AI Trends 2021–2025

One of the trends of 2021 that will continue at least for the next few years is the rise in popularity of the PyTorch framework. This can be seen from graphical data that the use of PyTorch has been steadily growing over the past few years, and though the popularity of the two frameworks has shown some correlation, their trends are different.

The differences between the two frameworks include:

- Dynamic versus static graph definition. At the same time, dynamism and flexibility are on the side of PyTorch.

- Debugging in PyTorch is possible using the standard python syntax, as in Tensorflow you need to use a special tool — tfdbg.

- Rendering is still on the side of Tensorflow thanks to its powerful Tensorboard tool. PyTorch contrasts the Tensor board with its own tool, Visdom. It doesn’t have many features, but it is easier to use. There is also the availability of integration with Tensorboard. In addition to these tools, you can use the standard visualization tools — matplotlib and seaborn. Of course, this group will continue to actively develop .

- So far, deployment also speaks in favor of TensorFlow. TF is currently the absolute winner in this respect, as it has a TensorFlow Serving framework for deploying a model on a special gRPC server.

- One of the main differences between PyTorch and TensorFlow is declarative data parallelization. Using torch.nn.DataParallel on any module, you can achieve almost magical data parallelism by the size of the batch. This way, the advantages of the GPU are used almost effortlessly.

- Finally, PyTorch feels more natural and ergonomic in Python and has an object-oriented approach, while TensorFlow has options from which you can choose what suits you, which more characterizes it as a library.

Summing up, TensorFlow still currently has advantages for deploying a model in production, support for mobile platforms, and visualization, however, these advantages may be significantly challenged in the near future.