Deep Learning allows us to solve more complex problems and perform different tasks with great efficiency, constructing such technology is rather a critical task for data scientists and engineers. Instead, an expanding range of frameworks makes it easier to construct deep learning solutions of some complexity. Every framework is different and unique to its own kind, built with a different prospect and offers a unique range of feature.PyTorch and TensorFlow are amongst the most popular deep learning framework available.
TensorFlow provides with an open source software library which is used for numerical computation through data flow graphs. It was developed by Google Brain Team for the conducting machine learning and deep neural networks research. It consists of nodes and graph edges. Nodes represent mathematical operations, while the graph edges represent the multi-dimensional data arrays. The software library is written in Python, C++ and CUDA and supports the Linux, MacOS, Windows and Android platform. Among the others, one of the major application provided includes automated image captioning software, such as DeepDream.
PyTorch is a Python package with a different way of constructing the neural network. It provides Tensors and has the ability to enhance computation speed. At a basic level, it is a library comprises of the different components such as torch that support strong GPU support, torch.util, torch.autograd which supports all tensor operation and various other components. The framework is used as a replacement for numpy to utilise the power of GPU or a research platform which enhances flexibility and speed. It provides you with a feature to improve the way network behaves with zero lag or overhead and the technique is called Reverse-mode auto-differentiation. Intel MKL and NVIDIA are libraries used to maximize speed.
PyTorch VS TensorFlow
Comparing two most efficient and powerful framework, we come across some similarities as well as differences whose combination entirely makes them unique and serves a great number of purposes.
TensorFlow is developed and backed by Google Brain. PyTorch is a cousin of another framework which is actively used on Facebook. It is not just a simple set of wrappers but was rewritten and altered to enhance speed and efficiency.
Both frameworks operate on tensors and view any model as a directed acyclic graph (DAG)
TensorFlow is considered as a to-go tool and is very well documented. It also has a large number of tutorials which are easily available whereas PyTorch is relatively a new concept. It also includes implementations of computer vision architectures which are easy to use and provides great results.
Tensorflow has constricted and limited supports for dynamic inputs through Tensorflow Fold hand PyTorch possess the support by-default.
In TensorFlow graphs are defined statically before execution of a model. All actions which are related to the outer world is performed through tf.Session object and tf.Placeholder . In PyTorch the concepts are enhanced, improved and more dynamic. There is no need for special session interface or place holder for alterations in the node.
Tensorflow provides you with an option to utilise a special tool called tfdbg which enables the evaluation of TensorFlow expressions at runtime and browse all tensors and operations in session scope and in PyTorch as the computation graph is defined and elaborated at runtime one can choose Python debugging tools such as pdb, ipdb, PyCharm debugger.
PyTorch provides you with a more pythonic platform and object oriented approach whereas TensorFlow provides with a number of options among which one can choose.
TensorFlow is very efficient and powerful deep learning library. It provides strong visualization capabilities and is a matured platform. It provides with ready deployment options and is one of the favourable options for many instances whereas PyTorch is still a new framework. It is widely accepted and heavily used along with this it provides better development and debugging language but it is still developing and improving the framework.