Today, we are delighted 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 deploy DeepSeek AI's first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative AI concepts on AWS.
In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled versions of the models too.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language design (LLM) established by DeepSeek AI that uses support learning to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential identifying function is its support knowing (RL) step, which was used to refine the design's actions beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, suggesting it's geared up to break down intricate queries and reason through them in a detailed way. This guided reasoning process allows the model to produce more precise, transparent, and detailed responses. This design fine-tuning with CoT capabilities, aiming to create structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has captured the market's attention as a flexible text-generation model that can be incorporated into different workflows such as agents, sensible thinking and information interpretation tasks.
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion specifications, enabling efficient inference by routing questions to the most appropriate expert "clusters." This method permits the design to concentrate on different issue domains while maintaining general efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design to more efficient architectures based on 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, more efficient designs to simulate the behavior and thinking patterns of the larger DeepSeek-R1 design, using it as a teacher model.
You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and examine models against crucial security requirements. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative AI applications.
Prerequisites
To deploy 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, pick Amazon SageMaker, and validate 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 ask for a limitation increase, produce a limitation increase request and connect to your account team.
Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For directions, see Establish approvals to use guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to present safeguards, avoid harmful material, and assess models against key safety criteria. You can carry out security steps for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
The general 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 design for reasoning. 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 stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following sections show reasoning using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, setiathome.berkeley.edu complete the following actions:
1. On the Amazon Bedrock console, pick Model brochure under Foundation models in the navigation pane.
At the time of writing this post, you can utilize the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 design.
The design detail page offers necessary details about the model's abilities, prices structure, and implementation guidelines. You can find detailed usage directions, including sample API calls and code bits for combination. The design supports different text generation tasks, consisting of material production, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT thinking abilities.
The page also consists of deployment alternatives and licensing details to assist you begin 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, go into an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of instances, enter a number of instances (between 1-100).
6. For example type, select your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can configure sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and encryption settings. For most utilize cases, the default settings will work well. However, for production releases, you might want to evaluate 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 test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in playground to access an interactive interface where you can explore different prompts and change model specifications like temperature and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For example, content for reasoning.
This is an excellent way to explore the model's reasoning and text generation capabilities before incorporating it into your applications. The play area supplies immediate feedback, assisting you comprehend how the model reacts to different inputs and letting you fine-tune your triggers for optimum outcomes.
You can quickly evaluate the model in the play ground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
The following code example demonstrates how to perform reasoning utilizing a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually developed the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends out a request to generate text based upon a user prompt.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML solutions that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production using either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 convenient methods: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to help you choose the method that finest fits your requirements.
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 design internet browser displays available models, with details like the company name and model capabilities.
4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each model card reveals crucial details, including:
- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if suitable), indicating 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 see the design details page.
The model details page includes the following details:
- The model name and service provider details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
The About tab consists of crucial details, such as:
- Model description. - License details.
- Technical specs.
- Usage guidelines
Before you deploy the model, it's recommended to evaluate the design details and license terms to confirm 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-made one.
- For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, enter the variety of instances (default: 1). Selecting appropriate circumstances types and counts is important for cost and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is picked 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 deploy the design.
The implementation process can take a number of minutes to finish.
When deployment is complete, your endpoint status will alter to InService. At this moment, the model is all set to accept inference requests through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation is total, you can conjure up the model utilizing a SageMaker runtime customer and incorporate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS consents and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is provided in the Github here. You can clone the notebook and run 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 develop a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
Clean up
To avoid undesirable charges, finish the steps in this area to clean up your resources.
Delete the Amazon Bedrock Marketplace deployment
If you deployed the design using Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace implementations. - In the Managed implementations area, locate the endpoint you wish to delete.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're deleting the correct release: 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 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 get started. 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 Beginning 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 develop ingenious options utilizing AWS services and sped up calculate. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning performance of large language designs. In his free time, Vivek takes pleasure in treking, seeing motion pictures, and attempting various foods.
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 Science 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 hub. She is enthusiastic about developing solutions that help consumers accelerate their AI journey and unlock business value.