Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications

By Tiago Carneiro, Raul Victor Medeiros Da Nóbrega, Thiago Nepomuceno, Guibin Bian, V. H. C. de Albuquerque, P. Filho. IEEE Access, 2018

490 citations recorded by the source corpus.

Google Colaboratory (also known as Colab) is a cloud service based on Jupyter Notebooks for disseminating machine learning education and research. It provides a runtime fully configured for deep learning and free-of-charge access to a robust GPU. This paper presents a detailed analysis of Colaboratory regarding hardware resources, performance, and limitations. This analysis is performed through the use of Colaboratory for accelerating deep learning for computer vision and other GPU-centric applications. The chosen test-cases are a parallel tree-based combinatorial search and two computer vision applications: object detection/classification and object localization/segmentation. The hardware under the accelerated runtime is compared with a mainstream workstation and a robust Linux server equipped with 20 physical cores. Results show that the performance reached using this cloud service is equivalent to the performance of the dedicated testbeds, given similar resources. Thus, this service can be effectively exploited to accelerate not only deep learning but also other classes of GPU-centric applications. For instance, it is faster to train a CNN on Colaboratory’s accelerated runtime than using 20 physical cores of a Linux server. The performance of the GPU made available by Colaboratory may be enough for several profiles of researchers and students. However, these free-of-charge hardware resources are far from enough to solve demanding real-world problems and are not scalable. The most significant limitation found is the lack of CPU cores. Finally, several strengths and limitations of this cloud service are discussed, which might be useful for helping potential users.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/6718ed5f9267960034155faa709e24988eb89fcc

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Citations in the corpus

Democratising deep learning for microscopy with ZeroCostDL4Mic (2021, methodology): “Google Colab is widely used in the data science community for developing DL projects 34 – 36 .”
A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting (2021, methodology): “For the second case study, the used dataset comes from ISO New England control area and its eight-wholesale load (ISO-NE) records [31].”
An optimized dense convolutional neural network model for disease recognition and classification in corn leaf (2020, background): “93 times faster on average than using all Linux server physical cores [25].”