1 DeepSeek R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart
berenice87z485 edited this page 3 months ago


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, along with the distilled variations varying 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 get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) developed by DeepSeek AI that utilizes reinforcement finding out to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial differentiating function is its reinforcement learning (RL) step, which was used to improve the model's responses beyond the standard pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and setiathome.berkeley.edu goals, eventually enhancing both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, implying it's geared up to break down complicated inquiries and reason through them in a detailed manner. This assisted reasoning process allows the design to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation design that can be incorporated into different workflows such as agents, logical reasoning and data interpretation jobs.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion specifications, enabling efficient reasoning by routing queries to the most appropriate professional "clusters." This technique enables the design to concentrate on different problem domains while maintaining overall efficiency. DeepSeek-R1 needs a minimum of 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 includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 model 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 procedure of training smaller, more effective designs to simulate the habits and reasoning patterns of the larger DeepSeek-R1 model, using it as an instructor design.

You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid damaging material, and evaluate models against crucial safety criteria. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To release 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, 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 instance in the AWS Region you are releasing. To request a limit boost, develop a limit boost demand and connect to your account team.

Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For directions, see Establish approvals to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to introduce safeguards, avoid harmful content, and examine models against crucial safety requirements. You can implement safety steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.

The general flow includes 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 to the design for reasoning. After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the last result. 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 show reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:

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

The design detail page provides necessary details about the design's abilities, prices structure, and . You can discover detailed use instructions, including sample API calls and code bits for combination. The model supports different text generation jobs, consisting of content creation, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT thinking abilities. The page also includes implementation choices and licensing details to help you begin with DeepSeek-R1 in your applications. 3. To start utilizing DeepSeek-R1, choose Deploy.

You will be triggered to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). 5. For Variety of circumstances, enter a number of circumstances (between 1-100). 6. For example type, select your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. Optionally, you can set up advanced security and facilities settings, including virtual personal cloud (VPC) networking, service function permissions, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production implementations, you might wish to evaluate these settings to line up with your company's security and compliance requirements. 7. Choose Deploy to start utilizing the design.

When the release is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. 8. Choose Open in playground to access an interactive user interface where you can experiment with different prompts and adjust model parameters like temperature and optimum length. When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For instance, content for reasoning.

This is an exceptional way to explore the model's thinking and text generation capabilities before integrating it into your applications. The play area supplies immediate feedback, assisting you comprehend how the design reacts to various inputs and letting you tweak your triggers for optimal outcomes.

You can rapidly evaluate the design in the play area through the UI. However, to invoke the released design 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 inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create 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 created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference specifications, and sends out a demand to create text based upon 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 options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 hassle-free methods: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both methods to help you select the method that finest suits 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, choose Studio in the navigation pane. 2. First-time users will be prompted to create 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 service provider name and model abilities.

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

- Model name

  • Provider name
  • Task classification (for instance, Text Generation). Bedrock Ready badge (if applicable), indicating that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design

    5. Choose the model card to view the design details page.

    The design details page consists of the following details:

    - The model name and provider 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 guidelines

    Before you deploy 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 automatically generated name or produce a customized one.
  1. For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
  2. For Initial circumstances count, get in the variety of circumstances (default: 1). Selecting appropriate circumstances types and counts is vital for expense and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency.
  3. Review all setups for precision. For this model, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
  4. Choose Deploy to release the model.

    The release procedure can take numerous 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 monitor the deployment progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is complete, you can conjure up the model utilizing a SageMaker runtime client and integrate it with your applications.

    Deploy DeepSeek-R1 utilizing the SageMaker Python SDK

    To get started with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the design is offered in the Github here. You can clone the notebook and run from SageMaker Studio.

    You can run additional requests 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 using the Amazon Bedrock console or the API, and execute it as shown in the following code:

    Tidy up

    To avoid undesirable charges, complete the actions in this area to tidy up your resources.

    Delete the Amazon Bedrock Marketplace deployment

    If you released the design utilizing Amazon Bedrock Marketplace, complete the following actions:

    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments.
  5. In the Managed deployments section, find the endpoint you wish to erase.
  6. Select the endpoint, and on the Actions menu, pick Delete.
  7. Verify the endpoint details to make certain you're deleting the correct deployment: 1. Endpoint name.
  8. Model name.
  9. Endpoint status

    Delete the SageMaker JumpStart predictor

    The SageMaker JumpStart design you deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you desire 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 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 designs, 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 Inference at AWS. He assists emerging generative AI companies develop innovative solutions using AWS services and sped up compute. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the reasoning efficiency of big language models. In his downtime, Vivek delights in hiking, watching films, and attempting different foods.

    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location 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 working on generative AI with the Third-Party Model Science team 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 building services that assist customers accelerate their AI journey and unlock company value.