Why use JVM (Java Virtual Machine) for deep learning

Deep learning is one of the revolutionary breakthroughs of the decade for enterprise application development. Today, majority of organizations and enterprises have to transform their applications to exploit the capabilities of deep learning. In this article, we will discuss how to leverage the capabilities of JVM (Java virtual machine) to build deep learning applications.

Entreprises prefer JVM

Major JVM languages used in enterprise are Java, Scala, Groovy and Kotlin. Java is the most widely used programming language in the world. Nearly all major enterprises in the world use Java in some way or the other. Enterprises use JVM based languages such as Java to build complex applications because JVM features are optimal for production applications. JVM applications are also significantly faster and require much fewer resources to run compared to their counterparts such as Python. Java can perform more computational operations per second compared to Python. Here is an interesting performance benchmarking for the same.

JVM optimizes performance benchmarks

Production applications represent a business and are very sensitive to performance degradation, latency, and other disruptions. Application performance is estimated from latency/throughput measures. Memory overload and high resource usage can influence above said measures. Applications that demand more resources or memory require good hardware and further optimization from the application itself. JVM helps in optimizing performance benchmarks and tune the application to the hardware’s fullest capabilities. JVM can also help in avoiding memory footprints in the application.

We have discussed on JVM features so far, but there’s an important context on why there’s a huge demand for JVM based deep learning in production. We’re going to discuss that next.

Python is undoubtedly the leading programming language used in deep learning applications. For the same reason, the majority of enterprise developers i.e, Java developers are forced to switch to a technology stack that they’re less familiar with. On top of that, they need to address compatibility issues and deployment in a production environment while integrating neural network models.

DeepLearning4J, deep learning library for JVM

Java Developers working on enterprise applications would want to exploit deployment tools like Maven or Gradle for hassle-free deployments. So, there’s a demand for a JVM based deep learning library to simplify the whole process. Although there are multiple deep learning libraries that serve the purpose, DL4J (Deeplearning4J) is one of the top choices.

DL4J is a deep learning library for JVM and is among the most popular repositories on GitHub. DL4J, developed by the Skymind team, is the first open-source deep learning library that is commercially supported. What makes it so special is that it is backed by ND4J (N-Dimensional Arrays for Java) and JavaCPP.

ND4J is a scientific computational library developed by the Skymind team. It acts as the required backend dependency for all neural network computations in DL4J. ND4J is much faster in computations than NumPy. JavaCPP acts as a bridge between Java and native C++ libraries. ND4J internally depends on JavaCPP to run native C++ libraries.

DL4J also has a dedicated ETL component called DataVec. DataVec helps to transform the data into a format that a neural network can understand. Data analysis can be done using DataVec just like Pandas, a popular Python data analysis library. Also, DL4J uses Arbiter component for hyperparameter optimization. Arbiter finds the best configuration to obtain good model scores by performing random/grid search using the hyperparameter values defined in a search space.

Why choose DL4J for your deep learning applications?

DL4J is a good choice for developing distributed deep learning applications. It can leverage the capabilities of Apache Spark and Hadoop to develop high performing distributed deep learning applications. Its performance is equivalent to Caffe in case multi-GPU hardware is used.

We can use DL4J to develop multi-layer perceptrons, convolutional neural networks, recurrent neural networks, and autoencoders. There are a number of hyperparameters that can be adjusted to further optimize the neural network training. The Skymind team did a good job in explaining the important basics of DL4J on their website. On top of that, they also have a gitter channel to discuss or report bugs straight to their developers.

If you are keen on exploring reinforcement learning further, then there’s a dedicated library called RL4J (Reinforcement Learning for Java) developed by Skymind. It can already play doom game!

DL4J combines all the above-mentioned components (DataVec, ND4J, Arbiter and RL4J) for the deep learning workflow thus forming a powerful software suite. Most importantly, DL4J enables productionization of deep learning applications for the business. If you are interested to learn how to develop real-time applications on DL4J, checkout my new book Java Deep Learning Cookbook.

In this book, I show you how to install and configure Deeplearning4j to implement deep learning models. You can also explore recipes for training and fine-tuning your neural network models using Java. By the end of this book, you’ll have a clear understanding of how you can use Deeplearning4j to build robust deep learning applications in Java.

Author Bio

Rahul Raj has more than 7 years of IT industry experience in software development, business analysis, client communication and consulting for medium/large scale projects. He has extensive experience in development activities comprising requirement analysis, design, coding, implementation, code review, testing, user training, and enhancements. He has written a number of articles about neural networks in Java and is featured by DL4J and Official Java community channel. You can follow Rahul on Twitter, LinkedIn, and GitHub.