adjust more successfully to user feedback and goals, eventually enhancing both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, suggesting it's equipped to break down complex inquiries and factor through them in a detailed manner. This directed thinking procedure allows the model to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation design that can be integrated into different workflows such as agents, rational reasoning and information interpretation tasks.
DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion specifications, allowing efficient inference by routing questions to the most appropriate expert "clusters." This technique enables the design to focus on various issue domains while maintaining general performance. DeepSeek-R1 requires 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 design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more efficient models to mimic the behavior and thinking patterns of the larger DeepSeek-R1 design, utilizing 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 place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and examine models against essential security 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 produce numerous guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across 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, select Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limit boost, create a limitation increase request and reach out to your account group.
Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For guidelines, see Establish approvals to use guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to introduce safeguards, avoid harmful material, and assess designs against key safety requirements. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and model 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 produce the guardrail, see the GitHub repo.
The basic flow includes the following actions: First, the system gets 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 model for inference. After receiving the model'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 stage. The examples showcased in the following sections 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 models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
1. On the Amazon Bedrock console, choose Model catalog under Foundation models in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.
The design detail page provides important details about the design's abilities, prices structure, and application standards. You can find detailed use instructions, consisting of sample API calls and code snippets for combination. The model supports numerous text generation tasks, consisting of material production, code generation, and concern answering, using its reinforcement discovering optimization and CoT thinking capabilities.
The page also consists of implementation alternatives and licensing details to help you start with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy.
You will be prompted to configure the deployment 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 Number of circumstances, go into a variety of circumstances (between 1-100).
6. For example type, pick your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure advanced security and infrastructure settings, including virtual personal cloud (VPC) networking, service function consents, and encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you might wish 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 deployment is total, you can check 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 various triggers and change model criteria like temperature and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For example, content for reasoning.
This is an exceptional method to check out the model's reasoning and text generation abilities before incorporating it into your applications. The playground provides instant feedback, assisting you understand how the model reacts to different inputs and letting you tweak your triggers for optimum outcomes.
You can quickly test the model in the playground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
The following code example demonstrates how to perform inference using a released DeepSeek-R1 model through Amazon Bedrock using 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 actually produced the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up reasoning parameters, and sends out a request to generate 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 just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 practical approaches: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to help you select the technique that best 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 develop a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The design browser displays available models, with details like the company name and model capabilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each design card shows crucial details, including:
- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if applicable), indicating that this model can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model
5. Choose the model card to see the model details page.
The design details page consists of the following details:
- The model name and supplier details. Deploy button to release the model. About and Notebooks tabs with detailed details
The About tab includes crucial details, such as:
- Model description. - License details.
- Technical requirements.
- Usage guidelines
Before you release the model, it's advised to review the model details and license terms to verify compatibility with your use case.
6. Choose Deploy to proceed with implementation.
7. For Endpoint name, use the automatically created name or produce a customized one.
- For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, enter the variety of circumstances (default: 1). Selecting proper circumstances types and counts is crucial for cost and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency.
- Review all configurations for precision. For this model, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
- Choose Deploy to release the model.
The implementation process can take numerous minutes to finish.
When release is complete, your endpoint status will alter to InService. At this moment, the design is ready to accept reasoning requests through the endpoint. You can monitor the implementation development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is total, you can invoke the model utilizing a SageMaker runtime client 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 install the SageMaker Python SDK and make certain you have the needed 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 offered in the Github here. You can clone the note pad and run from SageMaker Studio.
You can run additional requests 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 develop a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
Tidy up
To prevent undesirable charges, finish the steps in this section to tidy up your resources.
Delete the Amazon Bedrock Marketplace deployment
If you released the design utilizing Amazon Bedrock Marketplace, total the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments. - In the Managed deployments area, find the endpoint you desire to erase.
- Select the endpoint, and on the Actions menu, pick Delete.
- Verify the endpoint details to make certain you're erasing the correct deployment: 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 erase the endpoint if you desire to stop sustaining charges. For more details, see Delete Endpoints and Resources.
Conclusion
In this post, we checked out 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 get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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 companies develop ingenious services utilizing AWS services and sped up compute. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the reasoning efficiency of big language models. In his downtime, Vivek takes pleasure in treking, enjoying films, and attempting various foods.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team 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 dealing with generative AI with the Third-Party Model Science team at AWS.
Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about developing services that help customers accelerate their AI journey and unlock organization value.