Today, we are excited 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 design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative AI ideas on AWS.
In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the models also.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that utilizes support finding out to improve reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential differentiating function is its support learning (RL) step, which was utilized to refine the design's reactions beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, indicating it's geared up to break down complicated queries and reason through them in a detailed manner. This directed reasoning process enables the design to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation model that can be integrated into different workflows such as representatives, logical thinking and data interpretation tasks.
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, allowing efficient reasoning by routing inquiries to the most appropriate professional "clusters." This method allows the design to concentrate on various problem domains while maintaining overall 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 deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more effective architectures based on popular open designs 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 imitate the habits and reasoning patterns of the bigger DeepSeek-R1 design, 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 advise deploying this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and assess models against essential safety criteria. 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 multiple guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative AI applications.
Prerequisites
To release the DeepSeek-R1 model, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm 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, develop a limit increase request and reach out to your account team.
Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Establish authorizations to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to introduce safeguards, avoid harmful material, and evaluate models against crucial safety criteria. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and model reactions deployed 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 develop the guardrail, see the GitHub repo.
The basic flow involves the following steps: 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 out to the model for reasoning. After receiving the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections demonstrate 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, total the following steps:
1. On the Amazon Bedrock console, choose Model brochure 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 doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 design.
The design detail page provides necessary details about the model's abilities, rates structure, and execution guidelines. You can discover detailed usage instructions, consisting of sample API calls and code bits for combination. The design supports different text generation tasks, consisting of material creation, code generation, and concern answering, utilizing its support learning optimization and CoT thinking abilities.
The page likewise includes implementation options and licensing details to help you get going with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, select Deploy.
You will be prompted to set up the release details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters).
5. For Number of instances, get in a variety of instances (in between 1-100).
6. For example type, pick your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can configure advanced security and facilities settings, including virtual private cloud (VPC) networking, service role consents, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you might want to examine these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to start using the design.
When the release is total, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in play ground to access an interactive user interface where you can experiment with different prompts and change design specifications like temperature level and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For example, material for reasoning.
This is an excellent method to check out the model's reasoning and text generation abilities before incorporating it into your applications. The play ground offers immediate feedback, helping you comprehend how the design reacts to different inputs and letting you tweak your prompts for ideal results.
You can quickly test the design in the play area through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run inference using guardrails with the released DeepSeek-R1 endpoint
The following code example demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up reasoning parameters, bytes-the-dust.com and sends out a request to produce 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 release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 practical methods: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you select the approach that best matches 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 develop a domain.
3. On the SageMaker Studio console, pick 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 see the DeepSeek-R1 design card.
Each model card reveals essential details, including:
- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if appropriate), suggesting that this model can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design
5. Choose the model card to see the model details page.
The model details page consists of the following details:
- The design name and service provider details. Deploy button to release the design. About and Notebooks tabs with detailed details
The About tab includes important details, such as:
- Model description. - License details.
- Technical specifications.
- Usage standards
Before you deploy the design, it's recommended to examine the design details and license terms to confirm compatibility with your usage case.
6. Choose Deploy to proceed with deployment.
7. For Endpoint name, utilize the immediately generated name or create a custom-made one.
- For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, enter the number of circumstances (default: 1). Selecting suitable instance 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 optimized for sustained traffic and low latency.
- Review all configurations for precision. For this design, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
- Choose Deploy to deploy the model.
The release procedure can take a number of minutes to finish.
When implementation is complete, your endpoint status will alter to InService. At this point, the model is ready to accept reasoning demands through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is complete, you can conjure up the model using a SageMaker runtime client and integrate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for deploying the design is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.
You can run extra demands against the predictor:
Implement guardrails and run inference with your SageMaker JumpStart predictor
Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:
Clean 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, complete the following actions:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace deployments. - In the Managed deployments section, 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 proper implementation: 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 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 release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. 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 companies build ingenious options utilizing AWS services and sped up calculate. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the inference efficiency of big language models. In his leisure time, Vivek enjoys treking, seeing films, and trying 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 technology and .
Jonathan Evans is an Expert Solutions Architect working on generative AI with the Third-Party Model Science group at AWS.
Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about building options that assist customers accelerate their AI journey and unlock company worth.