Apache Spark

With the advent of big data rose the fame of Apache Spark. One of the most reliable and fast large-scale data processors, Spark is not new to the world of gigabytes and terabytes of number crunching and processing. In the last one and a half-decade, as the years unfolded, devices got smaller and more personal while user data got larger and more accessible. And one of the heroes of this revolution was Apache with its distributed file systems-based software and contribution to the open-source community.

The Spark became an instant hit with its ability to process huge sums of data and derives immense and detailed analysis out of these dates like never before. At that point, it made its way to the next revolution in the making, artificial intelligence; specifically deep learning or as people call it ‘the black box’. But it doesn’t make sense, does it?

Spark is a data analytics engine, what help can it provide on a neural network, for example? Why do you even need Spark that supports a cluster of systems while deep learning is essentially a single-node application? What are you gaining in the long run after all? And most importantly, when is it efficient to use Apache Spark for Deep Learning.

When is Apache Spark good enough for Deep Learning?

  • When You Want A Quick Implementation

In the new era of gigs of data being released by every user every day, storage, maintenance and retrieval of data poses a challenge to all organizations and companies, let alone processing that data through deep learning. To solve this problem, Apache Spark Implementation came out with advanced and incredibly fast solutions than ever before.

A great example would be of a simplified version of MapReduce that reduced the time and complexity of big data processing. Not only have that, data scientists often stated that the way to a good deep learning model is through systematic analysis of the raw data. Spark has an edge over every other engine here as well. With its unique and powerful analytics engine, it virtually eliminates the bottleneck of hefty work of data preprocessing.

It’s versatility also plays an important role in its adoption. From the creation of SQL data pipelines to applying machine learning models, it can fulfil a range of heavy functions.

  • When You Want To Indulge In Hyperparameter Tuning

This is one of the most integral techniques of a successful deep learning model. Some form of input, usually pictorial or sequence-based (such as images, sounds or videos) is taken by the model and mathematical operations on them are applied once they are ‘convolutionalized’ or transformed in general. This is done to facilitate conversion to a form that can be worked on by neural networks (a very simplified approach to how our brain works with data) or artificially intelligent models of similar kinds.

The true hold up in the past years has been that, even though hyperparameter training is possible in a parallel manner, the neural network as a whole is a sequential process. Here comes the role of Spark, its fault-tolerant distributed drive can not only drive the model faster by performing parallel computations but also increase the accuracy. This has made Apache Spark Implementation immensely popular.

Due to Apache Spark, on an average, the accuracy has increased on the same implementations on data sets by at least 30% while finishing the task up to seven times faster.

  • When Deploying Deep Learning Models On Big Data

Spark provides an incredible advantage over the centralized deployment of deep learning models. In this scenario, models are directly deployed within pipelines to perform heavy computations on each dataset. Spark’s distributive mechanism enables to send the parameters to the required location in a particular cluster first.

Now since the model has finally reached each node of the system, the parameters are finally processed with the actual input. After that it was specifically engineered to handle big data, running deep learning models on them become a piece of cake.

When NOT to USE Spark for Deep Learning

  • When You Know CUDA and Want The Greatness Of GPU

CUDA is the industry dominant framework for applying deep learning models on big data. So if you already know how to work with CUDA (which is extremely complex) and want an optimal, high performing flexible programming experience, you should skip using Spark and go for something comparatively more sophisticated. 

  • When You Don’t Care About Working In Scala/Java/Python

If you fall into this category, chances are you can already handle CUDA/GPU. CUDA, a parallel computing platform and application program interface, has been developed by Nvidia. If you have an Nvidia graphics card in your system, chances are you would love to exploit it using CUDA. In this case, you should chuck out other languages and should step out of your comfort zone to learn/use CUDA.

  • When You Are Out Doing Research

Research scientists say Spark is a tool for babies. Probably, they aren’t far from the truth. Spark is great for prototyping and testing your product, but it is in no way at par with the emerging technologies. If you are a researcher and are writing a paper on most cost or energy-efficient platforms, this is not the right option for you.

If you’re aiming to be the next tech hero in beating Google’s 14000-Machine Brain at recognizing Jennifer Aniston, you would do better not to touch Spark.

In conclusion, it may be said that while Spark is a powerful tool, it has its limitations being open source. However, you can still browse SparkNet which is used in training deep neural networks and check if it satisfies your needs.


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