Scaling deep learning workloads. Applications in computer vision and seismology
(English) Deep learning techniques have an enormous impact on the state-of-the-art in many fields, such as computer vision, natural language processing, audio analysis and synthesis, and many others. The increasing computing power, the increasing amount of available data, and the algorithms' ev...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/690154 |
| Acceso en línea: | http://hdl.handle.net/10803/690154 https://dx.doi.org/10.5821/dissertation-2117-403149 |
| Access Level: | acceso abierto |
| Palabra clave: | Àrees temàtiques de la UPC::Informàtica 004 |
| Sumario: | (English) Deep learning techniques have an enormous impact on the state-of-the-art in many fields, such as computer vision, natural language processing, audio analysis and synthesis, and many others. The increasing computing power, the increasing amount of available data, and the algorithms' evolution foster this impact. On the one hand, this thesis applies Deep Learning techniques to large parallel systems to train and validate Neural Networks models for different applications. First, a technology stack to enable the distribution of deep learning workloads on a traditional High Performance Computing (HPC) setup such as the MareNostrum supercomputer is designed and evaluated. The key element of the deployed layered architecture is Apache Spark, which enables to isolate machine-learning applications from the particularities of the infrastructure, in this case, the MareNostrum supercomputer. The deployment of Spark-enabled clusters over MareNostrum is not trivial and it has done with the help of Spark4MN, a custom interoperability layer. On top of this stack (Marenostrum, Spark4MN and Spark) a deep learning specific layer is placed, DL4J. The goal is to provide insights into how the job configuration on a traditional HPC setup can be optimized to efficiently run this kind of workloads. The derived conclusions should be useful to guide similarly complex deployments in the future. Second, in a derived work, a use case is explored. We design and train deep CNNs for annotating and filtering images from social media (Instagram and Twitter). We capture the images in real-time and processes them by multiple CNNs that automatically enrich their metadata with tags that describe their visual content and also how they fit the visual identity of a brand (VBI) . With this method, we have trained VBI classifiers for more than 10 real brands and more than 100 classifiers for generic description of social media images. On the other hand, this thesis applies Deep Learning techniques on a computer cluster to train multiple NN configurations employed for earthquake detection and location. First, we develop a new method called UPC-UCV, consisting of applying a convolutional neural network to single-station 3-channel waveforms for P-wave earthquake detection and source region estimation in north-central Venezuela. This part includes the build of a new dataset, CARABOBO, that has been made public for reproducibility and benchmarking purposes. Both the UPC-UCV network and the CARABOBO dataset are the first developed for this geographic region. The method obtains better results than the State of the Art (SOA), yielding higher detection accuracy (13.3 percentage point increase) for the new target seismicity. UPC-UCV achieves a 95.27% detection accuracy. Second, in a derived work, we focus on the source region estimation problem. Source region estimation is a relaxed version of the earthquake location problem that consists on, first, partitioning a study area into K geographic subdivisions and, second, attempting to determine to which one the earthquake epicenter belongs. In the previous work, we performed the partitioning with k-means. In this part, we experiment with a geographical subdivision provided by a seismologist, consisting on irregular polygons covering the main seismic faults of Venezuela. While the obtained results for a small number of geographic subdivisions are not better than the ones obtained with k-means clustering, the good results obtained with a large number of subdivisions (91.78% with K::;:; 10) outperform the k-means approach (66.10%). It should be noted that to obtain these results, the use of spatial-based techniques significantly improved the final model. This confirms the target hypothesis that the source region estimation accuracy is significantly increased if the geographical partitioning is performed considering the regional geophysical characteristics such as the tectonic plate boundaries. |
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