Apache MXNet has become immensely popular over the last few years for many reasons, but a big one is because of how flexible it is, allowing a person to train, define, and eventually deploy neural networks across multiple devices and machines at one time.
With that being said, however, it’s no use acquiring the framework if you aren’t aware of the languages that it supports as part of the machine learning library.
Thankfully, if you were curious about installing Apache MXNet for yourself, we’ve got the full breakdown of it right here so that you know exactly what you’re getting into, and if it’s worth your time.
So, are you ready to start creating your own machine learning library?
Keep reading as we discuss every aspect of this popular framework so that you can decide whether it’s the right choice for you, and if it will be compatible with your hardware.
How Does Apache MXNet Work?
MXNet is built on a dynamic dependency scheduler that can automatically parallelize symbolic and imperative operations in a matter of moments.
Most people use this framework to establish a scalable deep learning framework that contains learning models, such as convolutional neural networks (CNNs), for example, along with natural language processing models (NLPs).
Not only does this enable incredibly quick model training, but it will also scale automatically to the number of GPUs that are currently available across the numerous hosts and machines within the network, which is a feature that helps MXNet to stand out from a lot of its competitors.
In fact, after running a few tests, Amazon Web Services concluded that MXNet performed a staggering 109 times faster than normal when it was running across 128 GPUs, which gives you an idea of just how speedy the framework can be.
What Languages Are Supported By MXNet?
As of right now, these are the programming languages that Apache MXNet is capable of supporting:
It should also be noted here that you will be able to run MXNet on either Linux, MacOS, Windows, or Cloud devices.
The Main Features Of MXNet
There are three main features provided by MXNet that help to improve the quality and performance of its models, and that form the core of the framework.
These features are:
- Distributed Training – This is the feature that most people intend to use MXNet for when they sign up for it. It essentially grants developers the ability to train models across multiple different machines, making the entire process so much easier.
- Automatic Mixed Precision – This feature allows developers to have the option of using either 16-bit or 32-bit floating point numbers. These can help to improve the accuracy and precision of each model.
- Automatic Model Compression – The bigger the model, the more likely it is that it will lag behind in its speed and quality, so in order to make sure it remains as effective as possible, this feature will automatically reduce the overall size of the model so that it can run at its best without being compromised by the size.
The Two Programming Modes Of MXNet
To gain a deeper understanding of how exactly Apache MXNet works behind the scenes, it is worth looking at the two different programming modes which are the tools it uses to arrange and present its information to the developer.
Imperative Programming Mode
This is the primary programming tool utilized by MXNet, and it is what the framework uses to store and transform data. The imperative programming mode is essentially “how” the computation is performed, representing the inputs and outputs of a model through multi-dimensional arrays.
This type of program will be very familiar to any developers who have had some prior experience with procedural programming, and who know all about parameter updates and interactive debugging.
Symbolic Programming Mode
On the other hand, you have the symbolic programming mode which dictates “what” should be performed within the framework and is primarily concerned with how the neural networks transform and dictate input data by applying several layers of nested functions onto the input parameters.
Essentially, the symbol program will define functions through computation graphs which are data structures that consist of several different nodes.
All modern frameworks that are related to deep learning make use of these types of graphs, and MXNet is included in this.
Benefits Of Using Apache MXNet
There are multiple different benefits that come with using Apache MXNet which have seen it overshadow a lot of its competition over the past few years.
Let’s take a look at some of the biggest benefits you can expect while using it.
As mentioned previously, MXNet supports a whole range of programming languages, including some of the most popular ones such as C++ and Python.
This makes the framework incredibly accessible, and a step ahead of many of its competitors who tend to only stick to a handful of languages at a time, or even just a single one, which can feel very restrictive.
Popular public cloud providers such as Amazon Web Services fully support MXNet, many of whom actually use it as their learning framework of choice.
Intel, Microsoft, and Baidu also support MXNet through their cloud functionalities, making it fully compatible with this style of programming and networking.
MXNet contains a huge library of pre-trained models that developers can use as a basis when starting off their new projects.
This saves a tremendous amount of time that would normally be spent on researching and training their own models, making MXNet incredibly appealing and even more accessible.
When it was first developed, MXNet was designed to be highly scalable, meaning that developers would be able to implement even more computing resources to their projects as they see fit, without ever being restricted on how their projects will look, and what is actually included within them.
Of course, MXNet is also designed to scale across multiple GPUs at one time, which makes it the best framework for larger NLP tasks and projects since the scalability allows each of the deep learning models to work quickly and efficiently across a large dataset.
MXNet has much more efficient memory management than many other deep learning frameworks, with its memory-efficient algorithm being incredibly beneficial for tasks and projects which are on a much larger scale than usual.
This extra feature also saves a developer from having to buy expensive hardware to perform these tasks at a faster rate, since the quick and effective memory management will be able to accomplish this already.
As MXNet grows, we can only expect it to expand in its features, properties, and the number of programming languages that it supports, but as of this moment, it is definitely still leading the charge when it comes to deep learning frameworks, allowing you to create a quick and efficient machine learning library without having to install any fancy and expensive new hardware.
Simply load up MXNet, have your machines or GPUs at the ready, and you’ll be all ready to go, it really is that simple.
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