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 design, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion specifications to develop, experiment, and properly 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 actions to deploy the distilled variations of the models also.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that utilizes reinforcement discovering to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial identifying feature is its support knowing (RL) action, which was utilized to refine the model's actions beyond the standard pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust more successfully to user feedback and goals, eventually boosting both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, indicating it's equipped to break down complex queries and reason through them in a detailed way. This guided reasoning process allows the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually caught the market's attention as a flexible text-generation model that can be incorporated into different workflows such as representatives, rational reasoning and data analysis tasks.
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion parameters, enabling efficient inference by routing inquiries to the most appropriate specialist "clusters." This method permits the model to focus on various issue domains while maintaining total performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the reasoning capabilities 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 sized, more efficient models to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 model, using 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 recommend releasing this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and examine models against crucial 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 create several guardrails to different usage cases and apply them to the DeepSeek-R1 model, 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 circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick 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 circumstances in the AWS Region you are deploying. To ask for a limit increase, produce a limit increase request and connect to your account group.
Because you will be deploying 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 enables you to present safeguards, prevent hazardous content, and examine designs against crucial security criteria. You can implement safety measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and model actions released 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 flow includes the following actions: 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 design's output, another guardrail check is used. 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 areas demonstrate inference 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 actions:
1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 model.
The design detail page provides vital details about the design'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 different text generation tasks, consisting of material production, code generation, and question answering, using its reinforcement discovering optimization and CoT thinking capabilities.
The page likewise includes implementation options and licensing details to assist you get begun with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, choose Deploy.
You will be triggered to configure the release details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, hb9lc.org get in an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of instances, go into a variety of circumstances (between 1-100).
6. For example type, pick your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up sophisticated security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you may desire to evaluate these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to start utilizing the design.
When the deployment is total, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in play ground to access an interactive user interface where you can try out different triggers and adjust design specifications like temperature level and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For example, content for inference.
This is an excellent way to explore the model's thinking and text generation abilities before integrating it into your applications. The play ground supplies immediate feedback, helping you understand how the design responds to numerous inputs and letting you fine-tune your prompts for optimal outcomes.
You can rapidly evaluate the design in the play ground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
The following code example shows how to perform 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 create the guardrail, see the GitHub repo. After you have actually developed the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning specifications, and sends a request 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 solutions that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models 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 two convenient techniques: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you choose the technique that finest suits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
1. On the SageMaker console, choose 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 model web browser displays available designs, with details like the service provider name and design 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 instance, Text Generation).
Bedrock Ready badge (if applicable), suggesting that this model can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the model
5. Choose the model card to see the design details page.
The design details page includes the following details:
- The model name and provider details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
The About tab consists of crucial details, such as:
- Model description. - License details.
- Technical requirements.
- Usage guidelines
Before you release the design, it's suggested to evaluate the model details and license terms to verify compatibility with your use case.
6. Choose Deploy to proceed with deployment.
7. For Endpoint name, utilize the immediately generated name or produce a custom one.
- For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
- For Initial instance count, get in the variety of circumstances (default: 1). Selecting proper instance types and counts is crucial for cost and efficiency optimization. Monitor your release to change 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 accuracy. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
- Choose Deploy to release the model.
The implementation process can take a number of minutes to complete.
When deployment is complete, your endpoint status will alter to InService. At this moment, the design is prepared to accept inference requests through the endpoint. You can keep track of the release progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is total, you can invoke the model utilizing a SageMaker runtime customer and incorporate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is provided in the Github here. You can clone the note pad and run from SageMaker Studio.
You can run extra requests against the predictor:
Implement guardrails and run inference with your SageMaker JumpStart predictor
Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
Clean up
To prevent undesirable charges, finish the actions in this section to tidy up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you deployed the model using Amazon Bedrock Marketplace, total the following actions:
1. On the Amazon Bedrock console, forum.pinoo.com.tr under Foundation models in the navigation pane, select Marketplace implementations. - In the Managed implementations section, locate the endpoint you desire to delete.
- Select the endpoint, and on the Actions menu, pick Delete.
- Verify the endpoint details to make certain you're deleting the proper deployment: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart design you released will sustain costs 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 deploy the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. 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 Getting going with Amazon SageMaker JumpStart.
About the Authors
Vivek Gangasani is a Lead Specialist Solutions Architect for systemcheck-wiki.de Inference at AWS. He helps emerging generative AI companies develop innovative options utilizing AWS services and sped up calculate. Currently, he is concentrated on developing techniques for fine-tuning and enhancing the inference performance of large language models. In his leisure time, Vivek takes pleasure in hiking, enjoying motion pictures, and 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 technology 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 product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about constructing services that assist clients accelerate their AI journey and unlock service worth.