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
AWS 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.
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.
Supported Deep Learning Containers
For details on the support for Deep Learning Containers, see the release notes.
| Frameworks: | PyTorch | TensorFlow | |||
| Operating systems: | Ubuntu Linux | ||||
| Instances: | NVIDIA GPUs | AWS Trainium | AWS Inferentia | ||
| Platforms: | Amazon EC2 | Amazon ECS | Amazon EKS | AWS Graviton |
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.