What is PyTorch?.
PyTorch is a Python-based scientific computing package utilizes the power of graphics processing units. It is also one of the preferred deep learning research platforms built to provide maximum flexibility and speed. The workflow of PyTorch is as close as you can get to python’s scientific computing library based on Torch,[1][2][3] it is used in applications such as natural language processing. It provides two high-level features:
- Tensor computation (like numpy) with strong GPU acceleration.
- Deep Neural Networks built on a tape-based autodiff system.
| Maintained by | License Type | Popular Examples | Support | Updates | Developer Skills |
|---|---|---|---|---|---|
| PyTorch core team | BSD 2 | – | pytorch.org/support/ | – | Python, C++, CUDA |
| Often Compared to | Testing | Accessibility | Maintained by | Repository |
|---|---|---|---|---|
| Tensorflow, Keras | – | – | PyTorch core team | github.com/pytorch/pytorch |
Pros:
- Modeling process is simple and transparent.
- Features a lot of pretrained models and modular parts that are ready and easy to combine.
- Common debugging tools as pdb, ipdb or PyCharm can be used for debugging.
Cons:
- It lacks model serving.
- Lacks interfaces for monitoring and visualization such as Tensorboard – though you can connect externally.