Today, we are thrilled to reveal 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 model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion specifications to develop, 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 comparable actions to deploy the distilled variations of the models too.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that uses reinforcement discovering to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial identifying function is its reinforcement knowing (RL) step, which was used to fine-tune the design's responses beyond the standard pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately improving both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, meaning it's geared up to break down complicated inquiries and factor through them in a detailed manner. This directed reasoning process enables the design to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has caught the market's attention as a versatile text-generation model that can be incorporated into different workflows such as representatives, logical thinking and information interpretation jobs.
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient inference by routing inquiries to the most appropriate expert "clusters." This technique permits the model to specialize in different issue domains while maintaining total performance. 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 release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the thinking 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 describes a procedure of training smaller, more efficient models to imitate the habits and reasoning 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 suggest releasing this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and evaluate models against crucial security criteria. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security throughout your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 model, you need 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 confirm you're using 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 releasing. To request a limit boost, develop a limit increase demand and reach out 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 consents to use guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to introduce safeguards, prevent damaging material, and evaluate designs against essential safety criteria. You can implement safety steps for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and model responses 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 basic flow involves the following actions: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for reasoning. After receiving the model's output, 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 stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following sections show inference using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
1. On the Amazon Bedrock console, choose Model brochure under Foundation designs in the navigation pane.
At the time of composing 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 company and select the DeepSeek-R1 design.
The model detail page offers vital details about the model's capabilities, rates structure, and implementation guidelines. You can find detailed use guidelines, consisting of sample API calls and code bits for integration. The model supports various text generation jobs, consisting of material creation, code generation, and concern answering, utilizing its support discovering optimization and CoT thinking abilities.
The page also consists of implementation choices and licensing details to help you begin with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, choose Deploy.
You will be prompted to configure the implementation details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of instances, enter a number of instances (between 1-100).
6. For example type, pick your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can configure sophisticated security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function consents, and file encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you may wish to review these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to begin utilizing the design.
When the deployment is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in playground to access an interactive user interface where you can try out various triggers and adjust model specifications like temperature level and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal results. For instance, content for inference.
This is an exceptional method to check out the model's reasoning and text generation abilities before incorporating it into your applications. The play area offers instant feedback, assisting you comprehend how the model reacts to different inputs and letting you fine-tune your prompts for ideal results.
You can rapidly check the model in the play area through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
The following code example shows how to perform reasoning using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop 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 created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up inference parameters, and sends out a demand to produce text based upon a user prompt.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML services that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production using either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 practical techniques: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both methods to help you choose the method that finest 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 produce a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The model browser displays available models, with details like the provider name and model abilities.
4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each model card reveals crucial details, consisting of:
- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if suitable), suggesting that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design
5. Choose the model card to see the model details page.
The design details page consists of the following details:
- The design name and service provider 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 requirements.
- Usage guidelines
Before you deploy the design, it's suggested to evaluate the model details and license terms to validate compatibility with your usage case.
6. Choose Deploy to proceed with implementation.
7. For Endpoint name, utilize the automatically created name or create a custom one.
- For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, get in the variety of instances (default: 1). Selecting appropriate instance types and counts is important for cost and performance optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency.
- Review all setups for precision. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to release the model.
The release process can take several minutes to complete.
When deployment is total, your endpoint status will alter to InService. At this moment, the design is all set to accept inference requests through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the deployment is complete, you can invoke the model utilizing 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 set up the SageMaker Python SDK and make certain you have the necessary AWS consents and oeclub.org environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the model is offered 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 inference with your SageMaker JumpStart predictor
Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
Tidy up
To prevent undesirable charges, finish the steps in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace deployment
If you deployed the design utilizing Amazon Bedrock Marketplace, total the following actions:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace releases. - In the Managed deployments area, find 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 right 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 want to stop sustaining charges. For more details, see Delete Endpoints and Resources.
Conclusion
In this post, 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 start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, 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 business develop ingenious services using AWS services and accelerated calculate. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the reasoning efficiency of large language designs. In his totally free time, Vivek takes pleasure in treking, viewing movies, and attempting various foods.
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 Professional Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about building services that help consumers accelerate their AI journey and unlock company value.