Today, we are excited 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 model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative AI concepts 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 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 utilizes support discovering to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential differentiating feature is its reinforcement learning (RL) step, which was utilized to improve the model's reactions beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually improving both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, suggesting it's geared up to break down complex questions and reason through them in a detailed way. This assisted reasoning procedure enables the design to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation design that can be integrated into numerous workflows such as agents, logical reasoning and data analysis tasks.
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion parameters, enabling effective reasoning by routing questions to the most expert "clusters." This method allows the model to concentrate on different issue domains while maintaining general efficiency. DeepSeek-R1 requires 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 release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
DeepSeek-R1 distilled models 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, more effective models to simulate the habits and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as an instructor model.
You can deploy DeepSeek-R1 design 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, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and evaluate models against crucial security requirements. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across 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, 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 instance in the AWS Region you are deploying. To request a limitation increase, develop a limit increase request and connect to your account team.
Because you will be deploying this model 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 instructions, see Establish consents to utilize guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to present safeguards, avoid damaging content, and assess designs against essential safety requirements. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and model 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 flow includes 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 reasoning. After getting the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the last 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 occurred at the input or output phase. The examples showcased in the following areas show reasoning using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace offers 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 invoke 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 essential details about the model's abilities, prices structure, and implementation guidelines. You can find detailed use instructions, consisting of sample API calls and code bits for integration. The design supports different text generation jobs, consisting of material creation, code generation, and question answering, using its reinforcement finding out optimization and CoT reasoning abilities.
The page also includes implementation alternatives and licensing details to assist you get begun with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, select Deploy.
You will be prompted to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of circumstances, enter a variety of circumstances (between 1-100).
6. For Instance type, pick your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service function consents, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you may want to examine these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to begin using the design.
When the release is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in play ground to access an interactive interface where you can experiment with different prompts and change model specifications like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For instance, content for reasoning.
This is an exceptional method to check out the model's reasoning and text generation capabilities before integrating it into your applications. The playground offers immediate feedback, assisting you understand how the design reacts to various inputs and letting you fine-tune your prompts for ideal outcomes.
You can rapidly evaluate the design in the playground through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
The following code example shows how to perform reasoning utilizing a released DeepSeek-R1 model 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 create the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, 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) center 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 models to your usage case, with your data, and deploy them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart provides two practical methods: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you select the approach that best fits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
1. On the SageMaker console, choose Studio in the navigation pane.
2. First-time users will be prompted to develop a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The model internet browser shows available designs, with details like the provider name and model capabilities.
4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each design card reveals crucial details, including:
- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if suitable), indicating that this design can be registered with Amazon Bedrock, enabling 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 model name and supplier details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
The About tab includes important details, such as:
- Model description. - License details.
- Technical requirements.
- Usage standards
Before you release the design, it's suggested to evaluate the design details and license terms to verify compatibility with your usage case.
6. Choose Deploy to proceed with implementation.
7. For Endpoint name, utilize the instantly generated name or produce a custom one.
- For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, get in the number of instances (default: 1). Selecting appropriate circumstances types and counts is crucial for cost and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency.
- Review all configurations for precision. For this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
- Choose Deploy to release the model.
The release procedure can take a number of minutes to complete.
When implementation is complete, your endpoint status will change to InService. At this point, the design is prepared to accept inference requests through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is total, you can invoke the design utilizing a SageMaker runtime client and integrate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To get started with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the needed AWS consents and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is supplied in the Github here. You can clone the note pad and range 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
Tidy up
To avoid undesirable charges, finish the actions in this area to clean up your resources.
Delete the Amazon Bedrock Marketplace deployment
If you deployed the design using Amazon Bedrock Marketplace, total the following steps:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace deployments. - In the Managed implementations area, find the endpoint you wish to delete.
- Select the endpoint, and on the Actions menu, pick Delete.
- Verify the endpoint details to make certain you're deleting the appropriate release: 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 start. For more details, yewiki.org describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.
About the Authors
Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI business build innovative options using AWS services and sped up compute. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the reasoning efficiency of large language designs. In his spare time, Vivek takes pleasure in treking, seeing films, and trying various cuisines.
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 Science and Bioinformatics.
Jonathan Evans is a Professional Solutions Architect dealing with generative AI with the Third-Party Model Science group 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 developing services that help consumers accelerate their AI journey and unlock organization value.