Today, we are delighted to reveal 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 varying from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative AI ideas on AWS.
In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the models also.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that uses reinforcement discovering to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key identifying feature is its reinforcement knowing (RL) step, which was utilized to fine-tune the model's reactions beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and goals, ultimately improving both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, suggesting it's geared up to break down complicated queries and reason through them in a detailed way. This assisted reasoning process permits the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to create structured reactions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation design that can be integrated into various workflows such as representatives, rational thinking and information analysis tasks.
DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, enabling efficient inference by routing questions to the most pertinent specialist "clusters." This approach allows the model to specialize in different issue domains while maintaining general effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more effective models to imitate the habits and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor design.
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and evaluate designs against essential security criteria. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, enhancing 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 examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm 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 ask for a limit boost, create a limit increase request and reach out to your account group.
Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to utilize 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, avoid damaging material, and evaluate designs against key safety criteria. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and model responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
The general circulation involves 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 to the design for reasoning. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the outcome. 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 demonstrate inference utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
1. On the Amazon Bedrock console, choose Model brochure under Foundation models in the navigation pane.
At the time of writing this post, you can utilize the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 model.
The design detail page supplies important details about the design's abilities, pricing structure, and implementation standards. You can discover detailed use directions, including sample API calls and code bits for integration. The design supports numerous text generation jobs, consisting of content creation, code generation, and concern answering, using its support learning optimization and CoT thinking abilities.
The page likewise includes implementation choices and licensing details to help you get started with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, select Deploy.
You will be prompted to set up the release 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 Variety of instances, go into a variety of circumstances (in between 1-100).
6. For example type, choose your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure innovative security and facilities settings, including virtual private cloud (VPC) networking, service function approvals, and encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you might desire to review these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the design.
When the implementation is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in playground to access an interactive user interface where you can try out various triggers and adjust model criteria like temperature level and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For example, material for inference.
This is an excellent method to check out the design's thinking and text generation abilities before integrating it into your applications. The play area supplies instant feedback, helping you comprehend how the model reacts to various inputs and letting you tweak your triggers for ideal outcomes.
You can rapidly test the model in the play area through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
Run reasoning using guardrails with the released DeepSeek-R1 endpoint
The following code example shows how to perform reasoning utilizing a released 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 develop the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends out a request to generate text based on a user timely.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML services that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 model through SageMaker JumpStart offers two convenient techniques: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you choose the approach that finest fits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:
1. On the SageMaker console, pick Studio in the navigation pane.
2. First-time users will be prompted to create a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
The design browser shows available designs, with details like the service provider name and model capabilities.
4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each design card reveals key details, including:
- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if applicable), suggesting that this model can be registered with Amazon Bedrock, allowing 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 consists of the following details:
- The design name and service provider details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
The About tab consists of crucial details, such as:
- Model description. - License details.
- Technical specifications.
- Usage standards
Before you release the model, it's advised to review the design details and license terms to validate compatibility with your usage case.
6. Choose Deploy to proceed with release.
7. For Endpoint name, utilize the immediately generated name or produce a custom-made one.
- For example type ¸ pick an instance type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, get in the variety of instances (default: 1). Selecting appropriate instance types and counts is important for cost and performance optimization. Monitor your release to adjust 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 design, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
- Choose Deploy to deploy the model.
The implementation procedure can take numerous minutes to finish.
When implementation is complete, your endpoint status will alter to InService. At this point, the design is ready to accept inference demands through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the release is complete, you can conjure up the model using a SageMaker runtime client and integrate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To get begun with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that demonstrates how to release and oeclub.org use DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied in the Github here. You can clone the notebook 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 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 shown in the following code:
Tidy up
To prevent unwanted charges, finish the actions in this section to tidy up your resources.
Delete the Amazon Bedrock Marketplace release
If you released the design utilizing Amazon Bedrock Marketplace, total the following actions:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace implementations. - In the Managed releases section, 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 erasing the correct deployment: 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 delete 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 release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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 companies develop innovative options utilizing AWS services and sped up calculate. Currently, he is focused on developing strategies for fine-tuning and optimizing the inference efficiency of large language designs. In his leisure time, Vivek delights in hiking, seeing movies, and trying different foods.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
Jonathan Evans is an Expert 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 hub. She is enthusiastic about constructing options that help clients accelerate their AI journey and unlock service worth.