Gluon MXNet
The open-source MXNet deep learning framework defines, trains, and deploys deep neural networks on mobile phones and cloud infrastructure. It is scalable, supports multiple languages and programming styles, and allows efficient model training.
With MXNet, you can combine imperative and symbolic programming styles to increase productivity and efficiency. It is based on a dynamic dependency scheduler that parallelises imperative and symbolic processes automatically as they happen. On top of that, a graph optimisation layer speeds up and optimises memory for symbolic execution.
The MXNet library is lightweight and portable. You can train models more quickly because to its acceleration with NVIDIA Pascal GPUs and scalability across numerous GPUs and nodes.
Why Apache MXNet?
The following are the main attributes and advantages that Apache MXNet provides:
Hybrid frontend
Without compromising training performance, the imperative symbolic hybrid Gluon API offers a simple method for model deployment, training, and prototyping. For applications like object detection, speech recognition, and recommendation engines, developers may create linear regression, CNN, and recurrent LSTM models with just a few lines of Gluon code.
Scalability
MXNet, which was created specifically for cloud infrastructure, makes use of a distributed parameter server that can expand nearly linearly with the help of several GPUs or CPUs. With near-linear scalability and auto-scaling, deep learning tasks can be split over several GPUs. According to tests conducted by Amazon Web Services, MXNet outperformed a single GPU by 109 times while running on a cluster of 128 GPUs. MXNet was chosen by AWS as its preferred deep learning framework above TensorFlow, Theano, and Torch because to its scalability across many GPUs (across various hosts), development speed, and portability.
Ecosystem
MXNet offers libraries and toolkits for time series, computer vision, natural language processing, and other fields.
Languages
MXNet supports JavaScript, Scala, Julia, C++, R, Python, and Matlab. MXNet compiles to C++, making it a lightweight neural network model for cloud servers or Raspberry Pi.
How is MXNet operated?
MXNet, also known as “mix-net,” was developed by a group of academic institutions and fostered at the Apache Software Foundation. It was intended to combine the benefits of two programming styles for deep learning model development: declarative or symbolic, which concentrates on “what” should be done, and imperative, which outlines precisely “how” computation is carried out.
Imperative Programming Mode
The main tool for storing and altering data in MXNet is the NDArray, which has imperative programming. The inputs and outputs of a model can be represented and worked with as multi-dimensional arrays using NDArray. NumPy’s ndarrays and NDArray are comparable, however NDArray can operate on GPUs to speed up computation.
The benefit of imperative programming is that developers with experience in procedural programming will recognise it, and it is more suited for interactive debugging and parameter modifications.
Symbolic Programming Mode
Layers of nested functions are applied to input parameters in neural networks to alter input data. A linear function and a nonlinear transformation make up each layer. By calculating these parameters’ partial derivatives, or gradients, in relation to a loss metric, deep learning aims to optimise these parameters, which include weights and biases. When using forward propagation, the neural network uses the input parameters to generate a confidence score that is sent to the nodes in the subsequent layer until it reaches the output layer, when the score’s error is computed. Back propagation is a technique used in gradient descent to improve the model by sending the mistakes back through the network and adjusting the weights.
Data structures called graphs are made up of connected nodes, also known as vertices, and edges. Neural networks are represented as a graph structure of computations, and this is the foundation of all contemporary deep learning frameworks.
Using computation graphs, MXNet symbolic programming enables the abstract definition of functions. Complex functions are initially defined in terms of placeholder values when using symbolic programming. By tying these functions to actual values, they can then be used. Additionally, predefined neural network layers made possible by symbolic programming enable the simple expression of complex models with reduced repetition and improved performance.
Hybrid Programming Mode with the Gluon API
The Gluon hybrid programming interface, which bridges the gap between imperative and symbolic interfaces while retaining the benefits and capabilities of each, is one of MXNet’s main advantages. A simple language to learn, Gluon generates quick portable models. Using NDArray, you can imperatively prototype your model with the Gluon API. For quicker model training and inference, you may then use the hybridise command to switch to symbolic mode. With the backend MXNet executor, the model operates more quickly in symbolic mode as an optimised graph. It is also simple to export for inference in several language bindings, such as C++ or Java.
Why MXNet Is Better on GPUs
A small number of cores and a large amount of cache memory make up the CPU’s architecture, which allows it to manage several software threads at once. A GPU, on the other hand, is made up of hundreds of cores that have the capacity to manage thousands of threads at once.
Neural nets are inherently extremely parallel since they are constructed from a large number of similar neurones. Compared to CPU-only training, this parallelism offers a notable compute speedup and adapts readily to GPUs. For this reason, GPUs have emerged as the preferred platform for training big, intricate neural network-based systems. Additionally, inference procedures’ parallel nature makes them suitable for GPU execution.
Deep learning neural networks are now an essential tool for image recognition, audio recognition, language translation, and more in many industries because to better algorithms, larger datasets, and GPU-accelerated processing. The purpose of MXNet’s development was to provide developers with strong tools to fully utilise cloud computing and GPU capabilities.
In other words, an MXNet training algorithm runs quicker the more GPUs you use to work on it. With almost linear speed increases when more GPUs are used, the framework excels at scalable performance. Another benefit for performance optimisation is that MXNet was built to scale automatically based on the GPUs that are available.
Use Cases
Smartphone Apps
MXNet is a strong option for mobile phone deployment since it supports models that operate on low-power, limited-memory platforms and is well-suited for image recognition. It has been demonstrated that MXNet-built models produce incredibly dependable picture recognition outcomes when used natively on laptops. Strong distributed applications in fields like object and scene identification, augmented reality, and more may be made possible by combining local and cloud processors.
Applications for picture and voice recognition also provide fascinating opportunities for those with disabilities. For instance, smartphone apps could assist those with hearing impairments in translating voice interactions into text and those with vision impairments in better perceiving their environment.
Autonomous Vehicles
For self-driving cars and trucks to make judgements almost instantly, they need to digest vast amounts of data. Distributed computing is being used to an unprecedented extent in the sophisticated networks that are emerging to support fleets of autonomous vehicles. These networks coordinate everything from traffic management throughout a city to the brake decisions made by a single automobile.
MXNet was selected as the fundamental platform for the development of artificial intelligence models by TuSimple, which is creating an autonomous freight network of mapped routes that enable autonomous cargo shipments throughout the southwestern U.S. The business is introducing self-driving technology to a sector that is beset by a persistent driver shortage and costly overhead brought on by accidents, shift work, and fuel inefficiencies.
MXNet was selected by TuSimple due to its scalability, training effectiveness, and cross-platform compatibility. One of the factors was a benchmark that compared MXNet to TensorFlow and discovered that MXNet was more accurate, quicker, and memory-efficient in a setting with eight GPUs.
