Estudo comparativo de plataformas de Deep Learning: Apache Singa, Graphlab e H2O
Resumo
Técnicas de Deep learning vêm mostrando avanços em várias tarefas de aprendizado de máquina. Porém a implementação dessas técnicas é muito complexa. Assim, para ajudar na implementação de projetos de Deep Learning, plataformas estão sendo criados. Já existe uma quantidade considerável destas plataformas disponível. Isso acaba trazendo uma dificuldade na escolha de quem procura começar um projeto. Com o objetivo de auxiliar nesta escolha, este trabalho faz um estudo comparativo entre algumas plataformas: Apache Singa, Graphlab e H2O. Experimentos são conduzidos utilizando os conjunto de dados MNIST e KDD Cup 1999. Resultados apontam que as plataformas testadas têm suas vantagens: Graphlab é a mais intuitiva, a Apache Singa oferece mais recursos e H2O obteve os melhores resultados de predição.Referências
Bahrampour, S., Ramakrishnan, N., Schott, L., and Shah, M. (2015). Comparative study of deep learning software frameworks. In arXiv preprint arXiv:1511.06435.
Candel, A., Parmar, V., LeDell, E., and Arora, A. (2016). Deep learning with H2O. H2O.ai, Inc.
Deng, L. and Yu, D. (2014). Deep learning: methods and applications. Foundations and Trends in Signal Processing, 7(3–4):197–387.
Duarte, D. and Stahl, N. (2019). Machine learning: a concise overview. In Data Science in Practice, pages 27–58. Springer.
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., and Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187:27–48.
Kovalev, V., Kalinovsky, A., and Kovalev, S. (2016). Deep learning with Theano, Torch, Caffe, TensorFlow, and Deeplearning4J: Which one is the best in speed and accuracy? In XIII International Conference on Pattern Recognition and Information Processing.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.
Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A., and Hellerstein, J. M. (2012). Distributed GraphLab: a framework for machine learning and data mining in the cloud. Proceedings of the VLDB Endowment, 5(8):716–727.
Ng, S. S. Y., Zhu, W., Tang, W. W. S., Wan, L. C. H., and Wat, A. Y. W. (2016). An independent study of two deep learning platforms H2O and Singa. In 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).
Ooi, B. C., Tan, K.-L., Wang, S., Wang, W., Cai, Q., Chen, G., Gao, J., Luo, Z., Tung, A. K. H., Wang, Y., Xie, Z., Zhang, M., and Zheng, K. (2015). SINGA: A distributed deep learning platform. In ACM Multimedia.
Shatnawi, A., Al-Bdour, G., Al-Qurran, R., and Al-Ayyoub, M. (2018). A comparative study of open source deep learning frameworks. In 9th ICICS, pages 72–77. IEEE.
Zimmerman, D. W. (1997). Teacher’s corner: A note on interpretation of the pairedsamples t test. Journal of Educational and Behavioral Statistics, 22(3):349–360.
Candel, A., Parmar, V., LeDell, E., and Arora, A. (2016). Deep learning with H2O. H2O.ai, Inc.
Deng, L. and Yu, D. (2014). Deep learning: methods and applications. Foundations and Trends in Signal Processing, 7(3–4):197–387.
Duarte, D. and Stahl, N. (2019). Machine learning: a concise overview. In Data Science in Practice, pages 27–58. Springer.
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., and Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187:27–48.
Kovalev, V., Kalinovsky, A., and Kovalev, S. (2016). Deep learning with Theano, Torch, Caffe, TensorFlow, and Deeplearning4J: Which one is the best in speed and accuracy? In XIII International Conference on Pattern Recognition and Information Processing.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.
Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A., and Hellerstein, J. M. (2012). Distributed GraphLab: a framework for machine learning and data mining in the cloud. Proceedings of the VLDB Endowment, 5(8):716–727.
Ng, S. S. Y., Zhu, W., Tang, W. W. S., Wan, L. C. H., and Wat, A. Y. W. (2016). An independent study of two deep learning platforms H2O and Singa. In 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).
Ooi, B. C., Tan, K.-L., Wang, S., Wang, W., Cai, Q., Chen, G., Gao, J., Luo, Z., Tung, A. K. H., Wang, Y., Xie, Z., Zhang, M., and Zheng, K. (2015). SINGA: A distributed deep learning platform. In ACM Multimedia.
Shatnawi, A., Al-Bdour, G., Al-Qurran, R., and Al-Ayyoub, M. (2018). A comparative study of open source deep learning frameworks. In 9th ICICS, pages 72–77. IEEE.
Zimmerman, D. W. (1997). Teacher’s corner: A note on interpretation of the pairedsamples t test. Journal of Educational and Behavioral Statistics, 22(3):349–360.
Publicado
13/09/2021
Como Citar
FANK, Elias Augusto; SCHREINER, Geomar A.; DUARTE, Denio.
Estudo comparativo de plataformas de Deep Learning: Apache Singa, Graphlab e H2O. In: ESCOLA REGIONAL DE BANCO DE DADOS (ERBD), 16. , 2021, Santa Maria.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 11-20.
ISSN 2595-413X.
DOI: https://doi.org/10.5753/erbd.2021.17234.
