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 deploy DeepSeek AI's first-generation frontier design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion criteria to construct, experiment, and properly scale your generative AI ideas on AWS.
In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the models too.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that uses support learning to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential identifying feature is its support learning (RL) step, which was used to fine-tune the model's reactions beyond the standard pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, eventually improving both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, meaning it's geared up to break down intricate queries and reason through them in a detailed manner. This assisted thinking procedure allows the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation model that can be integrated into numerous workflows such as agents, rational thinking and information analysis jobs.
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion specifications, allowing efficient inference by routing questions to the most appropriate expert "clusters." This approach permits the design to specialize in various problem domains while maintaining general effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more effective designs to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 model, using it as a teacher model.
You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and assess designs against key safety criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose 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 limit increase, produce a limit increase demand and connect to your account team.
Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For directions, see Set up permissions to use guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to introduce safeguards, avoid harmful material, and evaluate designs against essential security criteria. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
The general circulation involves 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 design for inference. After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the final 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 reasoning utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
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 use the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a company and choose the DeepSeek-R1 model.
The model detail page provides essential details about the design's capabilities, prices structure, and application standards. You can find detailed use directions, consisting of sample API calls and code bits for combination. The design supports various text generation tasks, consisting of material development, code generation, and question answering, utilizing its support learning optimization and CoT reasoning abilities.
The page likewise includes implementation alternatives and licensing details to help you start with DeepSeek-R1 in your applications.
3. To DeepSeek-R1, select Deploy.
You will be triggered to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of circumstances, get in a number of circumstances (between 1-100).
6. For Instance type, pick your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role consents, and encryption settings. For most use cases, the default settings will work well. However, for production deployments, you might desire to evaluate these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to start utilizing the model.
When the deployment is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground.
8. Choose Open in play area to access an interactive interface where you can experiment with various prompts and adjust model specifications like temperature and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For instance, content for inference.
This is an excellent way to explore the model's reasoning and text generation abilities before integrating it into your applications. The play ground offers instant feedback, assisting you comprehend how the model reacts to various inputs and letting you fine-tune your prompts for optimum results.
You can rapidly check the design in the play ground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
The following code example shows how to perform inference utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning specifications, and sends out a demand to create text based on a user prompt.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML options that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 hassle-free techniques: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you pick the method that best matches your requirements.
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 triggered to create a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
The model browser displays available models, with details like the provider name and design capabilities.
4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each design card shows key details, including:
- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if appropriate), indicating that this model can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the design
5. Choose the design card to see the design details page.
The design details page consists of 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 includes important details, such as:
- Model description. - License details.
- Technical specs.
- Usage guidelines
Before you release the model, it's recommended to examine the model details and license terms to validate compatibility with your use case.
6. Choose Deploy to proceed with release.
7. For Endpoint name, use the automatically created name or create a custom-made one.
- For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, get in the number of circumstances (default: 1). Selecting suitable 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 chosen by default. This is optimized for sustained traffic and low latency.
- Review all configurations for accuracy. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to release the design.
The implementation process can take a number of minutes to complete.
When release is complete, your endpoint status will alter to InService. At this moment, the model is all set to accept inference requests through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is total, you can invoke the model using a SageMaker runtime customer and integrate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS authorizations and 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 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 utilizing the Amazon Bedrock console or the API, and implement it as revealed in the following code:
Tidy up
To avoid undesirable charges, complete the steps in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you deployed the design using Amazon Bedrock Marketplace, complete the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations. - In the Managed releases section, locate 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 erasing the right deployment: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart design you deployed will sustain expenses 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, surgiteams.com we explored 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, 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 using AWS services and accelerated compute. Currently, he is focused on developing methods for fine-tuning and enhancing the reasoning efficiency of big language models. In his spare time, Vivek takes pleasure in treking, seeing movies, and trying 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 item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is enthusiastic about developing services that help consumers accelerate their AI journey and unlock business value.