Both PyTorch and TensorFlow are top Deep Learning frameworks that are extremely efficient at handling a variety of tasks. But there are subtle differences in their ability, working, and the way they work and it is extremely important that you understand these differences that lie in between TensorFlow vs PyTorch.
In this blog, we will understand the difference between PyTorch and TensorFlow based on the following criteria:
PyTorch vs TensorFlow:
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Since both PyTorch and TensorFlow are very popular, it is common to be at crossroads when deciding on which one to learn or which is more powerful. This TensorFlow vs PyTorch comparison will make it easier and quicker for you to grasp all of the differences between these frameworks. Read on.
PyTorch
PyTorch is a library that provides users with amazing capabilities in terms of dynamism and ease of use. In PyTorch, code can be inspected in real-time, and it runs efficiently as well.
For Deep Learning and Machine Learning applications, PyTorch provides amazing features such as:
TensorFlow
TensorFlow is probably one of the most popular Deep Learning libraries out there. It allows for the seamless usage of complex mathematical operations to drive Machine Learning solutions across a spectrum of problems.
There are numerous features that give TensorFlow the top status that it is known to have:
- High-level APIs
- ML production on the go
- Research-based architecture
- Deployment on clouds
Also read: TensorFlow Tutorial
This is a very common question: Which is better PyTorch or Tensorflow?
If you are at this point in your learning path or the implementation phase where you’re confused about which framework is the right one for you, then it is only fit to compare these frameworks to give you better clarity and help you arrive at a decision.
The following section covers the comparison between these frameworks across a variety of points as shown below.
PyTorch vs TensorFlow
PyTorch vs TensorFlow: Open-source
You might already be aware of the fact that both PyTorch and TensorFlow are open-source. But are they the same? No. TensorFlow was built by the team at Google, keeping Theano in mind, whereas, PyTorch was developed by the team at Facebook, completely basing it on the Torch framework.
PyTorch vs TensorFlow: Implementation
TensorFlow is popular among professionals and researchers across a variety of domains. This is because TensorFlow offers good documentation and multiple articles across the web that makes it easier to implement solutions to complicated problems.
There are many certification programs for TensorFlow that help even novice learners get started and begin working with the framework rapidly.
Coming to PyTorch, it is relatively new when compared to TensorFlow. TensorFlow has been around for a while, but it is to be noted that PyTorch has a good collection of official documentation and many tutorials that can add value to the learners.
The next difference between PyTorch and TensorFlow is based on how easy each of them is to learn.
PyTorch vs TensorFlow: Easier to Learn
If you are wondering about which is easier to learn, the answer is that it is actually subjective and based upon what you plan to implement using either of these frameworks. But, the common opinion of the learners is that TensorFlow can sometimes seem to be more overwhelming than PyTorch as a whole.
PyTorch feels more ‘native’ to Python and makes it very easy to develop and implement Machine Learning models. There are many entities inside the working of TensorFlow, such as sessions, placeholders, and more, which make it a bit more convoluted than PyTorch.
PyTorch vs TensorFlow: Computation Graphs
A computation graph is a way of representing the evaluation of a mathematical expression via a graph structure. The major distinction between PyTorch and TensorFlow lies in how the computational graphs are defined and used.
In the case of TensorFlow, it uses a static graph for computation, meaning the entirety of the computation graph has to be defined first before any execution takes place.
However, this is not the case with PyTorch. Here, the computational graphs can be defined and manipulated dynamically, and this gives PyTorch the edge that it needs for being extremely easy to deal with inputs that vary in real-time when working with neural networks.
Next up we have to check another point that will help add clarity in terms of PyTorch or TensorFlow.
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PyTorch vs TensorFlow: Debugging
As mentioned before, computation graphs are dynamic in nature when working with PyTorch. This means that any of the Python debugging tools, such as ipdb or pdb, can be effectively put into use to debug code.
