A big lesson I learned from PyTorch vs other frameworks is that productivity trumps incremental performance improvement. Both Caffe and MXNet marketed themselves for being fast, yet apparently being faster here and here by some percentage simply didn't matter that much. On the other hand, once we make a system work and make it popular, the community will close the performance gap sooner than competitors expect.
Another lesson is probably old but worth repeating: investment and professional polishing matters to open source projects. Meta reportedly had more than 300 (?) people working on PyTorch, helping the community resolve issues, producing tons of high-quality documentation and libraries, and marketing themselves nicely in all kinds of conferences and media.
Another lesson is probably old but worth repeating: investment and professional polishing matters to open source projects. Meta reportedly had more than 300 (?) people working on PyTorch, helping the community resolve issues, producing tons of high-quality documentation and libraries, and marketing themselves nicely in all kinds of conferences and media.