What is PyTorch?
PyTorch is an open-source machine learning library based on the Torch library, primarily developed by Facebook’s AI Research lab, used for applications such as computer vision and natural language processing. It has a C++ interface.
What is TensorFlow?
TensorFlow is a free and open-source platform for numerical computation and machine learning. It is created by Google Brain team and is used for both research and production needs. The programming language used is C++ and Python.
The comparison between PyTorch and TensorFlow will be useful for people considering to start a new project.
Comparisions
- PyTorch is still in beta and is relatively new compared to its competitors. It is getting its momentum very fast. Official tutorials and documentation of PyTorch are also easy and nice.
TensorFlow is a go-to tool for industry professionals and researchers. Its framework is well documented and its tutorials on internet are also nicely written.
- PyTorch can be termed as exploited NumPy with the ability to use graphic card. Since we know that NumPy is so simple and easy it can be considered that PyTorch is very easy to learn and grasp.
In TensorFlow the graph is compiled first and then we get the actual graph output. TensorFlow is dependent when the compiled code is run using the TensorFlow Execution Engine.
- The code in PyTorch is known well and is easier to execute at fast speed. It turns out to be very efficient and you do not require to put any effort to learn the extra concepts.
In TensorFlow you need concepts like variable scoping, placeholders and sessions which is not preferred by most programmers.
- The Application Programming Interface can save all the weights of the model as well as pickle the entire class if you may.
In case of TensorFlow the entire graph can be saved as a protocol which includes parameters and operations as well.
- PyTorch is imperative and dynamic. You can easily define, change and execute nodes as you go no special session is needed. The framework is tightly integrated with Python language and feels native at times. PyTorch has support for dynamic inputs by default.
TensorFlow defines graph statically before a model run. The graph cannot be modified after compilation. TensorFlow has limited support for dynamic inputs through Tensorflow Fold.
- In PyTorch you can use Python debugging tools like pdb, ipdb, PyCharm debugger or old trusty print statements.
In TensorFlow you won’t be able to debug any Python code with any other debug tool except for pdb. You can use a special tool called tfdbg to evaluate tensorflow expressions at runtime and browse all tensors and operations in session scope but you wont be able to debug any code.
- There is no visualization tool as such for PyTorch. You can use some external tools.
In TensorFlow the visualization tool is lightweight and versatile tool. TensorBoard dashboard is convenient, flexible and dedicated to delivering visualizations. It can be used as a personal tool for tracking accuracy and loss.
- PyTorch is dominant in delivering the research work because of advancements in AI and ML. You can get Artificial Intelligence training and certification which will help you in decoding the mystery of artificial intelligence and its business application. The framework is very easy to modify or create new classes.
TensorFlow provides a stable and legible environment but it lacks flexibility required in experimental projects.
Both PyTorch and TensorFlow are great tools that are easy to use. Both frameworks have pros and cons. Both the frameworks are getting better and better upon their shortcomings because of great developers on each side. Its a difficult choice to accept that one is better than the other. The one which suits the project best is a better option. In my opinion I would prefer PyTorch to TensorFlow. Since, PyTorch is a pythonic framework and TensorFlow is a completely new language. PyTorch being simple to use makes the learning curve for developers short.
Sprintzeal provides simple yet powerful Application Programming Interface. Debugging is easier in PyTorch. PyTorch also has a very important feature known as data parallelism. This feature distributes computational work among multiple CPU or GPU cores. This feature can be used to wrap a module and do parallel processing over batch dimension. PyTorch provides a new hybrid front-end. There can be two modes of operation- eager mode and graph mode. Eager mode is used for research and development which provides flexibility. Graph mode is used for production because it provides better optimization, speed and functionality in C++ runtime environment.
