Today, we are delighted to announce 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 criteria to construct, experiment, and responsibly scale your generative AI ideas on AWS.
In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the designs as well.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that uses support discovering to improve thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential distinguishing feature is its reinforcement learning (RL) action, which was utilized to fine-tune the model's reactions beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually enhancing both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, suggesting it's geared up to break down complex inquiries and factor through them in a detailed way. This assisted thinking procedure permits the model to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has recorded the market's attention as a versatile text-generation design that can be integrated into numerous workflows such as agents, rational thinking and data interpretation jobs.
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion specifications, making it possible for by routing questions to the most appropriate expert "clusters." This approach permits the design to specialize in various problem domains while maintaining total performance. DeepSeek-R1 requires at least 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 design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 model to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more effective models to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as an instructor design.
You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and assess designs against crucial 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, improving user experiences and standardizing security controls across your generative AI applications.
Prerequisites
To release 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, pick Amazon SageMaker, and confirm you're utilizing 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 releasing. To ask for a limit boost, produce a limitation boost demand and connect to your account team.
Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For directions, see Establish consents to utilize guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to present safeguards, avoid hazardous material, and examine designs against essential security requirements. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to examine user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
The general circulation includes 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 out to the model for reasoning. After getting the model's output, another guardrail check is used. If the output passes this last check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred 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 provides you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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 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 design detail page offers essential details about the design's abilities, rates structure, and execution guidelines. You can discover detailed use guidelines, consisting of sample API calls and code bits for integration. The design supports numerous text generation tasks, including material development, code generation, and concern answering, using its reinforcement finding out optimization and CoT thinking abilities.
The page likewise includes implementation choices and licensing details to assist you get going with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy.
You will be triggered to set up the release 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 Variety of circumstances, get in a variety of circumstances (between 1-100).
6. For Instance type, choose your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure innovative security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For most use cases, the default settings will work well. However, ratemywifey.com for production deployments, you may wish to evaluate these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to begin utilizing the design.
When the release is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in play area to access an interactive user interface where you can try out various prompts and adjust model specifications like temperature and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For example, content for reasoning.
This is an exceptional way to explore the model's thinking and text generation capabilities before integrating it into your applications. The play area provides instant feedback, helping you understand how the model responds to various inputs and letting you tweak your prompts for optimal results.
You can rapidly check the model 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 reasoning using guardrails with the released DeepSeek-R1 endpoint
The following code example demonstrates how to perform reasoning using a released DeepSeek-R1 model 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 develop the guardrail, see the GitHub repo. After you have produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures reasoning specifications, and sends out a demand to generate text based upon a user timely.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML solutions that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart provides two practical approaches: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you pick the approach that finest 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, choose JumpStart in the navigation pane.
The model browser shows available designs, with details like the company name and model capabilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each design card reveals essential details, including:
- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if applicable), showing that this design 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 model details page.
The model details page includes the following details:
- The design name and company details. Deploy button to release 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 standards
Before you release the design, it's recommended to examine the model details and license terms to confirm compatibility with your usage case.
6. Choose Deploy to continue with deployment.
7. For Endpoint name, utilize the instantly created name or create a customized one.
- For example type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, enter the variety of instances (default: 1). Selecting suitable circumstances types and counts is crucial for cost and efficiency optimization. Monitor your implementation 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 accuracy. For this model, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to deploy the design.
The deployment procedure can take numerous minutes to complete.
When deployment is total, your endpoint status will alter to InService. At this point, the model is ready to accept reasoning demands through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is complete, you can invoke the design utilizing a SageMaker runtime client and incorporate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
You can run additional demands against the predictor:
Implement guardrails and run reasoning with your SageMaker JumpStart predictor
Similar to Amazon Bedrock, you can likewise 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 steps in this section to tidy up your resources.
Delete the Amazon Bedrock Marketplace deployment
If you released the design utilizing Amazon Bedrock Marketplace, complete the following actions:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace implementations. - In the Managed implementations section, locate the endpoint you want to erase.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're deleting the right release: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model you deployed will sustain costs 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 release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe 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 companies construct ingenious options using AWS services and accelerated compute. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the reasoning efficiency of large language models. In his leisure time, Vivek enjoys hiking, seeing films, and trying different foods.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team 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 team at AWS.
Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about developing services that help customers accelerate their AI journey and unlock service worth.