While Artificial Intelligence (AI) is booming on GPUs, building a Machine Learning (ML) infrastructure remains a complicated process. Deep Learning (DL) and ML have resulted in innovative hardware. However, developing applications on such an advanced infrastructure is complex.
Building the ML application is a herculean task that arouses significant interest among developers in data science platforms. These can automate the effort to pack libraries and other software items into containers. The goal is to run software successfully on an ML infrastructure in a GPU cloud, making the application faster.
Cloud providers, Hadoop distributors, ML specialists, and others have introduced cloud infrastructure platforms to support ML and AI applications, and we have dozens of different languages, tools, and libraries available to cover the AI and ML applications’ development.
All these tools aim to give a larger group of developers the ability to quickly and easily implement highly-iterative neural and AI workloads. Here are the top languages, libraries, and tools available:
Appropriate Programming Languages
If developers want to engage in ML, they need to know which programming languages are available to them. Currently, Scala, Python, Java, and R are available, appropriate programming languages.
A commonly debated question that data scientists argue over asks, “Which programming language is best suited for writing programs and applications when it comes to ML algorithms within the Apache Spark framework?” The Apache Spark open-source engine is widely used for in-memory data processing computing and machine-learning with support for Python, Scala, R, and Java ML language.
Whether it’s Assembler vs. Fortran in the 1970s or C++ vs. Pascal in the 1990s, the choice of language is determined by subjective preferences, individual expertise, and the developer’s experience. While the benefits are quick prototyping and a fast go-to-market, in the long run, it is not a decent choice for programming sustainable models to address business use cases nor for handling the amount of information and data processing required. The following points should be noted when choosing a programming language for ML applications as well as the design of each business model:
Scala and Java are known as compiled languages, which means that the source code is compiled into bytecode before execution. On the other hand, the Spark engine is written in Scala. Code written in Scala and Java run natively on the Java Virtual Machine (JVM).
The most popularly used languages, such as Python and R, are interpreted languages. For these languages, an interpreter performs the program directly and translates each language statement into more subroutines. For compilation, the interpreter then translates the subroutines into another programming language so that it can be successfully executed on the JVM.
In most cases, compiled languages (Scala and Java) offer better overall performance than interpreted languages (R and Python). However, it is advisable to tentatively create a profile of the application to determine the extent to which the language can be used effectively. This tip is especially useful for small applications.
Parallelism (concurrency) means that a job is not finished until all work and sub-threads have been processed. This ensures that all threads have the same start and end time.
Since Scala runs on the JVM, it has full permission to use the multithreading capabilities of the JVM. However, by default, Scala is not limited to the concept of threads to achieve parallelization. There are other, more profound options for achieving parallelism, such as Actors or Futures.
Both R and Python do not support true parallelism and multithreading. Multithreading, as used for several ML use cases, can only run in parallel for some input and output (IO) tasks, but for CPU-bound and multi-core tasks, multithreading runs on its own. This creates more overhead in dealing with storage and data processing jobs.
From a usability point-of-view, languages such as R and Python handle variable types at program runtime, permitting developers to develop applications quickly. During the type check in runtime, restriction of variables and data types are checked and enforced by the interpreter if necessary.
Statically-typed languages, such as Java and Scala, perform a type check during compilation. However, maintaining apps over an extended period is much more comfortable with Java and Scala than with R and Python.
Java is verbose, which means that Java-based applications require more lines of executable code compared to Python, Scala, and R to achieve the same operation. Besides, Java is not known to provision a read-evaluate-print loop (REPL). That makes Java more challenging to use with preferred data science tools like Jupyter Notebook.
Since Apache Spark is written in Scala, extensive knowledge in Scala can help developers understand how Spark works internally. Also, Application Programming Interfaces (APIs) of new features will always initially be available in Scala and Java. Python APIs are usually in continuous development but are updated in later versions.
Machine Learning Beginners
Python, Java, and Scala are functional, object-oriented languages, whereas R is functional and procedural. Python is more analytically-oriented as well as easier to learn and use. Also, Python is less detailed and easier to read than Java or Scala; the easier-to-understand syntax makes Python ideal for those who do not have much programming experience.
Scala and Java are more developer-oriented and suitable for engineers with more programming experience. R was developed for statisticians, academics, and data scientists, and it is frequently used for data visualization.
Community and Enterprise Support
In community and enterprise support, Java, R, and Python have a clear benefit over Scala. R and Python have an ample, more productive ecosystem with easily-accessible packages. These packages implement most of the standard models and methods that are widely used in various businesses and universities.
