Today, we are delighted 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 design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion specifications to construct, 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 steps to release the distilled variations of the models also.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that utilizes reinforcement discovering to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential identifying feature is its reinforcement learning (RL) step, which was utilized to improve the design's responses beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually boosting both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, indicating it's equipped to break down intricate queries and reason through them in a detailed way. This guided thinking process enables the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to create structured responses while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually caught the market's attention as a versatile text-generation design that can be integrated into different workflows such as agents, sensible reasoning and data analysis tasks.
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion parameters, enabling effective reasoning by routing questions to the most appropriate specialist "clusters." This technique enables the design to specialize in different problem domains while maintaining general effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 design to more efficient 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 sized, more effective models to simulate the habits and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as an instructor model.
You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and evaluate models against key security criteria. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 model, 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, choose Amazon SageMaker, and confirm you're using 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 limitation increase, develop a limit increase demand and reach out to your account group.
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) permissions to use Amazon Bedrock Guardrails. For directions, see Set up permissions to use guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to present safeguards, avoid damaging material, and evaluate designs against crucial safety requirements. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and model responses deployed 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 develop the guardrail, see the GitHub repo.
The basic circulation includes the following actions: 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 to the model 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 happened at the input or output stage. The examples showcased in the following sections show inference 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 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 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 supplier and choose the DeepSeek-R1 design.
The model detail page supplies vital details about the design's abilities, prices structure, and application standards. You can discover detailed usage instructions, consisting of sample API calls and code bits for integration. The model supports different text generation jobs, consisting of material development, code generation, and surgiteams.com concern answering, utilizing its support learning optimization and CoT thinking abilities.
The page also includes release alternatives 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 deployment details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of circumstances, get in a number of circumstances (in between 1-100).
6. For Instance type, select your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function permissions, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production implementations, you may wish to evaluate these settings to align with your company's security and compliance requirements.
7. Choose Deploy to start using the design.
When the deployment 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 user interface where you can experiment with various prompts and change model parameters like temperature level and maximum 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 reasoning and text generation capabilities before incorporating it into your applications. The play area offers immediate feedback, assisting you comprehend how the design responds to various inputs and letting you fine-tune your triggers for ideal results.
You can rapidly test the design in the play area through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run inference using guardrails with the deployed DeepSeek-R1 endpoint
The following code example shows how to perform reasoning using a deployed DeepSeek-R1 model 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 produce the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, 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) center with FMs, integrated 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 deploy them into production using either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart uses two practical techniques: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you pick the technique that finest fits 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 develop a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
The design web browser shows available models, with details like the company name and model capabilities.
4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each model card reveals essential details, including:
- Model name
- Provider name
- Task category (for pediascape.science example, Text Generation).
Bedrock Ready badge (if applicable), suggesting that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the design
5. Choose the model card to see the design details page.
The design details page includes the following details:
- The model name and company details. Deploy button to release the design. About and Notebooks tabs with detailed details
The About tab includes crucial details, such as:
- Model description. - License details. - Technical specs.
- Usage standards
Before you deploy the model, it's recommended to examine the design details and license terms to confirm compatibility with your usage case.
6. Choose Deploy to continue with deployment.
7. For Endpoint name, use the instantly generated name or create a customized one.
- For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, get in the number of instances (default: 1). Selecting proper instance types and counts is important for expense and performance optimization. Monitor your deployment to change 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 recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in .
- Choose Deploy to deploy the model.
The implementation process can take numerous minutes to finish.
When release is total, your endpoint status will change to InService. At this moment, the design is all set to accept inference demands through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is complete, you can invoke the design utilizing a SageMaker runtime client and incorporate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS consents and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the design 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 inference 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 utilizing the Amazon Bedrock console or the API, and implement it as revealed in the following code:
Tidy up
To avoid undesirable charges, finish the actions in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace release
If you deployed the design using Amazon Bedrock Marketplace, complete the following actions:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments. - In the Managed deployments area, find the endpoint you wish to erase.
- Select the endpoint, and on the Actions menu, select Delete.
- Verify the endpoint details to make certain you're deleting the right 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 erase the endpoint if you desire to stop sustaining charges. For more details, see Delete Endpoints and Resources.
Conclusion
In this post, we checked out how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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 build innovative solutions utilizing AWS services and accelerated compute. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the inference efficiency of large language designs. In his downtime, Vivek takes pleasure in treking, enjoying films, and trying different foods.
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 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 tactical partnerships for wiki.dulovic.tech Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about building solutions that help clients accelerate their AI journey and unlock company value.