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 release DeepSeek AI's first-generation frontier model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion parameters to build, experiment, and properly 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 similar actions to deploy the distilled variations of the models too.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that utilizes reinforcement finding out to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key identifying function is its reinforcement knowing (RL) step, which was utilized to fine-tune the design's reactions beyond the standard pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually enhancing both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, meaning it's equipped to break down complicated queries and factor through them in a detailed way. This guided thinking process permits the model to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation design that can be integrated into numerous workflows such as agents, sensible reasoning and information analysis jobs.
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion specifications, making it possible for effective inference by routing questions to the most relevant expert "clusters." This method allows the design to specialize in different issue domains while maintaining overall effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the model. 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 designs like Qwen (1.5 B, 7B, surgiteams.com 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient models to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher model.
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and examine models against crucial safety criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select 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 instance in the AWS Region you are releasing. To request a limit increase, develop a limit increase request and reach out to your account group.
Because you will be releasing this model 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 Establish consents to use guardrails for content filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging content, and examine models against essential security requirements. You can carry out precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and design responses deployed 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 produce the guardrail, see the GitHub repo.
The general circulation involves 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 to the design for reasoning. After getting the model's output, another guardrail check is applied. 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 took place at the input or output stage. The examples showcased in the following sections demonstrate 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 actions:
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 and pick the DeepSeek-R1 model.
The model detail page provides important details about the model's capabilities, prices structure, and implementation guidelines. You can find detailed usage instructions, consisting of sample API calls and code bits for combination. The design supports numerous text generation jobs, including content production, code generation, and question answering, using its reinforcement finding out optimization and CoT thinking capabilities.
The page also consists of deployment choices and licensing details to help you start with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, pick 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 Variety of circumstances, go into a number of instances (in between 1-100).
6. For example type, pick your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can set up sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service role approvals, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you might 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 implementation is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in play ground to access an interactive interface where you can explore different prompts and adjust model parameters like temperature level and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal results. For instance, material for reasoning.
This is an excellent way to check out the design's reasoning and text generation capabilities before incorporating it into your applications. The play ground supplies immediate feedback, helping you comprehend how the design reacts to different inputs and letting you tweak your prompts for ideal results.
You can quickly test the design in the play ground 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 using guardrails with the deployed DeepSeek-R1 endpoint
The following code example shows 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 produce the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures reasoning parameters, and sends a demand to generate text based on a user prompt.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub 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 models 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 provides 2 practical techniques: using the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both methods to assist you pick the approach that finest matches your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:
1. On the SageMaker console, pick 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 models, with details like the company name and design abilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each model card shows key details, consisting of:
- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if suitable), indicating that this model can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design
5. Choose the design card to see the model details page.
The model details page includes the following details:
- The design name and company 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 deploy the design, it's advised to review the model details and license terms to validate compatibility with your use case.
6. Choose Deploy to continue with deployment.
7. For Endpoint name, use the immediately produced name or develop a custom-made one.
- For example type ¸ pick an instance type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, go into the number of circumstances (default: 1). Selecting proper instance types and counts is vital for expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency.
- Review all setups for accuracy. For this design, we highly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
- Choose Deploy to release the design.
The release process can take several minutes to finish.
When implementation is complete, your endpoint status will alter to InService. At this point, the model is all set to accept inference requests through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the deployment is total, you can invoke the model utilizing a SageMaker runtime customer and integrate it with your applications.
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the necessary AWS authorizations 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 design is offered 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 likewise 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:
Clean up
To avoid unwanted charges, finish the steps in this area to tidy up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you released the model utilizing Amazon Bedrock Marketplace, total the following steps:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases. - In the Managed releases area, locate the endpoint you desire to erase.
- Select the endpoint, and on the Actions menu, select Delete.
- Verify the endpoint details to make certain you're erasing the proper release: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model you deployed will sustain costs 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 explored 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, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, 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 assists emerging generative AI business construct ingenious solutions using AWS services and accelerated calculate. Currently, he is focused on establishing strategies for fine-tuning and enhancing the reasoning performance of large language designs. In his spare time, Vivek enjoys treking, seeing motion pictures, and attempting various 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 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 product, engineering, and strategic partnerships for 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 service value.