Update Simple_MultiGPU_GP_Regression.ipynb
"\n",
"In this notebook, we'll demonstrate training exact GPs on large datasets using two key features from the paper https://arxiv.org/abs/1903.08114: \n",
"\n",
- "1. The ability to distribute the kernel matrix across multiple GPUs, thereby using additional parallelism.\n",
- "2. Partitioning the kernel in to chunks that are computed on-the-fly when performing each MVM so as to reduce memory usage enough.\n",
+ "1. The ability to distribute the kernel matrix across multiple GPUs, for additional parallelism.\n",
+ "2. Partitioning the kernel into chunks computed on-the-fly when performing each MVM to reduce memory usage.\n",
"\n",
"We'll be using the `protein` dataset, which has about 37000 training examples. The techniques in this notebook can be applied to much larger datasets, but the training time required will depend on the computational resources you have available: both the number of GPUs available and the amount of memory they have (which determines the partition size) have a significant effect on training time."
]
