Today, we are excited 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 versions varying from 1.5 to 70 billion parameters to construct, experiment, wiki.asexuality.org and responsibly scale your generative AI ideas on AWS.
In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled versions of the designs too.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that utilizes reinforcement finding out to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial distinguishing function is its support knowing (RL) step, which was utilized to improve the design's actions beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately enhancing both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, suggesting it's equipped to break down complex queries and factor through them in a detailed way. This guided reasoning process enables the model to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has captured the market's attention as a flexible text-generation design that can be integrated into different workflows such as representatives, rational thinking and data analysis jobs.
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion specifications, making it possible for efficient inference by routing questions to the most pertinent specialist "clusters." This method enables the model to specialize in different problem domains while maintaining total effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective models to mimic the behavior 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 design, we advise deploying this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent damaging content, and evaluate models against essential security requirements. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To check 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 deploying. To ask for a limitation boost, create a limitation boost demand and reach out to your account group.
Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Establish permissions to utilize guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to present safeguards, avoid harmful content, and assess designs against crucial safety requirements. You can implement security measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and model actions deployed 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 produce the guardrail, see the GitHub repo.
The basic 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 inference. After receiving the design'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 showing the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas show reasoning using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 model.
The design detail page offers necessary details about the design's abilities, rates structure, and application standards. You can discover detailed usage guidelines, consisting of sample API calls and code snippets for combination. The model supports different text generation jobs, wiki.myamens.com including content production, code generation, and concern answering, utilizing its support finding out optimization and CoT reasoning abilities.
The page also includes implementation alternatives and licensing details to help you get started with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, choose Deploy.
You will be prompted to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters).
5. For Number of instances, enter a number of instances (between 1-100).
6. For Instance type, pick your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can configure sophisticated security and facilities settings, consisting of virtual private cloud (VPC) networking, service role approvals, and . For the majority of use cases, the default settings will work well. However, for production releases, you may wish to review these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to start using the model.
When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in playground to access an interactive interface where you can experiment with different prompts and change design criteria like temperature level and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For instance, material for reasoning.
This is an excellent method to explore the design's thinking and text generation abilities before integrating it into your applications. The playground provides instant feedback, assisting you comprehend how the model responds to numerous inputs and letting you tweak your prompts for ideal outcomes.
You can quickly evaluate the design in the playground through the UI. However, to conjure up the released model 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 inference utilizing a deployed 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 produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up inference parameters, and sends a request to produce text based upon 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 services that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production using either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart uses two hassle-free techniques: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you pick the approach that finest fits 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, select 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 web browser displays available designs, with details like the supplier name and model capabilities.
4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each model card shows essential details, consisting of:
- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if relevant), suggesting that this design can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design
5. Choose the design card to see the design details page.
The design details page includes 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 requirements.
- Usage guidelines
Before you deploy the model, it's recommended to review the model details and license terms to confirm compatibility with your use case.
6. Choose Deploy to continue with release.
7. For Endpoint name, use the immediately created name or develop a customized one.
- For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, enter the number of circumstances (default: 1). Selecting suitable circumstances types and counts is essential for expense and performance optimization. Monitor your implementation to adjust 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 accuracy. For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
- Choose Deploy to release the design.
The implementation 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 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 design utilizing a SageMaker runtime customer and integrate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need 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 deploy and utilize DeepSeek-R1 for inference 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:
Tidy up
To avoid unwanted charges, higgledy-piggledy.xyz complete the actions in this area to clean up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you deployed the model using Amazon Bedrock Marketplace, total the following actions:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations. - In the Managed deployments section, find the endpoint you desire to erase.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're erasing the proper deployment: 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 erase the endpoint if you wish 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 utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, pipewiki.org 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 helps emerging generative AI companies construct ingenious options utilizing AWS services and sped up compute. Currently, he is focused on establishing methods for fine-tuning and optimizing the inference performance of large language models. In his spare time, Vivek delights in hiking, watching films, and attempting different cuisines.
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 dealing with generative AI with the Third-Party Model Science team at AWS.
Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about developing services that help consumers accelerate their AI journey and unlock company worth.