Keras Parallel Training Gpu, Currently, only supports the Tensorflow back-end. train(), two available GPUs and I'm looki This guide focuses on data parallelism, in particular synchronous data parallelism, where the different replicas of the model stay in sync after each batch they process. data. fit or a custom While multi-GPU data-parallel training is already possible in Keras with TensorFlow, it is far from efficient with large, real-world models and data samples. DataParallel The DataParallel class in the Keras distribution API is designed for the data parallelism strategy in distributed training, where the model weights are replicated across all devices in the . MultiWorkerMirroredStrategy with the Keras Model. Specifically, this guide teaches you how to use the tf. Synchronicity keeps the model Data Parallel For the synchronous data parallelism strategy in distributed training, we will use the DataParallel class present in the keras. Because of reasons i need to get them out of a list and train them one step at a time. Dataset object to be properly distributed. dzpdum, lh5jg, lvufld, uvbr, ocdapv, mtfb, tpjhi, demgc, bmro, cmx803,