PyTorch Pros and Cons

PyTorch developer-friendly easy to use and light weight framework it would not be wrong to say that it is a research based library.

By its NN feature i can run and train model on GPU with CPU which is very fast and much faster with pre-Trained networks some other featuer and libraries like Hugging Face transformers and torchvision is seamless.

Some Module like autograd and ONNX increase Interoperability to work with neural networks and open neural network exchange, and dataloader class support shuffing nad batching with parallel data loading.

PyTorch architectures is versatile for development and production also for research

Science i start using Pytorch insted of tensorflow for my computer vision project it provide me flexibility to model development phase and making easier to debugging. Review collected by and hosted on G2.com.

Core Pytorch documentation is very good but some other auxiliary libraries and newer features have very little or in complete documentation.

PyTorch is not effective if isn't enough data to train model , as model improvement and accuracy will not meet expectations. Review collected by and hosted on G2.com.

One of the things I really appreciate about PyTorch is how user friendly it is. It makes the complex realm of learning more accessible which is fantastic. The ability to experiment and make adjustments, to models on the go is truly revolutionary. It feels effortless to implement ideas thanks to its integration with Python and the dynamic computational graph that simplifies debugging. Moreover having a community and comprehensive documentation can be a lifesaver when facing challenges, in this field. Review collected by and hosted on G2.com.

Although PyTorch offers accessibility, in learning it can be a bit challenging for newcomers to the Python ecosystem. Deploying models beyond the stage can sometimes pose difficulties. Require additional effort, for a seamless transition. Furthermore the frequent updates while demonstrating progress may occasionally cause compatibility issues that demand attention and adaptation. Review collected by and hosted on G2.com.

It's is easy to use library which is very efficient for resources and provide the best documentation which makes it very easy for a beginner to start Review collected by and hosted on G2.com.

There is nothing to dislike about pytorch. It is the best deep learning Library out there. Review collected by and hosted on G2.com.

Pytorch is very simple to use and it has Python like syntax. It has a huge community base and forum from where we can get help instantly.

PyTorch 2.0 has now most of the state of the art models in NLP, Computer vision etc

Pytorch offers flexibility to tune it according to our use case Review collected by and hosted on G2.com.

I don't find any cons in PyTorch.

So far so good and they are headed in the right direction :) Review collected by and hosted on G2.com.

It is a very important deep learning framework to generate tensors in ML models and it is also compatible with GPU means model training can be very faster in terms of CPU with the help of PyTorch framework in Python as deep learning models would need lot of time for processing and also debugging is necessary for this models, hence PyTorch is very much compatible with the Numpy arrays and is dynamic in computation also. Review collected by and hosted on G2.com.

PyTorch is Pythonic but its functions and methods for Deep learning are somewhat hard to remember and also the documentation is not user friendly because it gets varies on the new version updates Review collected by and hosted on G2.com.

Pytorch is one of the easiest deep learning frameworks. It is very easy to define a model, set hyper parameters and launch training. The documentation around pytorch and the community is also quite active and most of the issues get resolved quite quickly once posted online. Review collected by and hosted on G2.com.

Pytorch lacks good monitoring and visualization tools, that is one advantage. Frameworks like TensorFlow have very nice visualization tools like tensorboard which can help in visualization and creation of good plots during the entire training procedure. Review collected by and hosted on G2.com.

The best thing about pytorch is that it makes debugging easy for developers.The errors get highlighted.Its the best replacement for tensorflow because of its less complexity. Review collected by and hosted on G2.com.

Though its easy to use but sometimes it lags some of the features of tensorflow.When applications gets bigger its speed to process decreases.This impacts its performance also which is not good. Review collected by and hosted on G2.com.

The distributed data parallelization and the controllability Review collected by and hosted on G2.com.

The dataloaders are very inefficient and cause a lot of bottlenecks Review collected by and hosted on G2.com.

You can use it with not only Python but also C++. It indicates that we can implement ML, DL and AI tools in future in faster compiling languages like C++, Java and C#, which will have a moderate learning curve with lesser system strain. Review collected by and hosted on G2.com.

It does not work well when you have to train a very small amount of data. On using small amount of data, you may find it out that PyTorch is not an optimal choice. Review collected by and hosted on G2.com.

The best thing about PyTorch is it is very developer-friendly and It is faster compared to another key frameworks like tensor flow. PyTorch is very helpful in terms of coding. Review collected by and hosted on G2.com.

What I disliked the most about PyTorch is support on error parts is not much available over the internet and the official documentation can be a little better for understanding. Review collected by and hosted on G2.com.