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 deploy DeepSeek AI's first-generation frontier design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative AI ideas on AWS.
In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled variations of the models too.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that utilizes support finding out to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key differentiating function is its reinforcement knowing (RL) action, which was used to fine-tune the design's responses beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately improving both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, indicating it's geared up to break down complex inquiries and factor through them in a detailed way. This directed reasoning process permits the design to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the market's attention as a flexible text-generation model that can be incorporated into various workflows such as agents, sensible reasoning and information interpretation jobs.
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows of 37 billion parameters, enabling effective reasoning by routing questions to the most appropriate professional "clusters." This method allows the design to concentrate on various issue domains while maintaining general effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the thinking abilities 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 describes a procedure of training smaller sized, more effective designs to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher design.
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and assess designs against key safety requirements. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, improving 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 circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select 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 instance in the AWS Region you are releasing. To ask for a limitation increase, produce a limitation increase demand and reach out to your account group.
Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Establish approvals to utilize guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to present safeguards, prevent harmful material, and examine models against crucial security requirements. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
The general circulation includes 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 to the design for reasoning. After receiving the model's output, another guardrail check is used. If the output passes this final check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned showing 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 gives 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, pick Model brochure under Foundation models in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 design.
The design detail page supplies essential details about the model's capabilities, rates structure, and application standards. You can discover detailed usage guidelines, including sample API calls and code bits for combination. The design supports different text generation tasks, consisting of material creation, code generation, and concern answering, using its reinforcement learning optimization and CoT reasoning capabilities.
The page also consists of release choices and licensing details to assist you get begun with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy.
You will be prompted to configure the release details for DeepSeek-R1. The design 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 number of instances (in between 1-100).
6. For Instance type, choose your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up advanced security and facilities settings, consisting of virtual private cloud (VPC) networking, service function permissions, and file encryption settings. For many use cases, the default settings will work well. However, for production releases, you might want to evaluate these settings to align with your company's security and compliance requirements.
7. Choose Deploy to begin using the model.
When the release is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play ground.
8. Choose Open in play ground to access an interactive interface where you can experiment with various prompts and change design specifications like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal results. For example, content for reasoning.
This is an outstanding way to check out the model's reasoning and text generation capabilities before integrating it into your applications. The play ground provides instant feedback, assisting you understand how the design reacts to different inputs and letting you fine-tune your triggers for optimum outcomes.
You can quickly check the model in the playground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run reasoning using 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 create 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 produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, and sends a demand to create text based on a user prompt.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML solutions that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production using either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 practical methods: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to help you pick the method that finest matches your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:
1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be prompted to create a domain.
3. On the SageMaker Studio console, wiki.myamens.com select JumpStart in the navigation pane.
The model internet browser displays available models, with details like the provider name and design abilities.
4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each model card reveals crucial details, including:
- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if applicable), indicating that this model can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the design
5. Choose the design card to view the model details page.
The model details page consists of the following details:
- The model name and company details. Deploy button to release 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 model, it's suggested to evaluate the design details and license terms to verify compatibility with your usage case.
6. Choose Deploy to continue with implementation.
7. For Endpoint name, utilize the instantly produced name or produce a custom one.
- For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge).
- For Initial instance count, enter the variety of circumstances (default: 1). Selecting appropriate circumstances types and counts is vital for expense and performance optimization. Monitor your deployment to adjust 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 advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
- Choose Deploy to deploy the model.
The release process can take numerous minutes to finish.
When release is total, your endpoint status will alter to InService. At this point, the model is ready to accept reasoning requests through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation is complete, you can invoke 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 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS authorizations 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 design is provided in the Github here. You can clone the note pad and run from SageMaker Studio.
You can run extra demands against the predictor:
Implement guardrails and run reasoning 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 utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:
Clean up
To avoid unwanted charges, finish the actions in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace deployment
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 implementations. - In the Managed releases area, find the endpoint you desire to delete.
- 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 released will sustain expenses 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 design using 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 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 options using AWS services and sped up calculate. Currently, he is concentrated on developing methods for fine-tuning and enhancing the inference efficiency of big language models. In his spare time, Vivek delights in treking, enjoying films, 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 Professional Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about developing solutions that help consumers accelerate their AI journey and unlock business value.