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Amazon Deep Learning AMIs

Score8.1 out of 10

13 Reviews and Ratings

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What is Amazon Deep Learning AMIs?

AMIs are Amazon Machine Images, virtual appliance deployed on EC2. The AWS Deep Learning AMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at scale. Users can launch Amazon EC2 instances pre-installed with deep learning frameworks and interfaces such as TensorFlow, PyTorch, Apache MXNet, Chainer, Gluon, Horovod, and Keras to train sophisticated, custom AI models, experiment with new algorithms, or to learn new skills and techniques.

Categories & Use Cases

Reviews

What users are saying about Amazon Deep Learning AMIs.
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No need to fear from Cuda and Nvidia installation

Incentivized
Verified User
Employee in Research & Development (51-200 employees employees)

Use Cases and Deployment Scope

We use AWS EC2 for training machine learning models. For spinning up these instances, we use prebuilt AMIs specific for Deep Learning. These AMIs help a lot in setting up the environment very easily without any worries of installing CUDA, Nvidia Drivers, and then specific libraries like PyTorch, and Tensorflow. Support for various Conda environments is also available. The amazing part of this is that it provides support for different types of machines like Ubuntu, Linux, Amazon OS, etc.

Pros

  • Setting up environment
  • Support for different types of machines
  • Perfect for Machine Learning / Deep Learning use cases
  • Nvidia / Cuda / Conda support easily

Cons

  • Simpler documentation of different types of AMIs
  • Clearly listing out different types of machines as I got confused and spinned up an AMI in Amazon Linux machine instead of Ubuntu
  • Support for latest version of libraries, to avoid manually updating them after launch

Most Important Features

  • Nvidia / Cuda drivers
  • Conda environment
  • Support for Ubuntu (multiple versions)

Return on Investment

  • Saves a lot of Infra Costs
  • Saves a lot of time in handling environment issues
  • Easy to start a new instance

Other Software Used

Asana, Atlassian Confluence, GitHub, Microsoft Visual Studio Code, Slack, Weights & Biases, Pytorch, TensorFlow

Amazon Deep Learning - A nifty service for all your deep learning needs!

Incentivized
Verified User
Employee in Information Technology (1001-5000 employees employees)

Pros

  • You can get several common packages including keras, pytorch and tensorflow setup within an environment ready to code on any AWS instance which saves time
  • Great for virtual applications that helps communicate between various pieces of software
  • Not need to worry about compatibility or any major aspects of setup e.g. GPU configuration

Cons

  • Some aspects of the User Interface are quite confusing and activating packages can be a bit convoluted
  • It can be a bit confusing to switch between frameworks for novice users

Return on Investment

  • It has made our Data Science/ Machine Learning Courses easier to manage/ need less human input therefore allowing us to increase the cohort size for this degree
  • It has unified a lot of technologies reducing the load on our IT team

Alternatives Considered

IBM Watson Machine Learning

Other Software Used

IBM Watson Machine Learning, Azure AI (Cortana), Azure Lab Services