PyTorch Vs TensorFlow
There were far more differences in PyTorch vs TensorFlow when they were first released. Many of these inconsistencies have since been ironed out. There are still some disparities, however, which are worth looking at:
The limitations of TensorFlow's API was the first thing that prompted the creation of PyTorch in the first place. TensorFlow's API has since been updated quite a bit, but PyTorch was created specifically to port a machine learning library into the Python environment.
Computation graphs are some of the more significant differences between PyTorch and TensorFlow.
TensorFlow uses static computation graphs to allocate resources. It creates a graph for the series of calculations you want to perform. When resources are being allocated, it uses placeholder data to make its calculations.
The data is then plugged in, after the fact.
PyTorch uses dynamic computation graphs, on the other hand. This means that calculations are performed after each line of code is
Static computation graphs are easier on processors. They're a pain to debug, however, which makes dynamic computation preferable for a lot of applications.
In its earliest days, running TensorFlow across multiple devices or platforms was prohibitively difficult. You would have to fine-tune TensorFlow by hand for it to run smoothly in decentralized applications.
PyTorch doesn't have the same limitations. As with many of the other issues we've been discussing, TensorFlow has solved a lot of these issues in the ensuing years. For this particular issue, TensorFlow created Tensor Programming Units (TPU).
TPUs are even faster than GPUs and are now widely used and available. PyTorch isn't as adept at handling TPUs, but this can be addressed using 3rd party plugins like XLA.