localperf 1.1

Measure of python performance in local.

Local Python performance measurement

This project aims to create an easy-to-use package for measuring the performance of python in any machine, in terms of CPU, multiprocessing and GPU (pytorch with CUDA), and also verify that the GPU is used.

Installation

Install the requirements in requirements.txt, then install from source:

pip install localperf

Usage

You can measure the performance of your machine in terms of CPU, multiprocessing and GPU (pytorch and tensorflow) by running the commands below.

Relevant arguments for visualization are:

• --plot : plot the results (default False)
• --log_dir [log directory] : directory where to save the results (default no logging)
• --image_dir [image directory]: directory where to save the images (default no image saving)
• --no-progess : do not show progress bar (default behavior is to show)

CPU

To measure the performance of your machine in terms of CPU, run the following command:

python3 -m localperf.cpu

Relevant arguments for the benchmark are:

• log_n_data [log n data] : maximum number of data to do the benchmark (in log10 scale)
• n_measures [n measures] : number of measures to do for each data size

Multiprocessing

To measure the performance of your machine in terms of multiprocessing, run the following command:

python3 -m localperf.multiprocessing

Relevant arguments for the benchmark are:

• log_n_data [log n data] : maximum number of data to do the benchmark (in log10 scale)
• log2_n_process [log2 n process] : maximum number of processes to do the benchmark (in log2 scale)
• n_measures [n measures] : number of measures to do for each data size

GPU (pytorch)

Install CUDA for pytorch

First, install pytorch with CUDA following the instructions on the pytorch website. If this code returns True, it means pytorch and CUDA are installed and its a good sign your GPU is used but this is not a guarantee:

import torch
print(torch.cuda.is_available())

You may use the command nvidia-smi to check if your GPU is recognized by the system.

nvidia-smi

This give you information about each GPU recognized by the system : the name, the VRAM used. You can run this command in a separate terminal to see the GPU usage during your code :

watch -n 0.1 nvidia-smi

Run the benchmark

To measure the performance of your machine in terms of GPU, run the following command:

python3 -m localperf.gpu_torch

Relevant arguments for the benchmark are:

• log_n_data [log n data] : maximum number of data to do the benchmark (in log10 scale)
• n_measures [n measures] : number of measures to do for each data size
• n_measures_gpu [n measures gpu] : number of measures to do for each data size, on the GPU. If not specified, the same number of measures as on the CPU is done.