Today, we are thrilled 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, together with the distilled versions varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative AI ideas on AWS.
In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the models too.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that uses support finding out to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential differentiating feature is its reinforcement learning (RL) action, which was used to improve the design's responses beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually boosting both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, suggesting it's geared up to break down intricate inquiries and reason through them in a detailed way. This assisted reasoning procedure permits the model to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has caught the industry's attention as a flexible text-generation model that can be incorporated into numerous workflows such as representatives, sensible reasoning and data interpretation jobs.
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, enabling effective inference by routing inquiries to the most relevant professional "clusters." This method enables the design to concentrate on various problem domains while maintaining total efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances 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 abilities of the main R1 design to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more efficient designs to mimic the behavior and thinking 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 model, we advise deploying this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and assess models against key safety requirements. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, enhancing 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 instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're utilizing 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 deploying. To request a limitation boost, create a limitation increase request and reach out to your account group.
Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For directions, see Establish consents to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to introduce safeguards, avoid hazardous content, and assess models against essential safety criteria. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and model reactions deployed 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 produce the guardrail, see the GitHub repo.
The basic circulation includes the following actions: First, the system receives 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 inference. After getting the design's output, wiki.rolandradio.net 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 indicating the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections show inference utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace offers 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 steps:
1. On the Amazon Bedrock console, select Model brochure under Foundation models in the navigation pane.
At the time of writing this post, you can utilize the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.
The design detail page supplies important details about the design's abilities, rates structure, and implementation standards. You can discover detailed use guidelines, consisting of sample API calls and code snippets for integration. The design supports numerous text generation jobs, consisting of material creation, code generation, and concern answering, using its support learning optimization and CoT thinking abilities.
The page likewise includes deployment choices and licensing details to help you get going with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, select Deploy.
You will be triggered to set up the deployment 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 circumstances, enter a variety of circumstances (between 1-100).
6. For Instance type, choose your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can set up innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service function permissions, and file encryption settings. For most use cases, the default settings will work well. However, for production deployments, you may wish to evaluate these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to start using the model.
When the release is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground.
8. Choose Open in playground to access an interactive interface where you can explore various prompts and adjust design parameters like temperature level and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For example, content for reasoning.
This is an outstanding method to explore the model's thinking and text generation capabilities before integrating it into your applications. The play area offers immediate feedback, assisting you comprehend how the model reacts to various inputs and letting you fine-tune your triggers for optimum results.
You can quickly evaluate the model in the play ground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
The following code example demonstrates how to perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create 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 actually created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference parameters, and sends out a request to generate 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 services that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 hassle-free techniques: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you choose the technique that finest fits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:
1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be prompted to develop a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The model internet browser displays available models, with details like the company name and design capabilities.
4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each design card shows essential details, consisting of:
- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if appropriate), showing that this model can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the model
5. Choose the model card to view the model details page.
The model details page includes the following details:
- The design name and supplier details. to deploy the design. About and Notebooks tabs with detailed details
The About tab includes important details, such as:
- Model description. - License details.
- Technical requirements.
- Usage guidelines
Before you release the model, it's suggested to review the design details and license terms to verify compatibility with your usage case.
6. Choose Deploy to proceed with deployment.
7. For Endpoint name, utilize the automatically produced name or produce a custom one.
- For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge).
- For Initial instance count, enter the number of circumstances (default: 1). Selecting appropriate instance types and counts is crucial for expense and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency.
- Review all setups for precision. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to release the design.
The deployment procedure can take numerous minutes to complete.
When deployment is complete, your endpoint status will alter to InService. At this point, the model is ready to accept inference demands through the endpoint. You can keep track of the release progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is complete, you can conjure up the model utilizing a SageMaker runtime client 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 require to set up 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 release and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is supplied 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 likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
Clean up
To avoid unwanted charges, finish the steps in this area to tidy up your resources.
Delete the Amazon Bedrock Marketplace release
If you released the model utilizing Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases. - In the Managed deployments section, find the endpoint you wish to delete.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're deleting the correct deployment: 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 desire to stop sustaining charges. For more details, see Delete Endpoints and Resources.
Conclusion
In this post, we explored how you can access and deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, 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 helps emerging generative AI companies construct innovative solutions using AWS services and sped up calculate. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the reasoning efficiency of big language models. In his spare time, Vivek delights in hiking, watching films, and trying various cuisines.
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 Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about developing options that help customers accelerate their AI journey and unlock organization value.