In this POV, Anand Sridhar Rao, General Manager, Data and Intelligence, Sylvester Daniel John, Head of Applied AI Center of Excellence, and Samson Saju, Senior Research Engineer, Mindtree, share their views on the three prominent deep learning frameworks, namely, PyTorch, Keras, and TensorFlow. They also study their suitability for different applications.
Comparing ML frameworks: PyTorch, Keras, and TensorFlow.
In the process of simplifying life, artificial intelligence is gaining immense popularity. Many industries are embracing AI to improve efficiency, productivity, revenue, and deliver superior experiences. With newer deep learning models, AI can gather and analyze large quantities of structured or unstructured data in the form of tables, text, or images—generating valuable business insights. Businesses on their path of adopting AI are often faced with a multitude of challenges like:
- How would the business benefit from AI?
- When to adopt AI?
- How and where to start?
- Which frameworks should we use?
When and which?
It’s hard to pick a winner among the three ML frameworks, and it often comes down to the use case. We recommend TensorFlow for scenarios that focus on multi-platform support, IoT, EDGE, Mobile as offerings around these are more mature in comparison to PyTorch. We recommend PyTorch for most applications due to its Pythonic API, availability of SOTA pre-trained models, and ease of creating complex network architectures. We don't advise using Keras as it is primarily developed for rapid prototyping. It often becomes challenging to make custom layer modifications in Keras owing to its abstraction over the underlying framework. However, Keras can be used as a tool for rapid prototyping of TensorFlow models and eases the steep learning curve of TensorFlow.
Read our POV to understand the distinguished features of PyTorch, Keras, and TensorFlow and their suitability for different applications.
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