The document introduces new functions of ReNom RG. It mentions functions like storing and managing registration information, updating registration information, and printing registration cards. It provides details on the functions such as being able to store information like name, date of birth, address etc. and being able to update stored information. It also mentions the ability to print registration cards with the stored information on them.
This document discusses optimizations for deep learning frameworks to better utilize CPU and GPU resources. It describes how an executor can parallelize operations like convolution and matrix multiplication across multiple devices. The executor aims to support distributed training on multiple GPUs with techniques like data parallelism and model parallelism to improve training speed. It is compatible with Python APIs and can be used to benchmark the training of CNN models on different numbers of GPUs.
The document introduces new functions of ReNom RG. It mentions functions like storing and managing registration information, updating registration information, and printing registration cards. It provides details on the functions such as being able to store information like name, date of birth, address etc. and being able to update stored information. It also mentions the ability to print registration cards with the stored information on them.
This document discusses optimizations for deep learning frameworks to better utilize CPU and GPU resources. It describes how an executor can parallelize operations like convolution and matrix multiplication across multiple devices. The executor aims to support distributed training on multiple GPUs with techniques like data parallelism and model parallelism to improve training speed. It is compatible with Python APIs and can be used to benchmark the training of CNN models on different numbers of GPUs.