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When we started AllenNLP, PyTorch was just starting to emerge as a competitor to Tensorflow and we made the difficult decision to support PyTorch. In hindsight this was a great decision as the majority of top research is done in PyTorch today.

Tango primarily supports PyTorch, but unlike AllenNLP, is flexible enough to support other deep learning libraries as well. For example, we're adding support for JAX so we can easily leverage TPUs.

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For what I've seen Tango is a general dag/pipeline that happens to have some facilities for PyTorch. I don't see any deep learning specific. You could execute sklearn or whatever.

Maybe we need to re-work the docs if the DAG aspects stick out to you so much. The main functionality is the cache. If you have a complex experiment, you can still write the code as if all the steps were fast, and let them be slow only the first time you run it. The DAG stuff is also nice, but less important.

That said, you could execute sklearn. If that's what your experiment needs, it's the right thing to do. This is why it gives us the flexibility to also support Jax: https://github.com/allenai/tango/pull/313

The DL-specific stuff is in the components we supply. Like the trainer, dataset handling stuff, file formats, and increasingly, https://github.com/allenai/catwalk.

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