Top Deep Learning Frameworks in 2020: PyTorch vs TensorFlow
This article outlines five factors to help you compare these two major deep learning frameworks; PyTorch and TensorFlow.
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Deep learning is a sub-branch of machine learning. The unique aspect of Deep Learning is the accuracy and efficiency it brings to the table. When trained with a vast amount of data, Deep Learning systems can match, and even exceed, the cognitive powers of the human brain. How do the two top deep learning frameworks, i.e., PyTorch and TensorFlow, compare?
This article outlines five factors to help you compare these two major deep learning frameworks.
How Do PyTorch and TensorFlow Compare
Tensorflow is basically a programming language that is embedded within Python, as Sorrow Beaver notes. Tensorflow's code gets 'compiled' into a graph by Python. It is then run by the TensorFlow execution engine. Pytorch, on the other hand, is essentially a GPU enabled drop-in replacement for NumPy that is equipped with a higher-level functionality to build and train deep neural networks.
With Pytorch, the code executes very fast, it is very efficient, and you will require no new concepts to learn. Tensorflow, on the other hand, requires concepts such as placeholders, Variable scoping as well as sessions.
Graph Construction and Debugging
Pytorch has a dynamic process of creating a graph. Graphs on PyTorch can be built by interpreting a line of code corresponding to the particular aspect of a graph.
Tensorflow, on the other hand, has a static process of graph creation that involves graphs going through compilation and running on the execution engine.
Pytorch code will use the standard Python debugger, unlike TensorFlow, where you will need to learn the TF debugger and request the variables from the session for inspection.
Tensorflow supports features such as:
- Fast Fourier transforms
- Checking a tensor for NaN and infinity
- Flipping a tensor along a dimension
These are features that Pytorch doesn't have, but as it grows in popularity, the gap will definitely be bridged.
When comparing the two frameworks in serialization, TensorFlow's graph can be saved as a protocol buffer, which includes operations and parameters. The TensorFlow graph can then be loaded in other programming languages, such as Java and C++. This is important, especially for deployment stacks, where Python is not an option.
Pytorch, on the other hand, has a simple API that can either pickle the entire class or save all weights of a model.
All in all, saving and loading models are simplified in these two frameworks.
According to Sebagam, both TensorFlow and Pytorch are easy to wrap in for deployment in small-scale server-side. For mobile and embedded deployments, TensorFlow works efficiently, unlike with Pytorch. Less effort is therefore needed in TensorFlow deployment in Android and IOS, compared to Pytorch.
You will be required to bring a service down to hot-swap Pytorch, unlike during TensorFlow deployment.
Using these five factors, we can generally conclude that PyTorch does not differ so much from TensorFlow. They are both based on the Python programming language. Python APIs are very well documented; therefore, you will find ease using either of these frameworks.
Pytorch, however, has a good ramp up time and is therefore much faster than TensorFlow. Choosing between these two frameworks will depend on how easy you find the learning process for each of them. Your choice will also depend on your organization's requirements.
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