There is a vast array of deep learning frameworks, and many of them are viable tools, but the duopoly of TensorFlow and PyTorch is evident. TensorFlow and PyTorch are the most popular frameworks for delivering machine learning projects. Both are fantastic and versatile tools, used extensively in academic research and commercial code, extended by various APIs, cloud computing platforms, and model repositories. Yet, there are several significant differences between them, making them better suited for particular projects.
This post will explore some of the critical similarities and differences between PyTorch and TensorFlow and, How to pick the one which is best suited for your project.
PyTorch is a Facebook-backed framework, primarily meant to be used in Python, but it has a C++ interface. The software is based on the Torch library that was initially developed using the Lua programming language. Python enthusiasts love it as PyTorch leverages Python’s popularity and flexibility while keeping the convenience and functionality of the original Torch library. Given its pythonic nature, PyTorch fits perfectly into the Python machine learning ecosystem.
On the other hand, TensorFlow was initially developed by the Google Brain team and had interfaces in many programming languages. The high-level working environment works in Python, though the computations are done using high-performance C++ binaries. Its interface offers ready-made building blocks that significantly help newcomers to implement deep-learning architectures.
Ease of Use
TensorFlow is user-friendly and convenient. The framework includes high-level APIs and more sophisticated tools for building complicated projects, making it a go-to tool by many researchers and industry professionals. Since the incorporation of Keras, multiple redundancies and inconsistencies have been trimmed, which makes the projects less bloated and the code more elegant. The framework is well documented, having many extremely well-written tutorials on the internet.
PyTorch delivers a more flexible environment with the price of slightly reduced automation. It is a better pick for a team that has a deeper understanding of deep learning concepts algorithms. The framework is more “Pythonic” in construction, so a programmer with greater Python programming skills can leverage this skill to gain more with Pytorch. But this flexibility comes with a price as the framework requires more lines of code to deliver a project.
For example, defining a simple training loop in both the frameworks typically looks like this:
Tensorflow with Keras
model.compile(loss=‘binary_crossentropy’, optimizer=‘Adam’, metrics=[‘accuracy’]) model.fit(x=X, y, batch_size=64, epochs=num_epochs,verbose=2, validation_split=0.2)
criterion = nn.BCEloss() optimizer = optim.Adam(model.parameters(), lr=0.001) for epoch in range(num_epochs): iterator = iter(dataloader) for batch in iterator: optimizer.zero_grad() out = model(batch[“x”]) loss = criterion(out, batch[“y”]) loss.backward() optimizer.step()
The most important difference between the two frameworks is the way these frameworks define the computational graphs. In Tensorflow, graphs are defined statically where you have to define the entire computation graph of the model first and then run your ML model. This allows for a significant amount of control of the training process and the data flow, but comes as a highly limiting factor once the compilation is done.
In PyTorch, you can define a dynamic graph on-the-go. That means, before computations are running, there is no graph at all, and the graph and its input can be modified during runtime. The dynamic definition is beneficial while using variable-length inputs in RNNs. It also makes the code relatively easier to debug.
In Tensorflow 2.0, dynamic computational graphs were introduced in eager execution, i.e., The computational graph is built dynamically when variables are declared and is run when the function is called. On the other hand, PyTorch now also allows for the execution of a static computational graph. In short, the difference between graph definition between the framework is getting less profound by the day.
Visualization plays a crucial role while working on any Machine Learning project. TensorBoard is a brilliant tool that enables visualizing your Machine Learning models. It aids in drawing the computational graph, but it also allows you to observe the behavior of your training parameters by logging the summaries of the network at discrete intervals, which makes TensorBoard a valuable device for debugging.
Graph visualization package for PyTorch is Visdom, but It is not as feature-complete and doesn’t display the same versatility as TensorBoard. However, you can always use tools like Matplotlib and seaborn for plotting the behavior of certain functions.
Both frameworks are easy to deploy in case of small-scale server-side deployments. TensorFlow works well for mobile and embedded deployments, and it has TensorFlow-Serve, which allows seamless deployment of your model to production and easy management of different versions. TensorFlow-based models’ readability and stability make them a better pick for the production and business-oriented model deployment.
In the case of PyTorch, we may use Flask or any other similar alternative to code up a REST API on top of the model. However, TensorFlow Serve may still be a better option because of its superior performance. To keep up with Tensorflow-Serve, Facebook announced the release of TorchServe, a PyTorch model serving library. Still, the package does not provide the full functionality and ease of use of TensorFlow-Serve yet.
Both TensorFlow and PyTorch are great tools that make data scientist’s lives easier and better. When it comes to choosing the better one, it is all about the desired effect to be delivered. TensorFlow is a mighty and mature deep learning library with strong visualization capabilities that have long been sought after for building large scale deep learning models for production. Whereas PyTorch is a framework that has quickly gained attention from researchers and python enthusiasts due to its superior development and debugging experience.
Both frameworks come with pros and cons, and with great developers working on both sides, both frameworks will only get better with time and improve upon their shortcomings. The latest releases have also seen the two libraries converge towards a more similar profile.
So, the best advice would be to try out both the libraries and find out which one suits you the best!!