Apache MXNet Deep Learning Framework Guide

Deep learning is a branch of machine learning that enables computer systems to identify very intricate patterns in data and solve complex problems. AI-based platforms like ChatGPT, DALL-E, and Midjourney use deep learning algorithms and neural networks to process user inputs and generate meaningful responses.

When creating deep learning applications, machine learning engineers use different tools and frameworks to get the job done. Among these frameworks is Apache’s MXNet deep learning framework. This open-source library is not only for research prototyping but also for building production-ready apps. It offers a rich ecosystem of libraries for developing computer vision, natural language processing, and other applications.

Keep reading to discover more about the Apache MXNet framework, including its core features, as well as how to set it up and build deep learning models.

What is Apache MXNet?

Apache MXNet is an open-source framework that provides tools, libraries, models, and dependencies for building deep learning applications. It features libraries like D2L.ai, GluonCV, GluonNLP, and GluonTS, which developers can use to incorporate AI functionalities like natural language processing and computer vision applications.

The framework is compatible with multiple programming languages, including Python, Scala, Julia, Java, Perl, Clojure, and JavaScript. It also supports operating systems like Linux, Windows, and macOS. This versatility allows the Apache MXNet framework to fit into diverse projects and use cases.

MXNet’s use of symbolic and imperative programming facilitates the creation of high-performance applications. Specifically, it allows deep neural networks to be exported in different languages like Java and C++. This means you can create a model using your preferred language and convert the underlying code to run the model on other environments. The symbolic and imperative programming paradigm results in a hybrid front end that supports flexibility, speed, and efficiency—desired attributes in deep learning model development.

Besides the hybrid front end, MXNet also provides the Gluon API to simplify deep learning model building. The API lets you prototype and train models while retaining high-speed computing. What’s more, the Gluon API can be accessed in multiple languages, from Python to Java.

Note: While Apache MXNet is a robust framework, it has been retired since September 2023. This could impact how Apache MXNet fits in your future deep learning applications. Additionally, it may not compare to fully supported platforms like TensorFlow and PyTorch, meaning you have to perform extra research to ensure it fits your use cases.

How does MXNet work?

MXNet works by using back-end, run-time, and computational graph optimization components.

In this context, the back end is the engine responsible for executing model computations on different platforms, including GPUs and CPUs. MXNet supports multiple back-end engines, which include the default integrated MXNet engine and Nvidia’s CUDA for GPU operations. The MXNet engine supports parallelism, which allows it to execute multiple computations at once, enhancing overall model performance.

Back-end components like the MXNet engines are also responsible for efficient memory management, kernel executions, and device management. By providing a hardware abstraction, the back end enables machine learning engineers to write code that can run on multiple platforms—without worrying about the underlying intricacies.

The MXNet runtime library is responsible for executing and managing computational tasks. The integrated mxnet.runtime library compiles necessary dependencies needed to run a particular model. The MXNet runtime also provides the tools for analyzing running processes, managing memory, and handling code errors and bugs.

Apache’s MXNet supports computational graph optimization, improving models’ speed and efficiency. The computational graph optimization allows MXNet to achieve scalability and high performance on CPUs (Intel) and GPUs (NVIDIA and CUDA). This technique results in better memory management and the ability to leverage multi-core CPUs for computations. Techniques like model quantization also reduce the memory footprint of neural networks, especially when running on Intel CPUs.

How to set up Apache MXNet

You can install Apache MXNet on Windows, Linux, and macOS. During installation, you will choose your preferred programming languages and set the required environment variables and dependencies. Alternatively, you can use Docker containerization for easier deployment.

In this section, we provide tutorials on how to set up Apache MXNet for Python on Windows, Linux, and macOS.

Installation on Microsoft Windows

1. Before installing Apache MXNet for Python, you have to ensure Python is installed on your computer. You can download the latest Python version from python.org.

2. Navigate to the Apache MXnet’s get started page and choose your operating system and desired MXNet version. For this tutorial, we selected Windows and the default MXNet version.

3. After choosing the Windows and MXNet version, select Python as your desired programming language and choose your preferred CPU or GPU version.

4. You should see three installation options: Pip, Docker, and Build from source. Installing MXNet using pip by running the following command is the easiest option.

