Today, we are thrilled 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 deploy DeepSeek AI's first-generation frontier design, DeepSeek-R1, in addition to the distilled versions ranging 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 comparable actions to release the distilled variations of the designs as well.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language design (LLM) established by DeepSeek AI that uses reinforcement discovering to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key distinguishing feature is its support learning (RL) step, which was utilized to refine the design's actions beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, eventually boosting both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, meaning it's equipped to break down intricate queries and reason through them in a detailed manner. This guided reasoning procedure allows the model to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as representatives, sensible reasoning and information interpretation jobs.
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion criteria, allowing effective inference by routing queries to the most relevant professional "clusters." This approach permits the design to specialize in different problem domains while maintaining overall efficiency. 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 instance to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model to more efficient 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 sized, more efficient models to simulate the habits and thinking 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 place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and evaluate models against key safety requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 model, 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 inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify 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 request a limit boost, develop a limitation boost request and reach out to your account group.
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) authorizations to use Amazon Bedrock Guardrails. For instructions, see Establish approvals to utilize guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to present safeguards, avoid hazardous material, and evaluate designs against key safety requirements. You can implement safety steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and model responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
The general circulation includes the following steps: 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 design for reasoning. After receiving the model'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 stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections show reasoning utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
1. On the Amazon Bedrock console, choose Model catalog under Foundation models in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 design.
The design detail page offers necessary details about the model's abilities, pricing structure, and application guidelines. You can discover detailed use instructions, consisting of sample API calls and code snippets for integration. The model supports different text generation tasks, consisting of content creation, code generation, and question answering, using its support finding out optimization and CoT thinking abilities.
The page also consists of implementation alternatives and licensing details to assist you get begun with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, pick Deploy.
You will be prompted to configure the release details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of instances, enter a number of circumstances (between 1-100).
6. For example type, choose your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can set up innovative 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 releases, you might desire to examine these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start using the design.
When the implementation is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in playground to access an interactive user interface where you can try out various prompts and change model parameters like temperature level and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum outcomes. For instance, content for inference.
This is an exceptional method to check out the model's thinking and text generation abilities before incorporating it into your applications. The play area offers immediate feedback, helping you understand how the model reacts to different inputs and letting you tweak your triggers for ideal results.
You can rapidly test the model in the play ground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run inference using guardrails with the deployed DeepSeek-R1 endpoint
The following code example shows how to carry out reasoning utilizing a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends a request to produce text based on a user timely.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production using either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart offers two hassle-free techniques: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you select the method that best matches 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, pick Studio in the navigation pane.
2. First-time users will be triggered to create a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The design internet browser displays available models, with details like the service provider name and model abilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each model card reveals crucial details, including:
- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if applicable), suggesting that this model can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the model
5. Choose the design card to see the design details page.
The model details page includes the following details:
- The model name and provider details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
The About tab includes essential details, such as:
- Model description. - License details.
- Technical specifications.
- Usage standards
Before you deploy the model, it's advised to examine the design details and license terms to verify compatibility with your use case.
6. Choose Deploy to continue with implementation.
7. For Endpoint name, use the instantly generated name or create a custom one.
- For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge).
- For Initial instance count, go into the number of circumstances (default: 1). Selecting appropriate circumstances types and counts is vital for cost and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency.
- Review all configurations for precision. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to deploy the model.
The implementation procedure can take numerous minutes to complete.
When deployment is complete, your endpoint status will change to InService. At this point, the model is prepared to accept reasoning requests through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is total, you can conjure up the model using 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 require to install the SageMaker Python SDK and make certain you have the essential AWS approvals and engel-und-waisen.de environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for reasoning programmatically. The code for releasing the design is in the Github here. You can clone the notebook and run 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 use 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:
Clean up
To avoid undesirable charges, complete the actions in this section to tidy up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you released the design utilizing Amazon Bedrock Marketplace, total the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases. - In the Managed releases area, locate the endpoint you wish to erase.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're deleting the appropriate implementation: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model you deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you desire 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 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 designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started 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 options using AWS services and accelerated compute. Currently, he is concentrated on establishing strategies for fine-tuning and optimizing the reasoning efficiency of big language models. In his spare time, Vivek enjoys treking, watching films, and attempting various 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.
Jonathan Evans is a Professional Solutions Architect dealing with generative AI with the Third-Party Model Science team at AWS.
Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about constructing options that assist clients accelerate their AI journey and unlock business value.