Today, we are thrilled 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 model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion criteria 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 release 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 learning to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key identifying feature is its reinforcement knowing (RL) action, which was utilized to refine the design's actions beyond the basic pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, ultimately improving both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it's equipped to break down intricate queries and reason through them in a detailed manner. This assisted thinking procedure allows the model to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has caught the market's attention as a versatile text-generation model that can be integrated into various workflows such as agents, sensible thinking and data analysis jobs.
DeepSeek-R1 utilizes a Mixture 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 questions to the most appropriate expert "clusters." This method allows the model to focus on various issue domains while maintaining general effectiveness. 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 instance to deploy the design. ml.p5e.48 xlarge comes with 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 procedure of training smaller, more effective designs to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 design, using it as a teacher 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 location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent hazardous material, and evaluate models against essential safety requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative AI applications.
Prerequisites
To release the DeepSeek-R1 model, you need 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 usage. 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 limit increase request and reach out to your account team.
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) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Establish permissions to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to present safeguards, prevent hazardous content, and evaluate designs against key safety requirements. You can carry out security steps for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
The basic flow involves 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 to the design for reasoning. After getting the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the result. However, if either the input or output is stepped in 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 sections show inference utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace gives 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 catalog under Foundation designs in the navigation pane.
At the time of composing 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 company and pick the DeepSeek-R1 model.
The design detail page supplies important details about the model's capabilities, pricing structure, and execution guidelines. You can find detailed use instructions, including sample API calls and code snippets for combination. The design supports different text generation jobs, including material development, code generation, and question answering, using its support learning optimization and CoT reasoning capabilities.
The page also includes implementation alternatives and licensing details to help you start with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, pick Deploy.
You will be triggered to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of instances, get in a variety of circumstances (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 recommended.
Optionally, you can set up sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service role permissions, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production deployments, you may want to evaluate these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to begin 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 play ground to access an interactive interface where you can explore various prompts and adjust model parameters like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For instance, content for inference.
This is an exceptional method to explore the design's reasoning and text generation abilities before incorporating it into your applications. The play ground offers instant feedback, helping you comprehend how the model reacts to numerous inputs and letting you fine-tune your triggers for ideal results.
You can rapidly check the design in the play area through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
The following code example demonstrates how to carry out inference using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures reasoning 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) hub with FMs, integrated algorithms, and prebuilt ML services that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and release them into production using either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 convenient methods: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to help you choose the method that finest fits 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 develop a domain.
3. On the SageMaker Studio console, select JumpStart in the pane.
The design browser displays available designs, with details like the supplier name and design abilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each model card shows key details, consisting of:
- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if appropriate), indicating that this model can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design
5. Choose the design card to view the design details page.
The design details page includes the following details:
- The design name and company details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
The About tab consists of essential details, such as:
- Model description. - License details.
- Technical requirements.
- Usage standards
Before you release the model, it's suggested to evaluate the model details and license terms to validate compatibility with your usage case.
6. Choose Deploy to continue with implementation.
7. For Endpoint name, use the instantly created name or create a customized one.
- For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, get in the variety of instances (default: 1). Selecting proper instance types and counts is vital for cost and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized 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 seclusion remains in place.
- Choose Deploy to deploy the model.
The implementation procedure can take numerous minutes to finish.
When deployment is complete, your endpoint status will change to InService. At this point, the model is ready to accept inference requests through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is complete, you can conjure up the design utilizing a SageMaker runtime customer and integrate it with your applications.
Deploy DeepSeek-R1 utilizing 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 approvals and setiathome.berkeley.edu environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.
You can run extra requests against the predictor:
Implement guardrails and run reasoning 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 displayed in the following code:
Clean up
To avoid undesirable charges, finish the actions in this area to clean up your resources.
Delete the Amazon Bedrock Marketplace release
If you released the design using Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace deployments. - In the Managed implementations area, find the endpoint you desire to delete.
- 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 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 checked out how you can access and release the DeepSeek-R1 design utilizing 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 models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, setiathome.berkeley.edu and Getting going 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 develop ingenious solutions utilizing AWS services and sped up calculate. Currently, he is focused on developing techniques for fine-tuning and enhancing the reasoning performance of big language designs. In his free time, Vivek delights in treking, watching movies, and attempting various 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 Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
Banu Nagasundaram leads product, engineering, bytes-the-dust.com and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is enthusiastic about constructing services that help clients accelerate their AI journey and unlock service value.