Today, we are delighted 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 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 show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled versions of the models as well.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that utilizes reinforcement learning to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial distinguishing feature is its support knowing (RL) action, which was utilized to fine-tune the model's responses beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, indicating it's geared up to break down intricate questions and reason through them in a detailed way. This assisted reasoning procedure allows the design to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has recorded the market's attention as a versatile text-generation design that can be incorporated into numerous workflows such as agents, logical reasoning and information interpretation tasks.
DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion specifications, allowing efficient inference by routing queries to the most appropriate expert "clusters." This approach enables the design to specialize in various problem domains while maintaining total efficiency. DeepSeek-R1 requires a minimum of 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 features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more efficient designs to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as an instructor model.
You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, wakewiki.de we suggest releasing this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and evaluate designs against key safety requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative AI applications.
Prerequisites
To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick 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 boost, systemcheck-wiki.de produce a limit boost demand and reach out to your account team.
Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For directions, see Establish permissions to utilize guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to introduce safeguards, prevent harmful material, and evaluate models against essential security criteria. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and model actions 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 basic circulation involves the following actions: 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 out to the design for inference. After getting the model's output, another guardrail check is used. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting 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 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, complete the following steps:
1. On the Amazon Bedrock console, choose Model catalog under Foundation designs in the navigation pane.
At the time of composing this post, you can use 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 provider and pick the DeepSeek-R1 model.
The model detail page supplies necessary details about the design's abilities, prices structure, and application guidelines. You can find detailed usage instructions, consisting of sample API calls and code snippets for combination. The model supports numerous text generation tasks, consisting of material production, code generation, and question answering, using its reinforcement discovering optimization and CoT reasoning capabilities.
The page likewise consists of release choices and licensing details to help you get begun with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, select Deploy.
You will be prompted to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, wavedream.wiki enter an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of circumstances, get in a variety of circumstances (in between 1-100).
6. For example type, select 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 advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service role consents, and file encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you may wish to review these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to start utilizing the model.
When the implementation is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in play ground to access an interactive user interface where you can try out various triggers and change model criteria like temperature level and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For instance, material for inference.
This is an excellent way to explore the model's reasoning and text generation capabilities before integrating it into your applications. The play ground offers immediate feedback, helping you understand how the model reacts to numerous inputs and letting you tweak your triggers for optimal outcomes.
You can rapidly evaluate the model 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 inference using guardrails with the released DeepSeek-R1 endpoint
The following code example shows how to carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce 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 execute guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, and sends a demand to generate text based on a user timely.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub 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 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 uses two hassle-free methods: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to assist you select the technique that finest fits your needs.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:
1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be prompted to develop a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The model internet browser shows available designs, with details like the provider name and design capabilities.
4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each design card shows crucial details, including:
- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if relevant), indicating that this design can be signed up with Amazon Bedrock, enabling you to utilize 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 company details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
The About tab includes crucial details, such as:
- Model description. - License details.
- Technical requirements.
- Usage standards
Before you release the design, it's recommended to examine the design details and license terms to verify compatibility with your usage case.
6. Choose Deploy to continue with release.
7. For Endpoint name, use the instantly generated name or create a customized one.
- For example type ¸ choose an instance type (default: ml.p5e.48 xlarge).
- For Initial circumstances count, get in the variety of circumstances (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 optimized for sustained traffic and low latency.
- Review all setups for accuracy. For this design, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
- Choose Deploy to release the model.
The deployment process can take numerous minutes to finish.
When release is total, your endpoint status will change to InService. At this moment, the model is all set to accept inference demands 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 conjure up the design utilizing a SageMaker runtime client and integrate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS consents and . The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is supplied 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 also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
Clean up
To prevent undesirable charges, complete the actions in this section to tidy up your resources.
Delete the Amazon Bedrock Marketplace release
If you deployed the design using Amazon Bedrock Marketplace, total the following actions:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace implementations. - In the Managed implementations area, find the endpoint you wish to erase.
- Select the endpoint, and on the Actions menu, choose Delete.
- Verify the endpoint details to make certain you're deleting the right release: 1. Endpoint name.
- Model name.
- Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model you released 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 checked out 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 get going. 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 Starting 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 develop innovative solutions using AWS services and sped up compute. Currently, he is focused on developing techniques for fine-tuning and enhancing the inference performance of large language models. In his totally free time, Vivek takes pleasure in treking, viewing movies, and attempting different foods.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area 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 item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is enthusiastic about constructing solutions that help customers accelerate their AI journey and unlock company worth.