2 DeepSeek R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart

Enid Symons edited this page 2 months ago

Today, we are delighted to announce 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, along with the distilled versions ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative AI concepts on AWS.

In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the designs as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language model (LLM) developed by DeepSeek AI that utilizes reinforcement learning to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential distinguishing feature is its support knowing (RL) action, which was utilized to refine the model's actions beyond the standard pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, ultimately enhancing both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, suggesting it's equipped to break down complex inquiries and reason through them in a detailed manner. This directed thinking process allows the design to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has caught the industry's attention as a flexible text-generation model that can be integrated into numerous workflows such as agents, logical thinking and information analysis jobs.

DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, making it possible for efficient reasoning by routing questions to the most pertinent expert "clusters." This technique enables the model to focus on 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, wiki.snooze-hotelsoftware.de 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 models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more efficient designs to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor design.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and examine models against crucial safety requirements. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 model, 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, select 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 circumstances in the AWS Region you are deploying. To ask for a limit increase, create a limitation boost demand and reach out to your account group.

Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For guidelines, see Set up authorizations to use guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, avoid harmful material, and evaluate models against crucial safety criteria. You can carry out security steps for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and oeclub.org design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or kigalilife.co.rw the API. For the example code to produce the guardrail, see the GitHub repo.

The basic flow includes the following actions: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for reasoning. After getting 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 intervened by the guardrail, a message is returned suggesting the nature of the intervention and wavedream.wiki whether it occurred at the input or output phase. The examples showcased in the following sections demonstrate reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:

1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane. At the time of writing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 design.

The model detail page offers necessary details about the model's abilities, rates structure, and execution standards. You can discover detailed use directions, consisting of sample API calls and code bits for combination. The model supports various text generation tasks, consisting of material creation, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT reasoning capabilities. The page likewise consists of release choices and licensing details to help you get going with DeepSeek-R1 in your applications. 3. To begin utilizing DeepSeek-R1, select Deploy.

You will be prompted to configure the release details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). 5. For Number of instances, enter a number of instances (between 1-100). 6. For Instance type, pick your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. Optionally, you can set up innovative security and infrastructure settings, including virtual private cloud (VPC) networking, service role consents, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you might want to review these settings to line up with your organization's security and compliance requirements. 7. Choose Deploy to start using the design.

When the implementation is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. 8. Choose Open in play area to access an interactive interface where you can try out different triggers and adjust model specifications like temperature and maximum length. When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum results. For instance, content for reasoning.

This is an outstanding way to check out the model's thinking and text generation capabilities before integrating it into your applications. The play ground offers instant feedback, helping you understand how the model reacts to numerous inputs and letting you tweak your triggers for optimum outcomes.

You can rapidly test the model in the playground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint

The following code example demonstrates how to carry out inference utilizing a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and higgledy-piggledy.xyz 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 actually produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures inference parameters, and sends a demand to produce text based upon a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and garagesale.es prebuilt ML services that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart offers two hassle-free approaches: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to help you select the approach that best fits your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:

1. On the SageMaker console, choose Studio in the navigation pane. 2. First-time users will be prompted to create a domain. 3. On the SageMaker Studio console, choose JumpStart in the navigation pane.

The design web browser shows available models, with details like the provider name and model capabilities.

4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. Each model card reveals key details, including:

- Model name

• Provider name
• Task category (for example, Text Generation). Bedrock Ready badge (if suitable), pipewiki.org indicating that this model can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the model
  
  5. Choose the design card to view the design details page.
  
  The model details page includes the following details:
  
  - The design name and provider details. Deploy button to deploy the design. About and Notebooks tabs with detailed details
  
  The About tab consists of important details, such as:
  
  - Model description.
• License details.
• Technical requirements.
• Usage standards
  
  Before you release the model, it's recommended to examine the model details and license terms to verify compatibility with your usage case.
  
  6. Choose Deploy to continue with implementation.
  
  7. For Endpoint name, utilize the instantly produced name or create a custom-made one.

8. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, enter the variety of instances (default: 1). Selecting suitable circumstances types and counts is vital for expense and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency.
10. Review all setups for accuracy. For this design, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to deploy the model.
    
    The release procedure can take a number of minutes to complete.
    
    When deployment is complete, your endpoint status will change to InService. At this moment, the design is all set to accept reasoning demands through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation is total, you can conjure up the model utilizing a SageMaker runtime customer and incorporate it with your applications.
    
    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
    
    To start with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the needed AWS permissions and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for deploying the model is offered in the Github here. You can clone the notebook and run from SageMaker Studio.
    
    You can run extra demands against the predictor:
    
    Implement guardrails and run reasoning with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:
    
    Clean up
    
    To prevent unwanted charges, finish the actions in this section to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you deployed the model using Amazon Bedrock Marketplace, total the following steps:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations.
12. In the Managed releases area, find the endpoint you wish to delete.
13. Select the endpoint, and on the Actions menu, select Delete.
14. Verify the endpoint details to make certain you're erasing the appropriate release: 1. name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design you released will sustain expenses 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 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, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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 assists emerging generative AI companies build ingenious services using AWS services and sped up compute. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning performance of big language models. In his leisure time, Vivek delights in hiking, viewing films, and attempting various cuisines.
    
    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 collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative AI hub. She is passionate about building options that assist clients accelerate their AI journey and unlock business value.