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Use PyTorch for ML/DL

Context and Problem Statement​

We need a mainstream framework for our deep learning.

Decision Drivers​

  • Widely Used
  • Rich Community
  • Python
  • Support for large datasets

Considered Options​

  • PyTorch
  • TensorFlow
  • Keras

Decision Outcome​

For now, we are sticking with TensorFlow with the goal of moving to PyTorch.

TODO: Add more info

Details​

This is an apple 🍎

Consequences​

  • Good, because {positive consequence, e.g., improvement of one or more desired qualities, …}
  • Bad, because {negative consequence, e.g., compromising one or more desired qualities, …}
  • …

Validation​

{describe how the implementation of/compliance with the ADR is validated. E.g., by a review or an ArchUnit test}

Pros and Cons of the Options​

{title of option 1}​

{example | description | pointer to more information | …}

  • Good, because {argument a}
  • Good, because {argument b}
  • Neutral, because {argument c}
  • Bad, because {argument d}
  • …

{title of other option}​

{example | description | pointer to more information | …}

  • Good, because {argument a}
  • Good, because {argument b}
  • Neutral, because {argument c}
  • Bad, because {argument d}
  • …

More Information​

{You might want to provide additional evidence/confidence for the decision outcome here and/or document the team agreement on the decision and/or define when this decision when and how the decision should be realized and if/when it should be re-visited and/or how the decision is validated. Links to other decisions and resources might here appear as well.}