## 4 Results
### Performance Evaluation of Distributed Frameworks  
The validation metrics used to evaluate the performance of the ResNet50 model in the computing environments were accuracy and loss. While it is possible to incorporate additional metrics, such as recall, F1 score, specificity, or precision, in this study, we chose to focus solely on accuracy and loss to avoid making the study overly complex and to focus on analyzing the behavior of the model throughout the training epochs for each dataset. Table 6 presents the results obtained for these metrics when using the PyTorch DDP and distributed TensorFlow strategies. For Dataset 38-Cloud, the best accuracy score was obtained using PyTorch DDP on Google Colab, with accuracy values above 97%. Meanwhile, for the MSLD, the best accuracy score was obtained using Distributed Tensorflow in Google Colab, with accuracy values close to 84%. Finally, for CIFAR-100, the best accuracy score was obtained by Workstation when implementing DDP from PyTorch, with accuracy values close to 89%. Furthermore, as shown in Table 6, specifically for the CIFAR-100 dataset, the accuracy and loss are both very low when implementing Distributed Tensorflow in all three computing environments. While the codes were homologous across libraries, this abnormal behavior was observed in this dataset. This could be due to various factors, such as the implementation of data enhancements, the distribution of training and validation data, hyperparameter management, or simply because the dataset is not well suited to DDP or Distributed TensorFlow with the applied configurations. This opens the door to investigating why this situation occurs in the first place. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Infrastructure** & **Dataset** & \multicolumn{3}{c}{**Distributed Data Parallel PyTorch**} & \multicolumn{3}{c}{**Distributed Tensorflow**} \\ \hline  & & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Loss**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Loss**} \\ \hline  & & **Training** & **Validation** & **Training** & **Validation** & **Training** & **Validation** & **Training** & **Validation** \\ \hline \multirow{3}{*}{Workstation} & Cloud & 0.9649 & 0.9672 & 0.0990 & 0.0863 & 0.9027 & 0.9121 & 0.2135 & 0.2688 \\  & MSLD & 0.8042 & 0.7590 & 0.5050 & 0.4830 & 0.8349 & 0.8070 & 0.3861 & 0.3884 \\  & CIFAR 100 & 0.8876 & 0.4892 & 0.6431 & 1.9475 & 0.1917 & 0.1695 & 3.4325 & 3.6841 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Low- \\ Scalability \\ DS-HPC \\ \end{tabular} } & Cloud & 0.7687 & 0.7951 & 0.4118 & 0.3721 & 0.9305 & 0.9125 & 0.1676 & 0.2303 \\  & MSLD & 0.6511 & 0.6278 & 0.7229 & 0.7953 & 0.7504 & 0.7171 & 0.5183 & 0.5532 \\  & CIFAR 100 & 0.7077 & 0.7038 & 1.0173 & 1.038 & 0.1258 & 0.1192 & 3.9149 & 3.9252 \\ \hline \multirow{3}{*}{Google Colab} & Cloud & 0.9713 & 0.9710 & 0.0795 & 0.0730 & 0.9557 & 0.1106 & 0.9558 & 0.1183 \\  & MSDL & 0.7722 & 0.7857 & 0.5235 & 0.5065 & 0.8392 & 0.8960 & 0.3688 & 0.3454 \\ \cline{1-1}  & CIFAR 100 & 0.6202 & 0.4637 & 1.5279 & 2.0078 & 0.1037 & 0.1089 & 4.0670 & 4.0532 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Validation metric values (accuracy and loss) obtained from the computational infrastructures.