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 release DeepSeek AI's first-generation frontier design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative AI concepts on AWS.
In this post, we show how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled versions of the models also.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that uses support finding out to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial identifying function is its support knowing (RL) action, which was used to fine-tune the design's responses beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately enhancing both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, meaning it's geared up to break down complicated inquiries and reason through them in a detailed way. This guided thinking process enables the model to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually caught the market's attention as a flexible text-generation design that can be integrated into numerous workflows such as representatives, sensible reasoning and data interpretation tasks.
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion parameters, enabling efficient inference by routing questions to the most relevant professional "clusters." This technique enables the model to focus on different problem domains while maintaining total effectiveness. DeepSeek-R1 needs 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 deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more effective architectures based upon 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 simulate the behavior and thinking patterns of the larger DeepSeek-R1 design, using it as a teacher design.
You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and evaluate designs against key safety criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, improving 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 check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select 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 ask for a limitation boost, create a limit boost request and connect to your account group.
Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For directions, see Set up permissions to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to present safeguards, prevent harmful material, and assess models against crucial security criteria. You can carry out security measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess 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 create the guardrail, see the GitHub repo.
The general flow involves the following steps: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for inference. After receiving the design's output, another guardrail check is used. If the output passes this final check, it's returned as the result. 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 occurred at the input or output stage. The examples showcased in the following areas show reasoning using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total 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 conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 model.
The model detail page offers vital details about the model's capabilities, pricing structure, and application standards. You can find detailed usage guidelines, including sample API calls and code snippets for combination. The model supports various text generation jobs, including material production, code generation, and question answering, using its support finding out optimization and CoT thinking abilities.
The page also includes deployment alternatives and licensing details to assist you start with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, pick Deploy.
You will be prompted to configure the implementation 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, enter a number of circumstances (between 1-100).
6. For Instance type, pick your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can configure advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service function authorizations, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production implementations, you might wish to review these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to start utilizing the design.
When the deployment is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in play area to access an interactive interface where you can try out various triggers and adjust model parameters like temperature level and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For instance, content for inference.
This is an outstanding way to explore the design's reasoning and text generation abilities before incorporating it into your applications. The play area provides immediate feedback, assisting you comprehend how the design reacts to numerous inputs and letting you tweak your triggers for optimal outcomes.
You can quickly check the model in the play area 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 utilizing guardrails with the released DeepSeek-R1 endpoint
The following code example shows how to perform reasoning using a deployed DeepSeek-R1 model 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 develop the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, 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) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with just a couple of clicks. With SageMaker JumpStart, pipewiki.org 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 design through SageMaker JumpStart uses 2 convenient techniques: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you select the technique that best fits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to deploy DeepSeek-R1 using SageMaker JumpStart:
1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be triggered to produce a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The design web browser shows available models, with details like the provider name and design abilities.
4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each model card reveals crucial details, consisting of:
- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if relevant), suggesting that this design can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design
5. Choose the model card to see the model details page.
The model details page consists of the following details:
- The design name and supplier details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
The About tab consists of crucial details, such as:
- Model description. - License details.
- Technical specifications.
- Usage standards
Before you deploy the model, it's suggested to evaluate the model details and license terms to validate compatibility with your usage case.
6. Choose Deploy to proceed with deployment.
7. For Endpoint name, utilize the instantly generated name or produce a customized one.
- For Instance type ¸ pick an instance type (default: ml.p5e.48 xlarge).
- For Initial instance count, go into the variety of instances (default: 1). Selecting proper circumstances types and counts is essential for cost and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency.
- Review all configurations for precision. For this model, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
- Choose Deploy to release the design.
The deployment process can take several minutes to complete.
When implementation is total, your endpoint status will change to InService. At this point, the design is all set to accept inference requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is total, you can conjure up the model utilizing a SageMaker runtime client and integrate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential 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 model is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
You can run additional 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 produce a guardrail using the Amazon Bedrock console or the API, and implement 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 implementation
If you deployed the model utilizing Amazon Bedrock Marketplace, total the following actions:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments. - In the Managed implementations section, find the endpoint you wish to erase.
- Select the endpoint, and on the Actions menu, pick Delete.
- Verify the endpoint details to make certain you're deleting the correct implementation: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart design you released will sustain expenses if you leave it running. Use the following code to delete the endpoint if you want 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 using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. 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 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 utilizing AWS services and accelerated compute. Currently, he is focused on developing strategies for fine-tuning and optimizing the reasoning efficiency of big language models. In his free time, Vivek enjoys hiking, seeing films, and trying different cuisines.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
is a Professional Solutions Architect working on generative AI with the Third-Party Model Science group at AWS.
Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is enthusiastic about constructing services that help customers accelerate their AI journey and unlock business value.