PyTorch Profiler 1.9更新啦~新功能提供開發人員更便利的操作與程式設計彈性
Pytorch profiler提供開發人員監控神經網路運行時所使用的各項資源功能，詳細的列出各個神經網路層使用的各項資源(CPU、memory、GPU…等等)。相較於前幾版的Pytorch profiler，最新版本的 Profiler 提供了更多的精細監控指標(神經網路中的各層數值)與視覺化功能，最大的差異在於Jump to Source Code 功能，提供了開發人員更便利的操作與程式設計彈性。
The goal of this new release (previous PyTorch Profiler release) is to provide you with new state-of-the-art tools to help diagnose and fix machine learning performance issues regardless of whether you are working on one or numerous machines. The objective is to target the execution steps that are the most costly in time and/or memory, and visualize the work load distribution between GPUs and CPUs.
Here is a summary of the five major features being released:
1. Distributed Training View: This helps you understand how much time and memory is consumed in your distributed training job. Many issues occur when you take a training model and split the load into worker nodes to be run in parallel as it can be a black box. The overall model goal is to speed up model training. This distributed training view will help you diagnose and debug issues within individual nodes.
2. Memory View: This view allows you to understand your memory usage better. This tool will help you avoid the famously pesky Out of Memory error by showing active memory allocations at various points of your program run.
3. GPU Utilization Visualization: This tool helps you make sure that your GPU is being fully utilized.
4. Cloud Storage Support: Tensorboard plugin can now read profiling data from Azure Blob Storage, Amazon S3, and Google Cloud Platform.
5. Jump to Source Code: This feature allows you to visualize stack tracing information and jump directly into the source code. This helps you quickly optimize and iterate on your code based on your profiling results.