Tensorflow vs PyTorch - A Battle of Ages

By Dhnesh on Feb. 7, 2022, 2:11 p.m.

If you are a newbie or an experienced machine learning engineer, you might have come through the two most common frameworks in the community named Tensorflow and Pytorch. The discussion over which heavyweight is better is a longstanding mark of discussion. While developed under the guidance of Google Tensorflow came a long way in recent years whereas Pytorch is the new buzz in the AI community. 



 - Launched in 2015 Tensorflow is an open-source help you create and train ML models.
 - Developed by Google Brains Team for use in research and production
 - Current stable version is 2.8.0 (2 February 2022)
 - Written in Python, C++, CUDA
 - TensorFlow can operate easily on multiple CPUs and GPUs
 1. AutoDifferentiation: is the process of automatically working out the gradients vector of a model as for every one of its boundaries

 2. Metrics: To survey the exhibition of AI models, TensorFlow gives API endpoints to usually used metrics

 3. Keras Integration:  Tensorflow has compatibility with keras, which permits to code a few high-stage functionality sections in it. Keras gives the device-unique capability to TensorFlow.

 4. Parallelism: TensorFlow tracks down its utilization as an equipment speed increase library because of the parallelism of work models. It involves different appropriation techniques in GPU and CPU frameworks.


1. Inconsistency: Tensorflow layers have sometimes similar names with different executions e.g tf.layers.conv2d, tf.layers.Conv2d

2. Speed: Pipelines are not optimized where most of the time network waits to read from the disk

3. No Training on TPUs: Currently TensorFlow’s architecture TPU only allows the execution of a model not to train it.



 - Launched in 2016 Python is an open-source library to create computer vision and natural language processing models.
 - Developed by Facebook AI Research lab
 - Current stable version is 1.11  (2 February 2022)
 - Written in Python, C++, CUDA
 - TensorFlow can operate easily on multiple CPUs and GPUs
 1. Computational Graphs: A PyTorch Tensor has only an n-layered cluster. The structure gives plenty of capacities to working on these Tensors. However, to speed up the mathematical calculations for Tensors, PyTorch allows the utilization of GPUs, which can provide speedups of 50x or greater.
 2. Automatic differentiation. : Pytorch uses a technique referred to as automatic differentiation. A recorder statistics what operations have been carried out, after which it replays it backward to compute the gradients. This technique is especially powerful whilst constructing neural networks to store the time on one epoch by calculating the differentiation of the parameters on the forward skip.

 3. Easy Debugging: As the computation graph in PyTorch is stated at runtime you can use our favorite python debugging tools such as pdb, ipdb, pycharm debugger or trusty log commands.


 1. Documentation and Community: The developer community is still small and needed a stable Documentation
 2.Lacking Visualization Metrics: Unlike TensorFlow’s tensorboard, you can not visualize your metrics on Pytorch.