Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications
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 Colaborato...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8485684/ |
| _version_ | 1818558474592714752 |
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| author | Tiago Carneiro Raul Victor Medeiros Da Nobrega Thiago Nepomuceno Gui-Bin Bian Victor Hugo C. De Albuquerque Pedro Pedrosa Reboucas Filho |
| author_facet | Tiago Carneiro Raul Victor Medeiros Da Nobrega Thiago Nepomuceno Gui-Bin Bian Victor Hugo C. De Albuquerque Pedro Pedrosa Reboucas Filho |
| author_sort | Tiago Carneiro |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T00:12:57Z |
| format | Article |
| id | doaj.art-e12376de91e44409ad01de977aa36d1e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T00:12:57Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e12376de91e44409ad01de977aa36d1e2022-12-21T23:25:41ZengIEEEIEEE Access2169-35362018-01-016616776168510.1109/ACCESS.2018.28747678485684Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning ApplicationsTiago Carneiro0https://orcid.org/0000-0002-6145-8352Raul Victor Medeiros Da Nobrega1https://orcid.org/0000-0003-4667-4222Thiago Nepomuceno2https://orcid.org/0000-0001-9609-5419Gui-Bin Bian3https://orcid.org/0000-0003-4708-2245Victor Hugo C. De Albuquerque4https://orcid.org/0000-0003-3886-4309Pedro Pedrosa Reboucas Filho5https://orcid.org/0000-0002-1878-5489Ciência e Tecnologia do Ceará, Instituto Federal de Educação, Fortaleza-CE, BrazilCiência e Tecnologia do Ceará, Instituto Federal de Educação, Fortaleza-CE, BrazilFraunhofer-Arbeitsgruppe für Supply Chain Services SCS, Nürnberg, GermanyState Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaPrograma de Pós-Graduação em Informática Aplicada, Universidade de Fortaleza, Fortaleza-CE, BrazilCiência e Tecnologia do Ceará, Instituto Federal de Educação, Fortaleza-CE, BrazilGoogle 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.https://ieeexplore.ieee.org/document/8485684/Deep learningColabconvolutional neural networksGoogle colaboratoryGPU computing |
| spellingShingle | Tiago Carneiro Raul Victor Medeiros Da Nobrega Thiago Nepomuceno Gui-Bin Bian Victor Hugo C. De Albuquerque Pedro Pedrosa Reboucas Filho Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications IEEE Access Deep learning Colab convolutional neural networks Google colaboratory GPU computing |
| title | Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications |
| title_full | Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications |
| title_fullStr | Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications |
| title_full_unstemmed | Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications |
| title_short | Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications |
| title_sort | performance analysis of google colaboratory as a tool for accelerating deep learning applications |
| topic | Deep learning Colab convolutional neural networks Google colaboratory GPU computing |
| url | https://ieeexplore.ieee.org/document/8485684/ |
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