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 deploy DeepSeek AI's first-generation frontier model, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative AI ideas 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 steps to release the distilled versions of the designs too.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that utilizes reinforcement finding out to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential distinguishing feature is its reinforcement knowing (RL) action, which was used to fine-tune the model's actions beyond the standard pre-training and tweak procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, suggesting it's geared up to break down intricate questions and factor through them in a detailed way. This guided thinking 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 actions while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the market's attention as a versatile text-generation design that can be incorporated into numerous workflows such as representatives, logical thinking and information interpretation jobs.
DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion parameters, allowing effective inference by routing questions to the most relevant professional "clusters." This technique enables the model to focus on different problem domains while maintaining general effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 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 designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient designs to mimic the habits and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as a teacher model.
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 place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and examine models against essential safety criteria. 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 multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 design, 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 instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint usage. 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, create a limitation boost demand and reach out to your account team.
Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For guidelines, see Establish permissions to use guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to introduce safeguards, avoid harmful content, and examine models against crucial safety criteria. You can execute security measures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply 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 general circulation includes the following steps: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the check, it's sent out to the design for inference. After getting the design's output, another guardrail check is used. If the output passes this last check, it's returned as the result. 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 took place 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 offers you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane.
At the time of writing 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 company and choose the DeepSeek-R1 design.
The design detail page offers important details about the model's capabilities, rates structure, and execution guidelines. You can find detailed use guidelines, including sample API calls and code bits for combination. The design supports different text generation tasks, including material development, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT reasoning abilities.
The page also consists of release options and licensing details to assist you begin with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, pick Deploy.
You will be triggered to configure the implementation 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 number of instances (between 1-100).
6. For example type, choose your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up advanced security and facilities settings, including virtual personal cloud (VPC) networking, service role consents, and encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you might wish to examine these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to begin utilizing 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 interface where you can explore various prompts and change model criteria like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For instance, content for inference.
This is an exceptional method to explore the model's thinking and text generation capabilities before integrating it into your applications. The play area offers immediate feedback, helping you comprehend how the design responds to different inputs and letting you tweak your triggers for ideal results.
You can rapidly test the model in the playground through the UI. However, to conjure up the released design 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 demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using 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, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends 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 usage case, with your information, and release them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 convenient approaches: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you select the method that finest suits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
1. On the SageMaker console, pick Studio in the navigation pane.
2. First-time users will be prompted to produce a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
The model web browser displays available models, with details like the company name and design abilities.
4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each design card reveals key details, including:
- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if suitable), suggesting that this design can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the model
5. Choose the design card to see the design details page.
The design details page consists of the following details:
- The design name and provider details. Deploy button to release the design. About and Notebooks tabs with detailed details
The About tab consists of essential details, such as:
- Model description. - License details.
- Technical specifications.
- Usage standards
Before you deploy the model, it's suggested to examine the model details and license terms to verify compatibility with your usage case.
6. Choose Deploy to continue with implementation.
7. For Endpoint name, utilize the instantly created name or produce a customized one.
- For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, enter the number of instances (default: 1). Selecting appropriate instance types and counts is vital for expense and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency.
- Review all configurations for precision. For this model, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
- Choose Deploy to deploy the design.
The implementation process can take several minutes to finish.
When deployment is total, your endpoint status will alter to InService. At this point, the model is ready to accept inference demands through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the deployment is total, 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 need to set up the SageMaker Python SDK and make certain you have the necessary AWS authorizations and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for releasing the design is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
You can run extra 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 implement it as displayed in the following code:
Clean up
To avoid unwanted charges, finish the actions in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you deployed the design using Amazon Bedrock Marketplace, complete the following actions:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace deployments. - In the Managed releases area, locate the endpoint you want to erase.
- Select the endpoint, and on the Actions menu, pick Delete.
- Verify the endpoint details to make certain you're erasing the right implementation: 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 erase 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 begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, pipewiki.org Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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 construct innovative solutions using AWS services and accelerated calculate. Currently, he is focused on developing methods for fine-tuning and enhancing the inference efficiency of big language designs. In his leisure time, Vivek enjoys treking, seeing motion pictures, and attempting 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 Professional Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.
Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI center. She is passionate about constructing options that assist clients accelerate their AI journey and unlock business worth.