By Saranya Ramesh
Machine Learning has become a necessity in every sector as a way of making machines intelligent while deep learning is gaining much popularity due to its superiority in terms of accuracy when trained with a huge amount of data. Whether we begin to start applying it to business, base our next project on it, or simply gain marketable skills – picking the right deep learning framework to learn is the essential first step towards reaching our goal.
Currently, the three most popular frameworks for Deep Learning are: Keras, an open source neural network library written in Python. It can run on top of TensorFlow, Microsoft Cognitive Toolkit, or Theano. It is designed to enable faster experimentation with deep neural networks and focuses on being user-friendly, modular and extensible. TensorFlow is an open-source software library for dataflow programming across a range of tasks. It’s a symbolic math library and is also used for machine learning applications such as neural networks. PyTorch is an open source machine learning library for Python, based on Torch, used for applications such as natural language processing. It is primarily developed by Facebook’s artificial-intelligence research group, and Uber’s “Pyro” software for probabilistic programming built on it. Each of them is suitable for different use cases.
Among the choice in terms of the libraries and frameworks available for machine learning/deep learning, these two libraries are the emerging frontrunners: Google’s TensorFlow, considered today’s best in class and the Facebook-backed Python package PyTorch, a new entrant that could compete in the field.“Google made TensorFlow to let you spend all your time googling TensorFlow document, and Facebook made PyTorch to let you have enough free time to browse Facebook.” – Anonymous
Yes, each of them has its own challenges revolving around two general use cases: training and inference. TensorFlow is built around the concept of Static Computational Graph (SCG), which means, first we define everything that is going to happen inside our framework and then run it. That is, in Define-and-Run framework, one would define conditions and iterations in the graph structure then run it. A network written in PyTorch is a Dynamic Computational Graph (DCG). PyTorch here is Define-by-run, wherein a graph structure is defined on-the-fly during forward computation; that’s a natural way of coding.
TensorFlow implementing this static graph has two great upsides: firstly, when a model becomes huge, it’s still easier to understand because everything is like a giant function that never changes and secondly, it’s always easier to optimize static computational graph because it allows for all kind of tricks like pre-allocating buffers, fusing layers, precompiling the functions, and so on.
On the other hand, a network written in PyTorch is Dynamic Computational Graph; and as the word “dynamic” suggests, it allows us to do anything we want to do. Like having any number of inputs at any given point of training in PyTorch – Lists, Stacks, no problem! Another interesting feature is, networks are modular. That means, each part is implemented separately which makes debugging easier, unlike in a monolithic TensorFlow construction. Thus, making PyTorch’s imperative style of programming very appealing. PyTorch was rewritten in Python due to the complexities of Torch. This makes PyTorch more native to developers. It has an easy to use framework that provides maximum flexibility and speed. It also allows quick changes within the code during training without hampering its performance. PyTorch is preferable even more because of its speed, efficiency, and ease of use. PyTorch includes custom-made GPU allocator, which makes deep learning models highly memory efficient. Due to this, training large deep learning models becomes easier.
PyTorch is known for providing two of the most high-level features: tensor computations with strong GPU acceleration support and building deep neural networks on tape-based auto-grad systems. PyTorch is known for advanced indexing and functions, imperative style, integration support and API simplicity. This is one of the key reasons why developers prefer PyTorch for research and hackability. Also, this is one of the many libraries that has the potential to change how deep learning and artificial intelligence are performed. The key reason behind PyTorch’s success is it’s completely Pythonic and one can build neural network models effortlessly. It is still a young player when compared to its other competitors, however, it is gaining momentum fast. Python. Because of its efficiency and speed, it’s a good option for small, research-based projects.