There are various deep learning libraries but the two most famous libraries are PyTorch and Tensorflow. Though both are open source libraries but sometime it becomes difficult to figure out the difference between the two. They are extensively used in commercial code and academic research.
PyTorch:
It is an open-source library used in machine learning. It was developed by Facebook and was released to the public for the first time in 2016. It is imperative which means it runs immediately and the user can check if it is working or not before writing the full code. We can write a part of code and check it in real time, it is built-in python based implementation to provide compatibility as a deep learning platform. It rapidly gained users because of its user-friendly interface, which made the Tensorflow team acquire its popular features in Tensorflow 2.0.
TensorFlow:
Just like PyTorch, it is also an open-source library used in machine learning. It was developed by Google and was released in 2015. Its name itself expresses how you can perform and organize tasks on data. Production and research are the main uses of Tensorflow. Neural networks mostly use Tensorflow to develop machine learning applications.
PyTorch V/S TensorFlow
| S.No | Pytorch | TensorFlow |
|---|---|---|
| 1 | It was developed by Facebook | It was developed by Google |
| 2 | It was made using Torch library. | It was deployed on Theano which is a python library |
| 3 | It works on a dynamic graph concept | It believes on a static graph concept |
| 4 | Pytorch has fewer features as compared to Tensorflow. | Its has a higher level functionality and provides broad spectrum of choices to work on. |
| 5 | Pytorch uses simple API which saves the entire weight of model. | It has a major benefit that whole graph could be saved as protocol buffer. |
| 6 | It is comparatively less supportive in deployments. | It is more supportive for embedded and mobile deployments as compared to Pytorch |
| 7 | It has a smaller community. | It has a larger community. |
| 8 | It is easy to learn and understand. | It is comparatively hard to learn |
| 9 | It requires user to store everything into the device. | Default settings are well-defined in Tensorflow. |
| 10 | It has a dynamic computational process. | It requires the use of debugger tool. |
| 11 | Some of its features or libraries are: PYRO, Horizon, CheXNet, etc. | Some of its features or libraries are: Sonnet, Ludwig, Magenta, etc. |
Conclusion
It cannot be said that one library is good and one is bad, both are very useful frameworks and are used on a large scale. Both are machine learning libraries which are used to do various tasks. Tensorflow is a useful tool with debugging capabilities and visualization, It also saves graph as a protocol buffer. On the other hand Pytorch is still getting momentum and tempting python developers because of it’s friendly usage. In nutshell Tensorflow is used to automate things faster and make artificial intelligence related products whereas developers which are more research oriented prefer using Pytorch.