In the case of TensorFlow, it calls for the installation of a separate tool called tfdbg that allows for the evaluation of TensorFlow expression at runtime. Since the code cannot be natively debugged with Python, the installation of tfdbg is compulsory.
PyTorch vs TensorFlow: Community Support
The curiosity to learn and implement solutions using Deep Learning from novice users is the reason why the Deep Learning community is very well-revered.
The size of the community matters as it would immensely help find solutions to problems quickly and easily, implement new things, and find help when stuck with a problem.
TensorFlow wins the comparison in this as it has the backing of a very large community behind it when compared to that of PyTorch. This is because of the fact that PyTorch as a framework is new in comparison to TensorFlow, and this age factor alone gives TensorFlow the edge as over time there has been more content about TensorFlow than PyTorch.
Nonetheless, with the onset of people realizing the ease of use and the power of PyTorch, this scenario is set to change in the near future.
Next up, we have to look at TensorFlow 2.0 vs PyTorch in the aspect of usage of visualization tools.
PyTorch vs TensorFlow: Visualization Tools
If you have dug around the Internet to see TensorFlow or PyTorch in use, you might have come across beautiful visualizations in the case of TensorFlow. TensorFlow achieves this using an amazing tool called TensorBoard.
It is used to provide a myriad of visualizations and good-looking graphs that are very easy to understand, and it helps derive analytics based on the details extracted from these graphs.
PyTorch does not have a tool like this, but of course, it can achieve the same effect by making use of libraries like Matplotlib. But then, TensorBoard can be used with PyTorch as well.
This could be a complex process that involves the integration of these two tools, but it is a possible task. However, the unavailability of native support is definitely a concern when it comes to getting started quickly.
If you are thinking about Pytorch vs TensorFlow performance, then this point will add value to your learning.
PyTorch vs TensorFlow: Deployment
In terms of the ease of deployment, TensorFlow takes the win as it provides a framework called TensorFlow Serving that is used to rapidly deploy models to gRPC servers easily. PyTorch, on the other hand, can achieve a similar result if used with Flask or any other REST APIs built on top of the model.
Also read: Keras vs TensorFlow
REST APIs can be used with TensorFlow if required as well. If performance is the main concern, then there should be no second thought that TensorFlow Serving is the go-to option.
PyTorch vs TensorFlow: Data Parallelism
When the talk is about using parallel computation power to support a pipeline for distribution of data rather than one entity to process the data, it can be said that PyTorch offers parallelization capabilities to its users.
Torch.nn.Parallel is used to effectively wrap modules to be used parallelly over other batch data. This way, the power of multiple GPUs can be harnessed simultaneously without much effort.
TensorFlow offers parallelism as well. But here, it requires more manual implementations that makes it complex both from a learner’s perspective and in a production environment.
PyTorch vs TensorFlow: Prototyping and Production
When it comes to building production models and having the ability to easily scale, TensorFlow has a slight advantage. However, on the other side of the same coin is the feature to be easier to learn and implement.
And in this domain, PyTorch excels. It is elegant and easy to build prototypes and work on projects that have more of a non-production implementation.
Conclusion
So, which one is better? Well, there is no straight answer to this question because there is a population of people who love to work with PyTorch, and there are people who stand loyal to TensorFlow.
Both of these frameworks offer amazing usability and a plethora of tools that make it easier to implement novel things. At the end of the day, both PyTorch and TensorFlow are very powerful frameworks that get a lot of community support and usage.
Your best bet to knowing which framework you should consider learning and using would be to just pick one and get started with it. The intuition that you develop when working in the field of Deep Learning will eventually help you understand which framework is a better fit for your usage.
I hope this blog helped you understand both PyTorch and TensorFlow in detail.
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You can thoroughly master all of the concepts in Data Science and implement them as well. Do head to the comments section to let us know if you have any more points to add to this comparison and about your preference regarding which framework better fits your application!