PyTorch v/s Tensorflow
Over the past few decades, deep learning has made astonishing progress in the field of Artificial Intelligence. Several frameworks can get you started with Deep Learning. But selecting the right one for your project is a difficult task. While there are a lot of frameworks to pick from, PyTorch and TensorFlow are the two most commonly used for deep learning. Let’s look are some of the features, advantages, and disadvantages of one over the other, which framework to use when, and which one is best for your project.
Both the framework uses the basic fundamental data type called Tensor. Tensors are a multidimensional array that is capable of high-speed computations.
PyTorch: This Open Source deep learning framework was developed by the team of Facebook. The framework has support for Python and C++. PyTorch provides flexibility and allows DL models to be expressed in Python language.
TensorFlow: This open-source deep learning framework was developed by Google and was released in 2015. The framework is used to automate systems. The framework is fast, flexible provides distributed training support, scalability, and support for Android, servers using the lighter version i.e Tensorflow Lite which makes it the best suitable for research and production. Recent versions of Tensorflow comes with high-level API Keras integrated into it. Tensorflow modes are created using Keras. The API provides fast and easy prototyping as it offers readymade building blocks called layers.
Let’s look at some dissimilarities between PyTorch and Tensorflow.
Differences: PyTorch TensorFlow
Features Development Wizards Facebook developed PyTorch using the Torch library. TensorFlow was developed by Google and is based on Theano (Python library) Computation Graph PyTorch uses a dynamic computational graph where the computations are done line by line at the time when code is interpreted. Tensorflow uses the static computation graph i.e we have to define the computations that we want to do in a sequence and then run the Machine Learning Model. Debugging The dynamic approach of Pytorch where the computation graph is defined at the run time makes it eligible to use python debugging tools. The static computation graph of the TensorFlow makes it hard to debug. To debug the code in TensorFlow, we can use a tool called tfdgb, which can test and ‘evaluate the TensorFlow expression at the run time. Production PyTorch is easier to learn and lighter to work with, and hence, is relatively better for passion projects and building rapid prototypes. There are a lot of advantages that TensorFlow enjoys over PyTorch when it comes to production. Better performance due to the static computation graphs, packages/tools which help in fast development over platforms like browser, mobile, and cloud. Data Visualization PyTorch doesn’t have such a tool. Although we have tools like MatPlot library using which we can compare different training runs. Tensorflow comes up with a brilliant tool called TensorBoard which helps the user to visualize the Machine Learning Model, debug and compare different training runs i.e training a model and the tuning hyperparameters and then training again.TensorBoard can show both the runs at the same time to show the differences between them.
Which one is best?
Although the two frameworks are different in many aspects it’s very difficult to say which one is best. Some people find PyTorch better to use while others find TensorFlow better. However, both frameworks are best in their own way. Both frameworks provide useful abstractions to reduce code size and speed up production/development.
When to use which one? PyTorch TensorFlow
Research Production Models Better development and debugging tools. Models that need to be deployed on mobile phones. Python-like experience Models that require large-scale distributed training.
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