Today, we are thrilled 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 release DeepSeek AI's first-generation frontier model, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion specifications to build, 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 comparable actions to deploy the distilled variations of the models as well.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that utilizes support learning to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential differentiating function is its support learning (RL) action, which was utilized to fine-tune the model's actions beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust more efficiently to user feedback and higgledy-piggledy.xyz objectives, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, meaning it's equipped to break down intricate inquiries and factor through them in a detailed way. This assisted thinking process permits the model to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has recorded the industry's attention as a flexible text-generation design that can be integrated into numerous workflows such as agents, rational thinking and data analysis tasks.
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient reasoning by routing questions to the most appropriate specialist "clusters." This technique permits the model to concentrate on various problem domains while maintaining total efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient designs to imitate the habits and thinking patterns of the larger DeepSeek-R1 model, using it as a teacher 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, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and examine designs against key safety requirements. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative AI applications.
Prerequisites
To deploy the DeepSeek-R1 design, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick 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 increase, create a limit increase request and reach out to your account team.
Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For instructions, see Set up permissions to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging content, and examine designs against crucial safety criteria. You can implement safety steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and design reactions released on Amazon Bedrock Marketplace and wiki.snooze-hotelsoftware.de 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 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 design for inference. After receiving the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections show reasoning utilizing this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace gives 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 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 conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.
The model detail page provides essential details about the design's abilities, pricing structure, and application guidelines. You can discover detailed use directions, including sample API calls and code snippets for integration. The design supports various text generation tasks, consisting of content production, code generation, and question answering, utilizing its support finding out optimization and CoT thinking abilities.
The page also consists of deployment choices and licensing details to assist you get begun with DeepSeek-R1 in your applications.
3. To begin utilizing 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, get in an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of circumstances, enter a number of instances (between 1-100).
6. For Instance type, select your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can set up innovative security and infrastructure settings, including virtual private cloud (VPC) networking, service role permissions, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production releases, you may wish to evaluate these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to begin using the model.
When the release is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground.
8. Choose Open in playground to access an interactive user interface where you can explore different prompts and adjust design criteria like temperature and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For example, material for reasoning.
This is an exceptional way to explore the model's reasoning and text generation capabilities before integrating it into your applications. The play area offers immediate feedback, assisting you understand how the model reacts to numerous inputs and letting you fine-tune your prompts for ideal outcomes.
You can quickly evaluate the model in the play area through the UI. However, to invoke the deployed design 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 inference utilizing a deployed DeepSeek-R1 design 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 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, sets up reasoning parameters, and sends a request to produce text based upon 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 few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production utilizing either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart offers two hassle-free techniques: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you select the approach that best 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 produce a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The design internet browser displays available designs, with details like the company name and design capabilities.
4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each design card reveals crucial details, consisting of:
- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if appropriate), showing that this design can be signed up with Amazon Bedrock, allowing you to use 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 model name and supplier details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
The About tab consists of essential details, such as:
- Model description. - License details.
- Technical requirements.
- Usage guidelines
Before you deploy the model, it's advised to examine the design details and license terms to validate compatibility with your usage case.
6. Choose Deploy to proceed with implementation.
7. For Endpoint name, utilize the instantly generated name or produce a custom-made one.
- For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
- For Initial instance count, go into the variety of circumstances (default: 1). Selecting suitable instance types and counts is essential for expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency.
- Review all setups for precision. For this model, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
- Choose Deploy to release the design.
The deployment procedure can take several minutes to finish.
When deployment is complete, your endpoint status will change to InService. At this point, the design is all set to accept reasoning requests through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is complete, you can conjure up the design utilizing a SageMaker runtime customer and integrate it with your applications.
Deploy DeepSeek-R1 utilizing 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 needed AWS consents and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for reasoning 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as revealed in the following code:
Tidy up
To prevent undesirable charges, complete the actions in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace deployment
If you released the model utilizing Amazon Bedrock Marketplace, total the following actions:
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace implementations. - In the Managed implementations area, locate the endpoint you wish to delete.
- Select the endpoint, and on the Actions menu, pick Delete.
- Verify the endpoint details to make certain you're erasing the right deployment: 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 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 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, 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 at AWS. He helps emerging generative AI companies build ingenious services utilizing AWS services and sped up calculate. Currently, he is concentrated on developing strategies for fine-tuning and enhancing the reasoning performance of large language models. In his totally free time, Vivek enjoys treking, seeing motion pictures, 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 Science and Bioinformatics.
Jonathan Evans is an Expert Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about constructing options that help customers accelerate their AI journey and unlock organization value.