--CODE language-markup--
pip install mxnet

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5. If the installation is successful, you should see the following output.

Installation on Linux and macOS

Like Windows, you can run the pip install mxnet command in your command terminal to add the MXNet framework to your computer. If you have GPU accelerators on Linux, run the pip install mxnet-cu102 command—but you must also install the CUDA library for this to work.

You can also install Apache MXNet via Docker on Linux. This requires that you download the necessary Docker images from DockerHub. You can then use Docker to unpack the images using the following command.

--CODE language-markup--
$ docker pull mxnet/python:gpu
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Don’t know where to start? These Apache MXNet tutorials on GitHub can help you master MXNet fundamentals for building neural networks and deep learning models. They include step-by-step guides on how to use the core MXNET components like the Symbol API, Gluon Python API, and Autograd API.

How to build deep learning models with Apache MXNet

Apache MXNet supports the development of different deep learning models and algorithms, including convolutional neural  networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and language and regression models.

This section provides a detailed guide on building a deep learning model with MXNet. Specifically, we’ll create an image classifier using a convolutional neural network.

‍Note: We’ll use Google Colab as our development environment and Python as the preferred programming language.

1. Install necessary dependencies

Since you’ve already installed MXNet on your system, the next step is to import the necessary dependencies into your projects. You’ll do so using the following code:

--CODE language-markup--
import mxnet as mx
from mxnet import gluon, autograd, init, nd
from mxnet.gluon import nn
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If you encounter an error when running the above code, you may need to install an older version of MXNet and NumPy with the following command:

--CODE language-markup line-numbers--
pip install mxnet-mkl==1.6.0 numpy==1.23.1

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2. Load dataset

Next you’ll download and install the datasets you’ll use for training. This tutorial uses the CIFAR 10 dataset, which contains training and testing images. The following code allows you to download and load the dataset in the project:

--CODE language-markup line-numbers--
batch_size = 64
Train_data = gluon.data.DataLoader(gluon.data.vision.CIFAR10(train=True).transform_first(gluon.data.vision.transforms.ToTensor()),
batch_size=batch_size, shuffle=True, num_workers=4)
test_data = gluon.data.DataLoader(
gluon.data.vision.CIFAR10(train=False).transform_first(gluon.data.vision.transforms.ToTensor()),
batch_size=batch_size, shuffle=False, num_workers=4)
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3. Define model

With the dataset loaded, the next step is to define the convolutional neural network, which will be responsible for image classification. You’ll create the CNNModel as a Python class and then import the necessary layers from the MXNet library. Here’s the code for creating the CNN model:

--CODE language-markup line-numbers--
class ImageModel(nn.Block):
def __init__(self, num_classes):
super(CNNModel, self).__init__()
with self.name_scope():
self.conv1 = nn.Conv2D(channels=32, kernel_size=3, activation=’relu’)
self.pool1 = nn.MaxPool2D(pool_size=2, strides=2)
self.conv2 = nn.Conv2D(channels=32, kernel_size=3, activation=’relu’)
self.pool2 = nn.MaxPool2D(pool_size=2, strides=2)
self.conv3 = nn.Conv2D(channels=32, kernel_size=3, activation=’relu’)
self.flatten = nn.Flatten()
self.fc1 = nn.Dense(128, activation=’relu’)
self.fc2 = nn.Dense(num_classes)
def forward(self, x):
x = self.conv1(x)
x = self.pool1(x)
x = self.conv2(x)
x = self.pool2(x)
x = self.conv3(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.fc2(x)
return x

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4. Initialize model

Once a CNN model is created, you have to initialize it by adding custom parameters like context.

--CODE language-markup line-numbers--
number_classes = 10
context = mx.cpu()  # Change to mx.gpu() if you have a GPU available
model = ImageModel(num_classes)
model.initialize(init=init.Xavier(), ctx=context)
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You can then add the loss and optimizer functions to improve the model’s performance.

--CODE language-markup--
criterion = gluon.loss.SoftmaxCrossEntropyLoss()
optimizer = gluon.Trainer(model.collect_params(), ’adam’)
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5. Train the model

You downloaded the CIFAR 10 dataset in the above sections and then defined the CNN model. You can now use the dataset to train the model. To do that, you have to declare the number of epochs the model will use.

