Apache MXNet is a deep-learning framework that supports, trains, and defines deep-learning networks. It’s flexible and can be used on various platforms, and it’s quickly becoming one of the most popular tools for programmers.
There are many features of Apache MXNet that can help you in your journey to creating deep-learning AMIs. From model training to multiple languages being supported by the portable MXNet library.
Today we’re looking at what exactly Apache MXNet is, how you can use it to reap the maximum benefits, and much more.
What Exactly Is Apache MXNet?
Apache MXNet is a deep-learning template that gives you the ability to train, define, and eventually deploy your deep neural network once you’re happy with it.
You can do this on a number of devices, with some of the most popular being mobile phones and cloud-based programs.
Apache MXNet is fully scalable, meaning that the possibilities for growth are seemingly endless. You have access to fast model training, multiple languages, and flexible programming options.
One incredible advantage of Apache MXNet is the fact that it lets you mix both imperative and symbolic programming, allowing you to reap the benefits of both.
This can further maximize your productivity. MXNet’s foundation is a dynamic dependency scheduler, which is how the tool can offer this flexibility.
Benefits Of Apache MXNet
Now you know what Apache MXNet is, you might be wondering why you should use it over the other deep-learning framework tools on the market.
Here are some of the biggest benefits that you can expect from using MXNet.
1. Highly Scalable
Apache MXNet was originally developed with cloud infrastructure in mind, so it’s largely based on this type of programming.
MXNet includes a distributed parameter server to keep your framework on an almost completely linear scale. This also implements the use of CPUs and GPUs.
You can now distribute your deep-learning workloads over multiple GPUs without compromising your linear scalability. Apache MXNet can also even handle your auto-scaling, giving you one less task to manage.
Amazon Web Services conducted tests to find that MXNet’s performance was 109 times faster using clusters of 128 GPUs rather than one single GPU.
This scalability across multiple hosts is one of the reasons why MXNet is preferred over similar tools such as Theano and TensorFlow.
2. Multiple Languages
MXNet supports many languages to be used with its framework.
The more options available will undoubtedly make this tool more beneficial, as there are fewer roadblocks if you want to incorporate multiple languages into your programming.
Some of the most popular languages supported by MXNet are:
- Python
- Scala
- R
- C++
- Matlab
- Julia
- JavaScript
The ability to use MXNet with C++ also allows you to produce neural networks that are lightweight enough to be run on a larger number of platforms.
This opens up even more opportunities for your infrastructure, making it compatible with anything from low-powered Raspberry PIs to cloud-based servers.
3. Gluon API Capabilities
Perhaps the most desirable trait of Apache MXNet is the symbolic hybrid Gluon API feature. This makes it incredibly easy to train, develop, preview, and deploy your deep-learning models.
By using the Gluon API capabilities, you can do all of this – without sacrificing your training speed!
With just a few lines of Gluon code, you can build cohesive CNN, linear regression, and LTSM models for any uses you can imagine. Many people overlook this feature, but it’s sure to become one of your most used tools!
4. Vast Variety Of Tools
We’ve already alluded to some of them, but plenty of users appreciate using Apache MXNet so much due to the many toolkits and libraries available for them to use.
These can help with language processing, computer vision, time series, and more.
Using Apache MXNet
MXNet was originally created to bring a number of different programming approaches together, giving users a one-shop-stop to their benefit.
Why use multiple toolkits when you can reach everything you need in one place? MXNet aims to be the solution.
If you’re looking to get into deep-learning model development, learning all of the processes can be overwhelming and difficult.
MXNet aims to offer an easily accessible interface to help you understand the different programming approaches.
But what are these approaches MXNet blends together?
The first is imperative, which focuses on how computation performs and what this means.
The second, otherwise known as symbolic, is more focused on what should be performed. So, blending these two approaches together allows you to focus on two approaches rather than only one.
Let’s take a closer look at these two approaches, as well as hybrid programming with MXNet’s Gluon API functionality.
Imperative Programming
MXNet features plenty of tools, but one of the most known and used is called NDArray. This uses imperative programming to store and transform data.
You can use this tool to represent and change the inputs and outputs of your model, creating multidimensional arrays.
MXNet isn’t the only tool to offer NDArray capabilities, but the difference is that this tool allows you to use GPUs to accelerate the computing speed.
Imperative programming offers plenty of advantages, one of which is that it’s familiar to lots of developers with backgrounds in procedural programming. It’s also the more natural option for updates and debugs.
Symbolic Programming
Neural networks can change the input data by adding layers of nested functions to the input parameters. Every layer will offer a different linear function, and then a nonlinear transformation.
The entire point of deep learning is to optimize the parameters, so symbolic programming is vital.
MXNet symbolic programming helps you to define your functions through computation graphs.
Even complex functions are expressed as placeholder values, which can then be executed by attaching them to real values. You can express big models without repetitive motions and improved performance.
Hybrid Programming
MXNet uses Gluon API to cover the gap between the imperative and symbolic interfaces, without compromising the benefits of both. You can get the best of both worlds by using MXNet!
Gluon is simple to use to create portable models quickly. You can prototype your model with the help of NDArray (imperative) while using the hybridized command (symbolic) for faster inference and training.
Summary
Thanks for reading our guide to Apache MXNet! This is undoubtedly an excellent tool to use if you’re working on deep-learning models.
You can benefit from plenty of its capabilities, and the best part is that they’re all in one place. This speeds up your experience and leaves you without frustrating repetitive tasks.
Some of the most beneficial features of MXNet are the sheer scalability of the tool, the different languages supported, the toolkits and libraries at your fingertips, and the ability to use both imperative and symbolic programming functions.
Gluon API is highly regarded by many developers – and for good reason. You’re getting the benefits of both practices without having to pay for both tools or source them from separate suppliers.
We hope that this guide has been helpful to you and that you now feel more comfortable trying Apache MXNet for yourself!
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