Which Framework Is Better: PyTorch or TensorFlow?

This comparison blog on PyTorch vs TensorFlow is intended to be useful for anyone thinking about starting a new project, switching from one Deep Learning framework to the other, or learning about the top two frameworks! When configuring the Deep Learning stack's training and deployment components, the emphasis is mostly on programmability and flexibility.

So let the fight begin!

I'll begin this PyTorch versus TensorFlow article by analysing the Ramp-Up Time of both frameworks.

Graph Construction And Debugging:

eginning with PyTorch, the clear advantage is the dynamic nature of the entire process of creating a graph.

The graphs can be built up by interpreting the line of code that corresponds to that particular aspect of the graph.

So this is entirely built on run-time and I like it a lot for this.

With TensorFlow, the construction is static and the graphs need to go through compilation and then running on the execution engine that I previously mentioned.

PyTorch code makes our lives that much easier because pdb can be used. Again, by making use of the standard python debugger we will not require learning to make use of another debugger from scratch.

Well with TensorFlow, you need to put in the little extra effort. There are 2 options to debug:

You will need to learn the TF debugger.
Request the variables you want to inspect from the session.

Well, PyTorch wins this one as well!

Ramp-Up Time:

PyTorch is just NumPy with the addition of the ability to use a graphics card.

PyTorch is pretty easy to understand and grasp because it requires something as simple as NumPy.

The fundamental difficulty with Tensorflow, as we all know, is that the graph is first compiled, and then the real graph output is produced.

So, where is the dynamism here? TensorFlow also offers a dependency that executes the produced code using the TensorFlow Execution Engine. The fewer the dependencies, the better in my opinion.

Returning to PyTorch, the code is well known for operating at breakneck speeds and proving to be pretty efficient overall, so you won't need to learn any new principles here.

TensorFlow requires the principles of variable scoping, placeholders, and sessions. This leads to more boilerplate code, which I'm sure none of the programmers here love.

So in my opinion, PyTorch wins this one!

Coverage:

Well certain operations like:

1. Flipping a tensor along a dimension

2. Checking a tensor for NaN and infinity

3. Fast Fourier transforms supported

TensorFlow natively supports them.

We also have the contrib package, which we may use to create more models.

This enables the use of higher-level functionality and provides you with a diverse set of options to deal with.

PyTorch, on the other hand, has less functionality implemented as of now, but I am confident that the gap will be overcome very soon owing to all of the attention PyTorch is garnering.

However, it is not as popular among freelancers and students as TensorFlow. This is, of course, subjective, but it is what it is, guys!

TensorFlow nailed it this time!

Serialization:

It should come as no surprise that storing and loading models is a breeze with both frameworks.

PyTorch offers a straightforward API. The API may either save all of a model's weights or pickle the entire class, if you want.

The main advantage of TensorFlow is that the full graph, including parameters and actions, can be preserved as a protocol buffer.

Depending on the requirements, the Graph can subsequently be loaded in other supported languages such as C++ or Java.

This is crucial for deployment stacks that do not support Python. This is also useful if you modify the model source code but still want to run old models.

TensorFlow has this one in the bag, as clear as day!

Deployment:

Both frameworks are simple to wrap in a Flask web server for small-scale server-side deployments.

TensorFlow performs admirably in mobile and embedded deployments. This is more than can be stated for the majority of deep learning frameworks, including PyTorch.

Deploying to Android or iOS does necessitate some effort in TensorFlow.

You don’t have to rewrite the entire inference portion of your model in Java or C++.

Aside from performance, one of the most noticeable characteristics of TensorFlow Serving is the ease with which models may be hot-swapped without disrupting the service.

I think I will give it to TensorFlow for this round as well!

Documentation:

Needless to say, I found everything I needed in the official documentation for both frameworks.

The Python APIs are fully documented, and there are plenty of examples and tutorials to get started with any framework.

However, one minor detail piqued my interest: the PyTorch C library is largely undocumented.

However, this is only relevant when building a bespoke C extension and maybe when contributing to the product as a whole.

To summarise, I'd say we're stuck with a tie here, people!

However, if you believe you have a bias against something, go to the comments section and express your thoughts. Let's get started!

Device Management:

TensorFlow's device management is simple — you don't need to specify anything because the defaults are adequate.

TensorFlow, for example, assumes you wish to run on the GPU if one is available.

Even if CUDA is enabled, you must explicitly move everything onto the device in PyTorch.

The one disadvantage of TensorFlow device management is that it consumes all available memory on all available GPUs by default, even if only one is used.

I've discovered that PyTorch code requires more frequent checks for CUDA availability and more explicit device management. This is especially true when writing code that must execute on both the CPU and the GPU.

TensorFlow has an easy win here!

Custom Extenders:

Moving on, last but not least, I have chosen special extensions for you.

Both frameworks support the creation and binding of custom extensions written in C, C++, or CUDA.

TensorFlow necessitates more boilerplate code, but it is arguably cleaner in terms of supporting numerous kinds and devices.

In PyTorch, however, you just create an interface and implementation for each CPU and GPU version.

Compiling the extension is likewise simple with both frameworks and does not necessitate the download of any additional headers or source code beyond what is available with the pip installation.

And PyTorch has the upper hand in this regard!