One of the things that makes TensorFlow such a popular deep learning library is its flexibility.
Compatible to some degree with numerous programming languages, it can be used to aid in deep and traditional machine learning across multiple sectors — Thanks Google Brain team!
As great as this is, however, it does pose something of a conundrum… With so many programming languages to choose from, which is best for your AI project?
Well, by and large, your two main options are TensorFlow Py and TensorFlow Js.
That said, those two-letter suffixes don’t really give much away, so you’re probably still left wondering what route you should take, which is why, in today’s post, I’ll be breaking down the primary differences between these TensorFlow-compatible programming languages.
Before We Begin
Let’s get the basics out of the way first.
TensorFlow is a deep learning library, whereas Js and Py are programming languages that can be used to implement TensorFlow modules and functions, so we cannot compare TensorFlow itself to Js or Py.
Instead, we’ll compare TensorFlow Js with TensorFlow Py.
What Is Py?
Py stands for Python, which is an object-oriented, high-level programming language, meaning that it cannot be understood by computers as is. It must run through a translator of sorts before a machine can execute commands based on the data given in the script.
Built-in data structures as well as dynamic typing and binding make it an incredibly useful language for rapid application development.
These features also make it a powerful glue language, which is to say that it can be used to seam multiple pre-existing components together.
What Are The Benefits Of Python?
Python brings a myriad of benefits to the table for programmers.
Development Speed
Firstly, it’s an incredibly efficient language, and thus, it tends to increase productivity significantly. Python has a debugger written into the language itself, meaning weeding out issues is a total breeze.
Simplicity
There are also a few other Python benefits to mention, including its syntactic simplicity that positions it as a fantastic choice for newcomers to coding who want to ease themselves into a language that isn’t too impenetrable and hard to remember.
Rich 3rd-Party Affiliations
Python is linked to a fleshed-out array of third-party frameworks, libraries, and packages that can streamline development, particularly where large-scale projects are concerned.
Flexibility
Despite the focus on readability, you’ll also enjoy a certain amount of flexibility with Python.
Allowing developers to choose either procedural or object-based programming modes, it’s suitable for a wealth of different projects, and as it uses 5 data types (string, number, tuple, dictionary, and list), data analysis is remarkably easy to conduct.
Portability
Python is supported by all modern operating systems, meaning teams won’t have to use the same hardware to work collaboratively.
Community
Python has one of the strongest communities of all programming languages. Thousands upon thousands of users contribute to the Python toolbox and provide support for one another.
What Is Js?
Js stands for JavaScript, and, as you might assume, it’s yet another high-level programming language.
In fact, it’s something of a celebrity in the world of programming languages, as it’s one of the primary tools used to form the internet as we know it.
For instance, roughly 98% of all websites run JavaScript client-side where site behavior is concerned.
Much like Python, JavaScript is a dynamic, object-based programming language, but unlike Python, it utilizes JIT compilation, meaning compilation (translation to a language computers can understand) occurs during the execution of programs rather than before.
Benefits Of JavaScript
JavaScript also brings a number of benefits to the equation.
Development Speed
Like Python, JavaScript is considered an efficient programming language. In fact, for most applications, it’s even faster than Python, as it uses multithreading as opposed to Python’s single-flow process.
Simplicity
It’s also a very simple programming language, making it an enticing prospect for beginners to programming, as well as professional developers hoping to increase productivity.
Interoperability
JavaScript slides seamlessly into other programming languages. In other words, it can be dropped into pre-existing programs to amend them.
Advanced Interfaces
You can create dynamic web pages using JavaScript, thus creating a more enjoyable and engaging user experience.
In addition, JavaScript can be implemented in front-end and back-end development, meaning it’s a comprehensive web development tool for building visual and structural website aspects.
Enhanced Website & Application Performance
Site and application speed is essential to the user experience, and as JavaScript reduces code length by using a number of baked-in functions, it streamlines functionality a great deal.
Python & TensorFlow
Although TensorFlow is compatible with a number of different programming languages, many consider Python to be its native language, as well as the best one to use due to its unparalleled simplicity and robust community and support network.
You’ll also get the benefit of Tensorboard when using Python, a facility that provides the tooling and visualization necessary for experimentation in machine learning, something that Js doesn’t support.
Then there’s the scale of your project to consider. For larger models, Python is almost always going to have an edge over JavaScript where speed is concerned.
TensorFlow Py is incredibly stable too, meaning it’s unlikely to change over time and across different platforms.
The extensive data science libraries it brings to the table are also one of the reasons someone might choose to use Python over other programming languages for implementing TensorFlow.
But, does this mean Python is the best choice for you and your project? Not necessarily.
JavaScript & TensorFlow
Generally speaking, JavaScript is a higher-performance language, and, as such, TensorFlow Js exceeds TensorFlow Py in a number of ways, the main one being dual-side implementation.
What does this mean, exactly? Well, Python only works well server-side, which is to say it can only reliably be used to execute code on the web server.
JavaScript, by contrast, is active on both the server-side and client-side, meaning it can execute programs on the server and the user’s physical hardware.
That said, we can assume Python is best for Server-exclusive projects, right? Actually… nope.
Thanks to the JIT process of JavaScript, which, if you remember from earlier, is a compilation protocol, TensorFlow Js is faster on the server-side of things than Python.
Furthermore, even though both Python and JavaScript are easy-to-learn programming languages, TensorFlow Js is far easier to use, and as it’s capable of utilizing integrated GPUs, you don’t have to fork out for a beefy discrete GPU to get started.
TensorFlow Py Vs. TensorFlow Js: Which Should You Use?
Okay, so we’ve covered a lot of ground here today, but the key takeaways are relatively few.
Ultimately, which programming language you choose for your TensorFlow project comes down to the following:
- Implementation Side — Being that Python was built from the ground up for server-side applications, JavaScript is almost always going to be the superior option for any client-side implementation.
- Scale of Project — JavaScript doesn’t scale very well for large models — It’s roughly 15 times slower for larger projects than it is for smaller, more focused undertakings. On the other hand, as Python has that vast 3rd-party resource library, it’s particularly good at handling bigger projects.
- GPU — You cannot use Python without a discrete GPU, so if you’re relying on integrated graphics facilities, you’ll have to use TensorFlow Js.
Final Thoughts
The differences outlined above mean that neither TensorFlow Py nor TensorFlow Js is technically superior, rather, they each have their specialties, and which you choose comes down to the nature of your project and hardware.
