In this article, we’ll compare the different aspects of Pytorch vs Tensorflow vs Keras libraries for our machine learning needs.
During the process of working on ML and AI models, there’s always a doubt – Are you using the Right Framework?
Questioning as to whether you’re working with the best framework or you should just switch out.
In the process of doing so, you might be wasting a lot of time that could be used on working with the models rather than going through the syntax.
In the end, you might even find that you did like it, but, sometimes you don’t.
Quick Comparison Between Pytorch vs Tensorflow vs Keras
|Datasets||Best for small data||Large data without performance issues||Large data without performance issues|
|Debugging||Debugging is rarely necessary due to the simplicity of the module||Strong debugging features||Complex to debug Tensorflow code|
|Trained models for testing?||Yes||Yes||Yes|
|Popularity||Most popular||Third most popular||Second most popular|
|Language||Python||Lua||C++, CUDA, Python|
Installation Steps for Pytorch – Tensorflow – Keras
Before we begin comparing the features, it might be a good idea to know where to go back to, in case you have a change of heart.
So, here’s a few sections that can help you get started with the different frameworks,
PyTorch is an open source machine learning library based on the Torch library, used to create applications on computer vision and natural language processing, primarily developed by Facebook’s AI Research lab.
Below are the installation instructions for the package manager pip.
It might make sense to use the Anaconda package manager for Data Science,
Here’s the command to install PyTorch and it’s libraries,
pip install torch===1.7.1 torchvision===0.8.2 torchaudio===0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
Here’s the Documentation to work with PyTorch.
TensorFlow is a free and open-source software library for machine learning.
While working with TensorFlow, it is important to note it’s strengths.
It can be used in a variety of tasks, but, TensorFlow has a particular focus on training and inference of deep neural networks.
Here’s the command to install TensorFlow and it’s libraries,
pip install tensorflow
Here’s the Documentation to work with TensorFlow.
Being an open-source software library that provides a Python interface for artificial neural networks. Keras mostly acts as an interface for the TensorFlow library.
Here’s the command to install the module and it’s libraries,
pip install Keras
Here’s the Documentation to work with keras.
Pytorch vs Tensorflow vs Keras – Comparison
Let’s take a look at some of the advantages that each of these libraries carries along with it.
Advantages of using PyTorch
Known for being able to offer debugging capabilities that far outclass both Tensorflow and Keras, PyTorch is a framework that offers a fair share of competition to the other two Frameworks.
Despite its recent debut, PyTorch is determined to provide a lot of flexibility to your code.
It particularly shines in being more effective while testing smaller pieces of code, and has extensive debugging capabilities.
Advantages of using Tensorflow
Being a framework that has been developed throughout the years, TensorFlow is considered as a reliable package to work with.
A large number of TensorFlow applications are quite heavy, and continually demand performance even when the dataset is incredibly vast.
A major reason as to why it’s reliable? It responds as required.
TensorFlow is great with working on Large Datasets, and provides High Performance even with a large amount of data to process.
Well renowned for it’s speed and it’s functionality, it is also used in Object Detection where you need to work with a large dataset of comparisons.
Advantages of using Keras
Being an API that is very high level, Keras provides a service placed on top of other Frameworks like TensorFlow.
Along with this aspect, Keras is a relatively simple module which is pretty easy to grasp once you go through the Documentation.
Keras however, doesn’t really work well with large scale projects and in such a sense, isn’t quite heavy.
Rather used mostly to work on smaller datasets, it is used to develop quick and simplistic prototypes.
Summing it all up, we could come to the conclusion that PyTorch is particularly more effective when one is writing code that tends towards being more of a script.
However, it provides very efficient and useful debugging options and should be considered highly in this regard.
TensorFlow is a framework which can work with the larger datasets, providing functionality and object detection tools, and is comparatively faster compared to the other two.
The Keras API is used as a prototyping medium and a key feature is that it can support TensorFlow too. Taking into consideration that the back end support is immense.
In case you’re still conflicted, browse some of our articles on working with each of these frameworks!
Overall, it is a good idea to choose the framework based on the use case and what you feel comfortable working with it.
Well, that’s all for now!