In 2011, a team of developers embarked on a project to design a web application from which it would be possible to code with many programming languages. In 2015, this project gave birth to Jupyter Notebook.
Jupyter Notebook is a web-based app that permits the editing and execution of notebooks, which are documents containing text in markdown, images, interactive visualizations, and executable code derived from more than 100 programming languages, including Julia, R, and Python. It also offers building blocks for interactive computing with data: a file explorer, terminals, and a text editor.
Over time, Jupyter Notebook has made its mark and has built a community of millions of users in areas such as data science, ML, education, and more. However, in recent years, the Jupyter team noted that Jupyter Notebook is difficult to customize and extend, mainly since it is built on web technologies from 2011. Also, to offer a more modern and extensible application, Jupyter project developers have created a more advanced application named JupyterLab.
As stated by the project’s team, “JupyterLab is an interactive development environment for working with notebooks, code, and data. More importantly, JupyterLab is providing full support for Jupyter notebooks. Also, JupyterLab allows you to use text editors, data file viewers, terminals, and other custom-required components side by side with notebooks in a tabbed workspace.”
Specifically, the project developers explain that it is possible to perform the following tasks with JupyterLab:
- Drag and drop to rearrange the cells of a notebook and copy between notebooks;
- Execute code blocks interactively from text files (.py, .md, .R, .tex, etc.);
- Associate a code support console with a notebook kernel to interactively explore the code without cluttering the notebook with a temporary job;
- Edit popular file formats, such as JSON, Markdown, Vega, CSV, VegaLite, and more, with live previews.
JupyterLab does more than the points mentioned above. It is designed to create personalization options for developers to create ML-based applications. All features, including notebook documents, terminals, the file browser, and the menu system, appear in this new web application in the form of extensions.
Developers who want to can add other features to JupyterLab as well as develop their own features. To add these extensions, developers can use the extension development API.
The search for even smarter applications and systems has shown the limitations of traditional tools used in their design. The conventional algorithms have certain drawbacks when developing next-generation applications, such as reasoning like a human, conversing dynamically with a person, recognizing shapes in images, recognizing objects in videos, etc.
When looking to develop these smarter applications and systems, using AI makes sense. AI can provide scalable capabilities to machines that rely on AI to fulfill tasks that are usually difficult for traditional algorithms to perform.
For several years, Google has also launched AI and ML-related projects by implementing the first version of its ML system called DistBelief. After years of research and improvements to DistBelief, engineers are able to simplify their basic code, enabling the tool to be faster and more robust.
In 2015, DistBelief evolved into TensorFlow, Google’s second generation of the ML system that is integrated with several of Google’s products. TensorFlow is a library dedicated to numerical computation using graphs of data flow. In 2015, Google announced the open source licensing of TensorFlow to allow field experts to make contributions to this project in hopes of accelerating its development.
When TensorFlow’s first stable release occurred, Google announced, “TensorFlow is incredibly fast.” A compiler called Accelerated Linear Algebra (XLA) supports TensorFlow’s graphs, and XLA is able to target the main processors (CPUs) and graphics processors (GPUs) for better performance. The performance of TensorFlow increases significantly when used with graphics processors.
In the subsequent releases of TensorFlow, many new APIs have been added along with the introduction of the new tf.keras module, which offers compatibility with Keras – another popular library with high-level neural networks.
Google also introduced Skflow, a simplified interface for ML and TensorFlow Slim. Skflow includes a lightweight library for defining, training, and evaluating models in TensorFlow. With this new addition, many new high-level API modules are also available.
TensorFlow APIs can be used with Python and C languages. However, in addition to Python and C languages, TensorFlow APIs can be used with C++, Java, and Go, as well.
Scikit-learn is one of the reference libraries for ML in Python. Its popularity stems from its large number of implemented algorithms as well as its straightforward and coherent interface that is designed to make life easier for beginners. For example, all the default parameter choices typically give quite good results.
Unlike Torch, TensorFlow, Caffe, or CNTK, scikit-learn does not focus on deep neural networks. At most, it has a multilayer perceptron (MLP), and some other techniques that are far from stabilized.
Within the various scikit-learn versions, scikit-learn 0.20 will be the latest version to work with Python 2.7 and 3.4. Python 3.5 (released in 2015) will be the minimum required for later scikit-learn versions.
This compatibility decision reflects the choice of NumPy to stop any compatibility with Python 2 at the end of 2018 for new features. Also, Python 3.4 will be out of the game because the newer versions offer a lot of facilities to the developers of scikit-learn.
