Quickly build scalable, secure deep learning applications in preconfigured environments

• Scale distributed machine learning (ML) training to thousands of accelerated instances and seamlessly deploy models for inference in production.
• Develop on accelerators—including AWS Trainium, AWS Inferentia, and NVIDIA GPUs—with the newest drivers, frameworks, libraries, and tools.
• Reduce risk with customized, stable machine images regularly patched to address security vulnerabilities.

Use cases

Autonomous vehicle development

Develop advanced ML models at scale to develop autonomous vehicle (AV) technology safely by validating models with millions of supported virtual tests.

Natural language processing

Accelerate the installation and configuration of AWS instances, and speed up experimentation and evaluation with up-to-date frameworks and libraries, including Hugging Face Transformers.

Healthcare data analysis

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

Accelerated model training

DLAMI includes the latest NVIDIA GPU acceleration through preconfigured drivers, the Intel Math Kernel Library (MKL), Python packages, and the Anaconda Platform.

How it works

AWS Deep Learning AMIs (DLAMI) provides ML practitioners and researchers with a curated and secure set of frameworks, dependencies, and tools to accelerate deep learning on Amazon EC2. Built for Amazon Linux and Ubuntu, Amazon Machine Images (AMIs) come preconfigured with TensorFlow, PyTorch, NVIDIA CUDA drivers and libraries, Intel MKL, Elastic Fabric Adapter (EFA), and AWS OFI NCCL plugin, allowing you to quickly deploy and run these frameworks and tools at scale.