Today, we are excited 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 model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative AI concepts on AWS.
In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the models too.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that utilizes support finding out to enhance thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating feature is its reinforcement knowing (RL) step, which was used to fine-tune the design's actions beyond the basic pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately enhancing both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, implying it's geared up to break down intricate inquiries and factor through them in a detailed manner. This assisted reasoning process allows the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation design that can be incorporated into different workflows such as representatives, sensible reasoning and data interpretation jobs.
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion specifications, allowing efficient reasoning by routing questions to the most pertinent specialist "clusters." This technique permits the model to concentrate on various problem domains while maintaining general effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more efficient models to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor design.
You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and assess designs against crucial safety criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 model, you need 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 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 request a limit boost, produce a limitation boost request and connect to your account team.
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) permissions to utilize Amazon Bedrock Guardrails. For directions, see Set up permissions to utilize guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to present safeguards, avoid hazardous material, and examine designs against crucial security requirements. You can execute security procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to examine user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
The basic 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 model for reasoning. After receiving the design's output, another guardrail check is used. 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 happened at the input or output phase. The examples showcased in the following areas demonstrate reasoning utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane.
At the time of composing 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 supplier and select the DeepSeek-R1 design.
The design detail page supplies essential details about the design's abilities, pricing structure, and implementation standards. You can discover detailed usage instructions, consisting of sample API calls and code snippets for integration. The model supports different text generation jobs, consisting of material development, code generation, and question answering, using its reinforcement discovering optimization and CoT thinking capabilities.
The page likewise includes implementation alternatives and licensing details to assist you get started with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, pick Deploy.
You will be prompted to configure the release details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of instances, go into a number of circumstances (in between 1-100).
6. For example type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can set up advanced security and facilities settings, including virtual personal cloud (VPC) networking, service function consents, and file encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, oeclub.org you might desire to review these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start using the design.
When the deployment is complete, you can check 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 adjust model parameters like temperature level and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For instance, content for reasoning.
This is an excellent way to check out the design's reasoning and text generation capabilities before integrating it into your applications. The play area offers immediate feedback, helping you comprehend how the model reacts to various inputs and letting you fine-tune your prompts for optimum outcomes.
You can quickly test the design in the play area through the UI. However, to invoke the released model 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 carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, and sends out a request to create text based upon 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 services that you can release 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 utilizing either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 practical approaches: utilizing the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to help you select the technique that finest suits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:
1. On the SageMaker console, choose Studio in the navigation pane.
2. First-time users will be triggered to create a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
The model web browser displays available designs, with details like the company name and design capabilities.
4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each model card reveals crucial details, including:
- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if suitable), suggesting that this model can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model
5. Choose the design card to view the design details page.
The design details page includes the following details:
- The design name and company details. Deploy button to release the design. About and Notebooks tabs with detailed details
The About tab consists of important details, such as:
- Model description. - License details.
- Technical specifications.
- Usage guidelines
Before you release the design, it's recommended to examine the model details and license terms to validate compatibility with your use case.
6. Choose Deploy to continue with release.
7. For Endpoint name, use the immediately generated name or develop a custom-made one.
- For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, get in the number of instances (default: 1). Selecting suitable instance types and counts is important for cost and efficiency optimization. Monitor your deployment to change 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 configurations for precision. For this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to release the design.
The implementation procedure can take several minutes to finish.
When release is total, your endpoint status will change to InService. At this moment, the design is ready to accept inference requests through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is complete, you can conjure up the design utilizing a SageMaker runtime customer and integrate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the needed AWS authorizations and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for deploying the design is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
You can run additional demands against the predictor:
Implement guardrails and run inference with your SageMaker JumpStart predictor
Similar to Amazon Bedrock, you can likewise utilize 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 prevent unwanted charges, finish the actions in this area to clean 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 designs in the navigation pane, select Marketplace releases. - In the Managed releases area, locate the endpoint you wish to delete.
- Select the endpoint, and on the Actions menu, select Delete.
- Verify the endpoint details to make certain you're erasing the proper deployment: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart design you released 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 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 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 build innovative options using AWS services and sped up compute. Currently, he is concentrated on developing strategies for fine-tuning and enhancing the inference efficiency of large language designs. In his leisure time, Vivek enjoys hiking, watching films, and attempting various foods.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group 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 a Professional Solutions Architect dealing with 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 hub. She is passionate about developing options that assist clients accelerate their AI journey and unlock organization value.