/Compare_Deep_Learning_Frameworks

This repo shows how to implement a training on CIFAR10 dataset with different Deep Learning frameworks: FastAI, JAX, Keras, MXNet, PaddlePaddle, Pytorch and Pytorch-lightning. An article was written to compare the ease of implementation (user friendly coding, ease of finding information online, etc.), time per epoch, memory and GPU usage, etc.

Primary LanguagePythonMIT LicenseMIT

Compare_Deep_Learning_Frameworks

Article link to read the full comparison between FastAI, JAX, Keras, MXNet, PaddlePaddle, Pytorch and Pytorch-lightning:

  • ease of implementation (user friendly coding, ease of finding information online, etc.),
  • time per epoch for the same model and the same training parameters,
  • memory and GPU usage (thanks to pytorch-profiler),
  • accuracy obtained after the same training.

How to use the code

compare_framework_colab.ipynb notebook allows to run a CIFAR10 training using any frameworks. Only the framework_name variable should be updated to switch between frameworks.

Open In Colab