AWS Deep Learning Containers

Quickly deploy deep learning environments with optimized, prepackaged container images

Deploy deep learning environments in minutes using prepackaged and fully tested Docker images.

Automatically improve performance with optimized model training for popular frameworks like TensorFlow, PyTorch, and Apache MXNet.

Quickly add machine learning (ML) as a microservice to your applications running on Amazon EKS and Amazon EC2.

Build custom ML workflows for training, validation, and deployment through integration with Amazon SageMaker, Amazon EKS, and Amazon ECS.

How it works

Deep Learning Containers are Docker images that are preinstalled and tested with the latest versions of popular deep learning frameworks. Deep Learning Containers lets you deploy custom ML environments quickly without building and optimizing your environments from scratch.

Diagram showing how AWS Deep Learning Containers helps users deploy custom ML environments and integrates with other AWS ML products

Use cases

Autonomous vehicle (AV) deployment

Develop advanced ML models at scale to deploy AV technology safely and quickly within your environments.

Natural language processing (NLP)

Reduce the time needed to deploy your ML models and accelerate time to production with up-to-date frameworks and libraries, including Hugging Face Transformers.

Healthcare data analysis

Analyze raw, disparate health data with advanced analytics, ML, and deep learning capabilities to identify trends and make predictions.

How to get started

Check out more resources

Explore the Deep Learning Containers documentation and tutorials.

Get started with a free account

Instantly get access to the AWS Free Tier.

Take the hands-on training

Get started with Deep Learning Containers on Amazon EC2.

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