In this context, epoch refers to the number of times a dataset is passed through an algorithm. In this case, you’ll set the number as 10. You will then use a for loop to go through the data, as shown in the following code.

--CODE language-markup line-numbers--
num_epochs = 10
for epoch in range(num_epochs):
train_loss, train_acc = 0., 0.
for data, label in train_data:
data = data.as_in_context(ctx)
label = label.as_in_context(ctx)
with autograd.record():
output = model(data)
loss = criterion(output, label)
loss.backward()
optimizer.step(batch_size)
train_loss += loss.mean().asscalar()
label = label.astype(’float32’)
train_acc += (output.argmax(axis=1) == label).mean().asscalar()
print(f”Epoch {epoch + 1}, Loss: {train_loss / len(train_data):.4f}, Accuracy: {train_acc / len(train_data):.4f}”)
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When you run the code, you should see the following output. The accuracy variable states how accurate your model is. In our case, we achieved an accuracy rate of 75% (0.7508) in the last iteration.

6. Evaluate the model

Now that you’ve trained the model, you can further evaluate its accuracy and performance using the following code:

--CODE language-markup line-numbers--
test_loss, test_acc = 0., 0.
for data, label in test_data:
data = data.as_in_context(context)
label = label.as_in_context(context)
output = model(data)
loss = criterion(output, label)
test_loss += loss.mean().asscalar()
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After running the evaluation loop, test_loss will contain the total loss accumulated over the entire test dataset, and test_acc will contain the accuracy. To understand the model's performance better, you might want to compute the average loss per batch or per sample, which can be done by dividing test_loss by the number of test samples or batches—for example.

The primary output here is the total test loss, which provides a quantitative measure of the model’s performance on unseen data. Lower loss values typically indicate better model performance. Based on the performance metrics (like loss and accuracy), you might decide to adjust the model architecture, tweak hyperparameters, or even use more training data to improve the model. These metrics are crucial for reporting in research, product development, or to stakeholders to demonstrate the efficacy of the model. If you have multiple models or configurations, you can use these metrics to compare them and select the best performing model for your task.

Practical applications

You can use MXNet to develop numerous real-world applications, including the following.

• Computer vision. MXNet provides pretrained models to build computer vision applications for tasks like image classification, face recognition, and object detection.
• Natural language processing. MXNet can help you build applications that process natural language, identify context, and produce meaningful information. AI technologies like ChatGPT use similar models for generating text, images, and other types of data.
• Time-series forecasting. MXNet can assist in developing time series forecasting applications capable of extracting hidden patterns in complex datasets. Insight from such apps can facilitate informed decision-making.

Apart from installing MXNet manually on your computer, you can also use it in other tools like Amazon SageMaker to build exciting AI-driven applications.

Advanced features and techniques

Other core features powering Apache MXNet include:

• Multi-language support. Apache MXNet supports multiple programming languages due to its language-specific APIs. For example, if you’re building models in Python, you can easily use the Gluon API to access desired functionality. You can also access compatible APIs in Python, Scala, and Julia. MXNet’s hybridization feature (which combines imperative and symbolic programming) also enables a smooth transition between different languages during development.
• Performance tuning and optimization. You can use techniques like low learning rates, data parallelism, and batch processing to optimize MXNet models for performance. Besides, implementing stochastic gradient descent can also help you minimize the loss function and enhance “learning.” Leveraging hardware components like GPUs and CUDA can also lead to accelerated computing. Also, consider using the Intel Math Kernel Library for Deep Learning (Intel MKL-DNN) when working with CPUs for better optimization.

MXNet community

MXNet is open-source, meaning you can dive into the source code and use it to craft your applications for free. However, it’s being deprecated. You can’t contribute to MXNet on Github because it’s been moved to the Attic—as Apache puts it.

Nevertheless, MXNet still has a vibrant community, which you can interact with on platforms like GitHub and Reddit.

Find deep learning experts on Upwork

Whether you’re looking to create computer vision apps or natural language processing tools, Apache MXNet provides the tools to get you started with your deep learning development journey. With its hybrid architecture, you can build applications using diverse programming languages like Python, Java, and C++. You can also run these applications on CPUs and leverage hardware accelerators like GPUs for enhanced performance.

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