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, together with the distilled variations varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative AI concepts on AWS.
In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the designs as well.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that uses reinforcement finding out to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial distinguishing feature is its reinforcement learning (RL) step, which was utilized to refine the design's responses beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, meaning it's geared up to break down complicated queries and factor through them in a detailed manner. This guided thinking process permits the design to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually caught the market's attention as a flexible text-generation model that can be incorporated into various workflows such as representatives, rational thinking and data interpretation tasks.
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, making it possible for efficient reasoning by routing inquiries to the most pertinent specialist "clusters." This method enables the model to specialize in various problem domains while maintaining overall performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 model to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more efficient models to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 design, using it as an instructor model.
You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and examine models against essential security criteria. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative AI applications.
Prerequisites
To release the DeepSeek-R1 design, 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, pick Amazon SageMaker, and verify you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To request a limit boost, create a limitation increase demand and connect 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) consents to use Amazon Bedrock Guardrails. For instructions, see Establish permissions to utilize guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to introduce safeguards, prevent harmful content, and examine models against key security criteria. You can carry out safety procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to examine user inputs and model actions deployed 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 develop the guardrail, see the GitHub repo.
The basic 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 design's output, another guardrail check is used. If the output passes this final check, it's returned as the last 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 occurred at the input or output stage. The examples showcased in the following areas show inference using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
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 utilize the InvokeModel API to conjure up the model. It does not 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 necessary details about the model's abilities, pricing structure, and application guidelines. You can discover detailed usage guidelines, consisting of sample API calls and code snippets for integration. The design supports numerous text generation tasks, including content creation, code generation, and concern answering, using its reinforcement learning optimization and CoT reasoning abilities.
The page likewise consists of implementation choices and licensing details to assist you start with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, pick Deploy.
You will be prompted to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of instances, get in a variety of instances (in between 1-100).
6. For example type, select your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can set up sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service role permissions, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production implementations, you may wish to evaluate these to line up with your organization's security and compliance requirements.
7. Choose Deploy to begin using the model.
When the deployment is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in play ground to access an interactive user interface where you can explore various triggers and adjust model criteria like temperature and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For instance, content for inference.
This is an excellent method to check out the model's reasoning and text generation capabilities before integrating it into your applications. The play area offers instant feedback, assisting you comprehend how the design reacts to different inputs and letting you tweak your prompts for optimal results.
You can rapidly check the design in the play area through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
The following code example demonstrates how to carry out reasoning utilizing a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends a demand to produce 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 services that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart uses two practical approaches: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you pick the technique 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, pick Studio in the navigation pane.
2. First-time users will be triggered to produce a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
The design web browser shows available designs, with details like the provider name and model capabilities.
4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each design card shows crucial details, including:
- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if applicable), showing that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the design
5. Choose the design card to view the design details page.
The design details page includes the following details:
- The model name and company 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 specs.
- Usage guidelines
Before you release the model, it's suggested to review the model details and license terms to verify compatibility with your use case.
6. Choose Deploy to proceed with implementation.
7. For Endpoint name, use the immediately produced name or develop a custom-made one.
- For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, get in the number of circumstances (default: 1). Selecting appropriate instance types and counts is important for expense and efficiency optimization. Monitor your release to change 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 suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
- Choose Deploy to release the model.
The implementation procedure can take several minutes to complete.
When deployment is total, your endpoint status will alter to InService. At this moment, the model is prepared to accept inference demands through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is complete, you can invoke the design 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 need 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 deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the design is provided in the Github here. You can clone the notebook and range from SageMaker Studio.
You can run additional requests against the predictor:
Implement guardrails and run reasoning with your SageMaker JumpStart predictor
Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
Tidy up
To prevent undesirable charges, complete the steps in this area to tidy up your resources.
Delete the Amazon Bedrock Marketplace deployment
If you released the design using Amazon Bedrock Marketplace, complete the following actions:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations. - In the Managed deployments section, locate the endpoint you wish to erase.
- Select the endpoint, and on the Actions menu, select 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 SageMaker 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 explored 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 begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, 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 construct innovative options utilizing AWS services and accelerated compute. Currently, he is focused on establishing strategies for fine-tuning and enhancing the reasoning performance of big language models. In his spare time, Vivek enjoys hiking, viewing films, and pipewiki.org attempting 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 an Expert Solutions Architect dealing with 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 hub. She is passionate about building options that help consumers accelerate their AI journey and unlock organization value.