Today, we are thrilled to announce that DeepSeek R1 distilled Llama and Qwen designs 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 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 demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled versions of the models as well.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that uses support learning to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial differentiating function is its reinforcement knowing (RL) action, which was used to fine-tune the model's actions beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, ultimately improving both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, suggesting it's equipped to break down intricate inquiries and reason through them in a detailed manner. This directed thinking procedure permits the design to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually caught the market's attention as a flexible text-generation design that can be incorporated into various workflows such as representatives, sensible thinking and information analysis tasks.
DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion criteria, allowing efficient inference by routing inquiries to the most relevant expert "clusters." This technique allows the design to focus on different issue domains while maintaining general effectiveness. DeepSeek-R1 requires 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 deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 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 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 simulate the behavior and thinking patterns of the bigger DeepSeek-R1 design, using it as a teacher model.
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and examine designs against key safety requirements. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative AI applications.
Prerequisites
To release the DeepSeek-R1 design, you require access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're using 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 limit boost, produce a limit 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 correct AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For directions, see Set up consents to utilize guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails allows you to present safeguards, prevent hazardous content, and assess models against key safety requirements. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and model responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
The basic 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 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 suggesting the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following areas show inference using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
1. On the Amazon Bedrock console, select Model brochure under Foundation models in the navigation pane.
At the time of composing this post, you can utilize 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 company and pick the DeepSeek-R1 model.
The design detail page offers important details about the model's capabilities, pricing structure, and implementation guidelines. You can find detailed usage guidelines, including sample API calls and code snippets for integration. The design supports different text generation tasks, including material production, code generation, and concern answering, utilizing its reinforcement discovering optimization and CoT reasoning capabilities.
The page likewise consists of implementation choices and licensing details to help you begin with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, select Deploy.
You will be triggered to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters).
5. For Number of instances, enter a variety of instances (between 1-100).
6. For example type, choose your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can set up innovative security and facilities settings, including virtual personal cloud (VPC) networking, service role authorizations, and encryption settings. For most use cases, the default settings will work well. However, for production implementations, you may wish to evaluate these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to start using the design.
When the deployment is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play ground.
8. Choose Open in play ground to access an interactive user interface where you can experiment with various prompts and adjust design criteria like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For instance, content for reasoning.
This is an outstanding method to explore the design's thinking and text generation abilities before incorporating it into your applications. The playground provides instant feedback, assisting you comprehend how the design reacts to numerous inputs and letting you fine-tune your triggers for optimum results.
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 need to get the endpoint ARN.
Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
The following code example demonstrates how to perform inference using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop 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 customer, configures reasoning criteria, and sends out a demand to create text based on a user prompt.
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 deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart provides two convenient methods: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you select the approach that best suits 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 produce a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
The design internet browser displays available models, with details like the supplier name and design abilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each model card shows crucial details, including:
- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if relevant), suggesting that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
5. Choose the model card to see the model details page.
The model details page includes the following details:
- The design name and provider details. Deploy button to release the model. About and Notebooks tabs with detailed details
The About tab includes crucial details, such as:
- Model description. - License details.
- Technical specifications.
- Usage standards
Before you release the design, it's advised to examine the design details and license terms to verify compatibility with your usage case.
6. Choose Deploy to proceed with deployment.
7. For Endpoint name, utilize the automatically generated name or develop a custom-made one.
- For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, go into the number of circumstances (default: 1). Selecting proper instance types and counts is crucial for expense and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency.
- Review all setups for accuracy. For this model, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
- Choose Deploy to deploy the model.
The release procedure can take several minutes to finish.
When implementation is complete, your endpoint status will change to InService. At this point, the design is ready to accept reasoning demands through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is total, you can invoke the model using a SageMaker runtime customer and incorporate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is provided in the Github here. You can clone the note pad and run from SageMaker Studio.
You can run extra requests 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 produce a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
Clean up
To prevent unwanted charges, finish the actions in this area to tidy up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you released the model using Amazon Bedrock Marketplace, total the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments. - In the Managed releases area, locate the endpoint you desire to delete.
- Select the endpoint, and on the Actions menu, select Delete.
- Verify the endpoint details to make certain you're deleting the correct implementation: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart design you deployed will sustain costs if you leave it running. Use the following code to erase the endpoint if you want 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 utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or wiki.dulovic.tech 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, 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 ingenious services utilizing AWS services and sped up calculate. Currently, he is focused on developing methods for fine-tuning and enhancing the reasoning performance of large language designs. In his downtime, Vivek delights in treking, viewing motion pictures, and trying different cuisines.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS AI (AWS Neuron). He holds a Bachelor's degree in Computer technology 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 tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about constructing solutions that assist customers accelerate their AI journey and unlock company worth.