Have you done distributed training/inference of models (using multiple GPUs)?  If yes, please mention the project in brief.

Yes, I have experience with distributed training/inference of models using multiple GPUs. One project where I utilized this technique was a computer vision project for object detection. The goal was to train a deep learning model to detect and classify various objects in images. Due to the large size of the dataset and the complexity of the model, training on a single GPU was not feasible within a reasonable time frame. Therefore, I implemented a distributed training approach using multiple GPUs. This involved partitioning the dataset and distributing the training process across multiple GPUs, allowing for parallel processing and faster training times. Additionally, during inference, the model was deployed on multiple GPUs to enable faster and more efficient predictions. The distributed training/inference approach significantly reduced the training time and improved the overall performance of the model.