Is TensorFlow Better Than PyTorch?
Since PyTorch made its way into the machine learning sphere in 2016, loyalists from both camps have sung the praises of their framework of choice.
Today, curious minds such as yourself are looking through page after page to find out which one is worth your valuable time and effort. Both frameworks do a fantastic job of allowing developers and researchers to harness the power of artificial intelligence. They give the ability to complete tasks and discover findings that would be impossible through more traditional means.
The proper framework for you depends on your personal goals and ambitions.
- Over the years, TensorFlow has been widely known to be used by those working in industries.
- At the same time, PyTorch was more known to be utilized by researchers for studies, papers, and the like.
We’ll show you that today’s differences between the two aren’t as clear-cut as they were in the past.
Why is PyTorch More Popular Than TensorFlow?
Checking in with Google Trends for the period from September 13, 2021, to September 13, 2022, we can see that PyTorch has a reasonably comfortable lead throughout.
Now, why could that be?
The main reason could be that PyTorch has a much more Pythonic and object-oriented approach when compared to TensorFlow.
According to IEEE Spectrum’s Top Programming Languages 2022 article, Python ranks highly across all three of its ranking weights and only loses out on the number one spot in one ranking weight.
💡 Opinion: We can see that PyTorch and Python already enjoy high popularity and a steep upward trend. From that, one can safely say that PyTorch will maintain its healthy lead over TensorFlow for at least the next few years.
Is TensorFlow Still Relevant?
Despite PyTorch’s seeming dominance over TensorFlow in terms of interest, Google’s artificial intelligence library is still a smart choice for any developer looking to get into the field.
Those interested in making it in an industry setting would find it challenging to consider any time invested in TensorFlow a mistake. It’s simply the superior choice when it comes to productionizing models.
PyTorch has made great strides in catching up to TensorFlow’s production capabilities. However, a few models still use TensorFlow, and it would be rash to say TensorFlow is already considered irrelevant.
Which Is Better for Beginners: PyTorch or TensorFlow?
Either framework would serve a complete beginner well, but one’s coding background is what’ll determine which one is best.
If you feel more at home coding in Python, PyTorch is what you’ll want to choose. Alternatively, TensorFlow offers extensive support for multiple coding languages for any other coding language.
Is PyTorch Difficult to Learn?
Experienced Python developers would find working with PyTorch fairly intuitive since it was created with the language as the primary focus. They would have nearly no problem smoothly integrating PyTorch code into any existing code they may already have.
PyTorch’s API also has more elegant object-oriented classes that allow for more straightforward utilization of essential data choices and selection of model architecture.
PyTorch even implements dynamic computational graphs. These graphs mean the network can alter its behavior as it’s running while enjoying little to no overhead. Debugging and constructing sophisticated models are made a breeze, allowing the program to differentiate PyTorch expressions without manual input.
Is TensorFlow difficult to learn?
Many developers initially thought TensorFlow to be a complicated and unwieldy piece of technology that was difficult to learn and utilize properly.
With TensorFlow 2.0, the introduction of many helpful features vastly improved productivity workflow while making the framework more intuitive to understand. Among these added features were deeply-interwoven integration of Keras, default Eager execution, and Pythonic function execution.
Does Tesla Use PyTorch or TensorFlow?
Tesla’s development team uses PyTorch for its self-driving cars, including for features like AutoPilot and Smart Summon. PyTorch powers the computer vision capabilities of each vehicle in the Tesla fleet.
It’s a somewhat impressive feat for PyTorch does this without the help of LIDAR or high-definition maps. Everything is done on the fly as the car navigates its way around the world without driver assistance.
The fact that Tesla chose PyTorch as their internal development framework speaks to their faith in PyTorch as the future of machine learning. Their decision as pioneers in the self-driving car market has undoubtedly contributed significantly to PyTorch’s dominant popularity over TensorFlow.
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Does Google Use PyTorch or TensorFlow?
Google has recently started moving all support from its in-house framework, TensorFlow, to its successor, JAX (Just After eXecution).
Developers consider JAX to be simpler to use than TensorFlow. However, Google developers are pushing back on its implementation because TensorFlow is already a deeply-integrated part of its culture.
Despite that, Google expects JAX to completely replace TensorFlow as the artificial intelligence framework of choice for all Google products in the coming years.
TensorFlow seems to be slowly on its way to an unfortunately early retirement, giving JAX the chance of succeeding where it failed in the battle against PyTorch.
Conclusion
PyTorch and TensorFlow have been used to create millions and millions of models. Each brings us closer and closer to the artificial intelligence we’ve seen many times in films of old and new.
TensorFlow is still being used in many industry settings. Nonetheless, PyTorch is quickly on track to replacing TensorFlow for every situation that calls for a machine learning framework.
Google, the company that developed and released TensorFlow, has apparently seen the writing on the wall, so they went ahead and created a new framework named JAX.
JAX was most likely intended to be a direct competitor to PyTorch, which is why JAX was given capabilities that far surpass those of TensorFlow. You could even say it’s already superior to PyTorch in several aspects.
TensorFlow is still a decent choice since a good deal of models still make use of it across all industries. However, PyTorch is undoubtedly the dominant framework and will have no trouble increasing that dominance.
If we were to recommend a framework, it would be PyTorch solely due to its sheer adoption volume and ever-rising trend upwards. JAX would also be a worthwhile endeavor to learn, for it has the full backing of Google while showing promise to be much more competitive than TensorFlow.
But no matter what framework you end up dedicating yourself to, getting involved with artificial intelligence development while it’s still in its infancy is not an opportunity anyone should miss.
