Today, we are thrilled to announce 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, together with the distilled versions ranging from 1.5 to 70 billion criteria to construct, 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 comparable steps to release the distilled variations of the designs too.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language design (LLM) established by DeepSeek AI that utilizes support discovering to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key identifying feature is its reinforcement knowing (RL) action, which was utilized to fine-tune the design's actions beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately improving both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, suggesting it's equipped to break down intricate questions and factor through them in a detailed way. This assisted thinking process permits the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually captured the market's attention as a flexible text-generation design that can be integrated into various workflows such as representatives, sensible 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 parameters, enabling effective inference by routing inquiries to the most pertinent professional "clusters." This technique permits the design to focus on various problem domains while maintaining overall performance. 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 xlarge circumstances to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 model 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 models to imitate the behavior and thinking patterns of the larger DeepSeek-R1 model, utilizing it as an instructor design.
You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and evaluate designs against key security criteria. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative AI applications.
Prerequisites
To release the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limitation boost, create a limitation increase demand and reach out to your account group.
Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For directions, see Set up authorizations to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to present safeguards, prevent damaging content, and examine designs against key safety criteria. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and design responses 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 general circulation involves 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 inference. After getting the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the 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 took place at the input or output phase. The examples showcased in the following sections demonstrate inference utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
1. On the Amazon Bedrock console, choose Model brochure under Foundation designs in the navigation pane.
At the time of writing 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 supplier and choose the DeepSeek-R1 design.
The model detail page offers essential details about the design's capabilities, pricing structure, and execution standards. You can discover detailed usage guidelines, including sample API calls and code snippets for combination. The model supports different text generation jobs, consisting of content creation, code generation, and question answering, using its support discovering optimization and CoT reasoning abilities.
The page also consists of deployment options and licensing details to assist you get going with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, select Deploy.
You will be prompted to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of instances, enter a number of instances (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 sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you may wish to examine these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to begin utilizing the design.
When the implementation is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground.
8. Choose Open in play ground to access an interactive user interface where you can try out various prompts and change model parameters like temperature level and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum results. For instance, content for reasoning.
This is an excellent way to explore the design's reasoning and text generation capabilities before incorporating it into your applications. The playground offers immediate feedback, assisting you understand how the model reacts to various inputs and letting you tweak your triggers for ideal outcomes.
You can rapidly check the design in the playground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run inference using guardrails with the deployed DeepSeek-R1 endpoint
The following code example demonstrates how to perform reasoning using 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 create the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, and sends a request to generate text based upon a user timely.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML services that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into production using either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart uses two hassle-free methods: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you pick the approach that best fits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to deploy DeepSeek-R1 using SageMaker JumpStart:
1. On the SageMaker console, choose Studio in the navigation pane.
2. First-time users will be prompted to create a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The design browser displays available models, with details like the service provider name and model capabilities.
4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each model card reveals crucial details, consisting of:
- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if suitable), showing that this design can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the design
5. Choose the design card to view the model details page.
The design details page includes the following details:
- The model name and company details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
The About tab includes important details, such as:
- Model description. - License details.
- Technical specs.
- Usage guidelines
Before you deploy the model, it's suggested to evaluate the model details and license terms to validate compatibility with your use case.
6. Choose Deploy to proceed with deployment.
7. For Endpoint name, utilize the immediately produced name or create a customized one.
- For example type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, get in the number of instances (default: wiki.dulovic.tech 1). Selecting proper instance types and counts is essential for expense and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency.
- Review all setups for accuracy. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
- Choose Deploy to deploy the design.
The release process can take numerous minutes to complete.
When implementation is complete, your endpoint status will alter to InService. At this point, the design is ready to accept reasoning demands through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is total, you can conjure up the model using a SageMaker runtime customer and incorporate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To get going with DeepSeek-R1 using the SageMaker Python SDK, you will need 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 deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is provided in the Github here. You can clone the note pad 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as revealed in the following code:
Tidy up
To avoid undesirable charges, complete the steps in this section to tidy up your resources.
Delete the Amazon Bedrock Marketplace deployment
If you deployed the design using Amazon Bedrock Marketplace, total the following actions:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations. - In the Managed deployments area, find the endpoint you desire to erase.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're erasing the correct implementation: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The JumpStart model you released will sustain expenses if you leave it running. Use the following code to erase 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 model using 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 models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.
About the Authors
Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI companies develop innovative options using AWS services and accelerated compute. Currently, he is focused on establishing strategies for fine-tuning and enhancing the inference performance of big language designs. In his spare time, Vivek enjoys treking, viewing films, and trying different foods.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location 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 working on 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 building services that assist consumers accelerate their AI journey and unlock service value.