To make it easier for beginners to use the library, scikit-learn’s documentation now provides a complete glossary. In particular, the glossary mentions a whole series of parameters available for learning algorithms (like n_outputs).
A major novelty of scikit-learn 0.20 is the management of ‘dirty’ data. Instead of having to use another library like Pandas to manage this data, scikit-learn provides a whole array of functions to handle them.
At the community level, scikit-learn now has a foundation that is legally hosted by Inria. The foundation will be used to collect donations from individuals and sponsorships and also to ensure that the project can hire contributors to work exclusively on scikit-learn.
With this foundation, scikit-learn will be able to focus on more ambitious functionalities. In doing so, they can eliminate the constraints of other jobs, especially at the level of parallelism, without having to go through the administrative restrictions specific to financing research.
Matplotlib is probably one of the most used Python packages for 2D graphical representation. It provides a fast way to visualize data using the Python language and also provides high-quality illustrations in various formats.
IPython is an enhanced Python interactive console that supports a lot of great features including named I/O, direct use of shell commands, improved debugging, and much more. By launching this console with the -pylab argument, we immediately have an interactive Matplotlib session with features we used to see in Matlab or Mathematica.
Matplotlib comes with a default set of parameters that allow you to customize specific properties. You can control the default settings for many properties, including chart size, dpi (dots per inch), line weight, colors, styles, views, guides, grids, text, fonts, and more. Although the default settings will work in most instances, you may need to change some settings for more specific cases.
A careful presentation of the marker argument (this argument allows to make scatter plots using the plot function) is an integral part of the final rendering of a ready-to-print graphic. Matplotlib provides a fully-customizable plotting library like GNUplot to create publication quality figures.
DL framework PyTorch is Facebook’s ML framework. It is the successor to 2002’s Torch, an open source ML library based on the Lua programming language.
Based on Python, PyTorch can take advantage of the main Python packages, such as NumPy. This framework uses dynamic graphs, and its ML algorithms are developed using the standard Python control flow.
Thus, Python developers are able to control PyTorch more efficiently. Further, it is also easier to create complex algorithms like recurrent neural networks. PyTorch is not Keras-compatible, but there are other APIs, like Ignite and Scorch, that are compatible with Keras.
The Keras API is intended for users to develop AI applications and provides a positive user experience. Keras has unlocked DL features to more developers and individuals without previous ML experience.
Microsoft’s Cognitive Toolkit (CNTK), a toolkit dedicated to DL and AI, supports Keras. Microsoft ensures that Keras follows the best practices to reduce cognitive load; it provides consistent APIs, reduces the number of user-called actions required for everyday use cases, and provides clear, useful feedback on user errors.
The SciPy library contains numerous toolboxes dedicated to scientific computing methods and ML. Its different submodules correspond to scientific applications, such as integration, interpolation, image processing, optimization, mathematical functions, statistics, etc.
SciPy can be compared to other standard scientific computing libraries, such as the GSL (GNU Scientific Library for C and C++) or Matlab toolboxes. SciPy is THE library to use in Python for scientific routines because it works perfectly on NumPy arrays or matrices, allowing NumPy and SciPy to interact together.
Before implementing a scientific function, it is best to check that the function is not already implemented in the SciPy library. As scientists are not always programming experts, they often tend to want to “reinvent the wheel,” which leads them to produce code that is often buggy, difficult to maintain, un-interoperable, and un-optimized. In contrast, SciPy routines have been optimized and tested and, therefore, should be used where possible.
If you want to work in data science or ML, NumPy is invaluable. NumPy is used to perform calculations on large volumes of data, but to understand topics such as ML, you first need to follow some basic underlying concepts.
Installing NumPy is effortless; merely install it through Python Package Index Utility (PIP). If you want to train for ML, then you can get one of the precompiled packages, like Anaconda, that includes the necessary libraries, including NumPy.
NumPy is the backbone of scientific computing in Python. It’s also a general-purpose n-dimensional container for data that is widely used for data science and ML.
Software engineering has always been primarily the art of compromise between conflicting requirements. Ultimately, when it comes to choosing an ML environment, it all depends on what problem is being addressed, what experiences and capabilities are available, and what amount of data needs to be processed.
Another consideration is whether to create a fast prototype application or a sustainable, enterprise-wide application. Python offers rapid prototyping and development, while Scala and Java are the better choices for processing large volumes of data and enterprise implementations. R is right for specific needs that are best addressed by R or for transferring an existing R environment to the Apache Spark platform.