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.
marvinalone|3 years ago
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.