Today, we are excited 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 release DeepSeek AI's first-generation frontier model, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion criteria to build, experiment, and properly 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 steps to deploy the distilled versions of the models also.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that uses reinforcement discovering to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential differentiating feature is its support learning (RL) action, which was used to fine-tune the design's responses beyond the standard pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, ultimately improving both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, meaning it's equipped to break down intricate inquiries and reason through them in a detailed way. This guided thinking procedure 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 responses while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation model that can be integrated into different workflows such as agents, rational thinking and information interpretation tasks.
DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion specifications, enabling effective reasoning by routing queries to the most appropriate expert "clusters." This technique permits the model to specialize in different issue domains while maintaining overall performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 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 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 models to mimic the habits and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as an instructor 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 design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful content, and assess models against crucial safety 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 develop several 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 deploy 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 confirm 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 request a limitation increase, develop a limit boost demand and connect to your account group.
Because you will be deploying 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 guidelines, see Set up permissions to utilize guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to introduce safeguards, avoid harmful content, and evaluate designs against key safety criteria. You can implement security procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate 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 create the guardrail, see the GitHub repo.
The basic circulation involves the following steps: 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 receiving the model's output, another guardrail check is used. If the output passes this final check, it's returned as the final outcome. 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 phase. The examples showcased in the following areas show reasoning using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation designs (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 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 provider and pick the DeepSeek-R1 design.
The design detail page supplies vital details about the model's abilities, prices structure, and execution guidelines. You can discover detailed use guidelines, consisting of sample API calls and code bits for combination. The model supports numerous text generation tasks, including material creation, code generation, and concern answering, utilizing its support finding out optimization and CoT thinking capabilities.
The page likewise includes release options 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 triggered to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of instances, get in a variety of instances (in between 1-100).
6. For Instance type, choose 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 advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function authorizations, and encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you might want to review these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start using the model.
When the release is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in play ground to access an interactive user interface where you can try out different prompts and change model criteria like temperature and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal outcomes. For instance, material for reasoning.
This is an outstanding method to check out the model's reasoning and text generation abilities before incorporating it into your applications. The playground offers immediate feedback, you understand how the design reacts to numerous inputs and letting you tweak your prompts for optimal outcomes.
You can rapidly test the model 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 released DeepSeek-R1 endpoint
The following code example shows how to perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce 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 client, sets up reasoning specifications, 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 options that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor it-viking.ch pre-trained models to your usage case, with your data, and release them into production using either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 practical approaches: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you select the method that best suits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:
1. On the SageMaker console, pick Studio in the navigation pane.
2. First-time users will be prompted to create a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
The model internet browser shows available models, with details like the provider name and model capabilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each model card reveals crucial details, including:
- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if applicable), indicating that this model can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design
5. Choose the design card to see the model details page.
The design details page includes the following details:
- The model name and supplier details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
The About tab consists of essential details, such as:
- Model description. - License details.
- Technical specifications.
- Usage standards
Before you deploy the design, it's advised to examine the model details and license terms to verify compatibility with your use case.
6. Choose Deploy to continue with release.
7. For Endpoint name, use the automatically generated name or create a custom-made one.
- For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, go into the number of instances (default: 1). Selecting suitable instance types and counts is essential for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency.
- Review all setups for precision. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
- Choose Deploy to release the model.
The release process can take several minutes to finish.
When deployment is complete, your endpoint status will change to InService. At this point, the design is ready to accept inference requests through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is total, you can invoke the design using a SageMaker runtime customer and integrate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To start with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS consents 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 supplied 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:
Clean up
To prevent unwanted charges, complete the steps in this area to tidy up your resources.
Delete the Amazon Bedrock Marketplace deployment
If you deployed the design using Amazon Bedrock Marketplace, complete the following actions:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments. - In the Managed deployments section, find the endpoint you desire to erase.
- Select the endpoint, and on the Actions menu, select Delete.
- Verify the endpoint details to make certain you're erasing the correct release: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model 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 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 begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, 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 construct ingenious solutions using AWS services and sped up compute. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the reasoning efficiency of big language models. In his totally free time, Vivek delights in hiking, enjoying movies, and attempting different cuisines.
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 product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about developing services that assist consumers accelerate their AI journey and unlock organization worth.