AWS Deep Learning Base AMI

Example(s):
- …
Counter-Example(s):
See: Machine Learning Framework, Machine Learning Model Deployment System, Ambient Intelligence (AMI).

References

(AWS Amazon,2019) ⇒ https://docs.aws.amazon.com/dlami/latest/devguide/overview-base.html Retrieved:2019-01-06
- QUOTE: Deep Learning Base AMI is like an empty canvas for deep learning. It comes with everything you need up until the point of the installation of a particular framework. It will have your choice of CUDA versions. (...) This AMI group is useful for project contributors who want to fork a deep learning project and build the latest. It's for someone who wants to roll their own environment with the confidence that the latest NVIDIA software is installed and working so they can focus on picking which frameworks and versions they want to install.

(AWS Amazon,2019) ⇒ https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-base.html Retrieved:2019-01-06
- QUOTE: The Base AMI comes with a foundational platform of GPU drivers and acceleration libraries to deploy your own customized deep learning environment. By default the AMI is configured with NVIDIA CUDA 9 environment. However, you can also switch to a CUDA 10 or CUDA 8 environment. Refer to the following instructions for how to do this.

(AWS Amazon,2019) ⇒ https://aws.amazon.com/marketplace/pp/B077GCZ4GR Retrieved:2019-01-06
- QUOTE: Deep Learning AMI with a foundational platform of NVIDIA CUDA, cuDNN, GPU drivers, Intel MKL-DNN, and other low-level system libraries for deploying your own custom deep learning environment.
  For example, for machine learning developers contributing to open source deep learning framework enhancements, the Deep Learning AMI provides a clean foundation for installing your custom configurations and forked repositories to test out new framework features. You could also be a Machine Learning / AI startup with a highly specialized deep learning setup that needs a foundation to run on a cloud-scale infrastructure.