That's definitely true, though worth noting that those libs didn't have anything close to the dominance the modern ones do.

I was doing ML academia stuff in the pre-TF era, and most of my department was using custom C++/CUDA, or a pile of random stuff like https://en.wikipedia.org/wiki/Weka_(machine_learning) and https://en.wikipedia.org/wiki/Waffles_(machine_learning).

Our department also didn't even have GPUs to use for the first part of my time there, whereas now it would seem ridiculous to have an ML research wing without access to GPUs.

I think the TensorFlow release roughly coincided with the widespread adoption of proper deep learning library, but it didn't cause it. In fact the first TensorFlow seemed to be just after (by 6 months to a year) the inflection point of deep learning library usage.

At the time, there seemed to be a lively ecosystem but no clear winner. Theano, Caffe and Torch were the main hitters but there were smaller players too. Torch was particularly good, maybe functionally ahead (but I don't really remember for sure) but hampered because, at the time, it was Lua only. In fact Keras's first release said "Keras is a minimalist, highly modular neural network library in the spirit of Torch, written in Python / Theano so as not to have to deal with the dearth of ecosystem in Lua."

When TensorFlow was released, it looked like that would be the death of all the other libraries. The fact that any others survived at all is really down to how hard to use TensorFlow was (/is?). It didn't live up to the expectation of being the killer of other libraries.