This repository contains an example of programmatic monitoring of Nvidia GPUs in C++ using NVML library.

Refer to Monitoring Nvidia GPUs using API article for details.

Core deliverables of this project are contained in separate directories and are the following:

monitor

Collects metrics from GPU devices.

Console application, written in C++17.

data_extractor

Extracts metrics data from monitor's output.

Console application, written in Python 3.

device_data_filter

Filters metrics of a specific device from full data set obtained via data_extractor.

Console application, written in Python 3.

data_visualizer

Plots metrics of a specific GPU device.

Console application, written in Python 3.

This section briefly describes steps needed to be taken to get each deliverable ready for execution.

The monitor component is written in C++17 with build instructions defined in cmake.

Hence, one needs a cmake and a C++ frontend supporting std:c++17 to build the monitor.

Having those both installed, the monitor build steps are the following:

mkdir build
cd build
cmake ..

The data_extractor component uses Python 3.7+.

Has no build steps.

The device_data_filter component uses Python 3.7+.

Its dependencies are listed at:

device_data_filter/requirements.txt

Dependencies installation example:

pip install -r device_data_filter/requirements.txt

The data_visualizer component uses Python 3.7+.

Its dependencies are listed at:

data_visualizer/requirements.txt

Dependencies installation example:

pip install -r data_visualizer/requirements.txt

Current section contains brief usage description for components provided by this repository.

NVML shared library must be present in the system and its directory must be listed in system's PATH variable in order to run the monitor.

Usually, it comes installed with graphics driver on Windows and has to be installed additionally on Linux-based systems.

The monitor does not take any parameters.

Basic usage:

./monitor

Watching monitor and persisting logs on Windows:

./monitor | Tee-Object -FilePath "monitor.log"

Watching monitor and persisting logs on Linux-base systems:

./monitor | tee "monitor.log"

Executable of the data_extractor component is located at:

./data_extractor/run.py

Its usage doc is listed below:

usage: run.py [-h] [monitor_log] [extracted_data]

Extract data from monitor log

positional arguments:
  monitor_log     path to monitor log file (default: -)
  extracted_data  path to extracted data file (default: -)

optional arguments:
  -h, --help      show this help message and exit

As stated in the doc, the component accepts 2 positional arguments.

Both of them are optional and default to std streams.

Example of reading from stdin and writing to stdout:

cat /path/to/captured/monitor.log | ./data_extractor/run.py

Same with specifying std streams explicitly:

cat /path/to/captured/monitor.log | ./data_extractor/run.py - -

Example of using files:

./data_extractor/run.py /path/to/captured/monitor.log /path/to/output/data_all.csv

Or same using output redirection:

./data_extractor/run.py /path/to/captured/monitor.log > /path/to/output/data_all.csv

Example of reading from a file and writing to stdout:

./data_extractor/run.py /path/to/captured/monitor.log

Executable of the device_data_filter component is located at:

./device_data_filter/run.py

Its usage doc is listed below:

usage: run.py [-h] [device_index] [input_data] [output_data]

Filter monitoring data for a specific device

positional arguments:
  device_index  index of device to filter data by (default: 0)
  input_data    path to monitor log file (default: -)
  output_data   path to extracted data file (default: -)

optional arguments:
  -h, --help    show this help message and exit

As stated in the doc, the component accepts 3 optional positional arguments.

device_index defaults to 0 and the rest defaults to std streams, just as in case of data_extractor.

Example of filtering data of device with index 0 using files:

./device_data_filter/run.py 0 /path/to/output/data_all.csv /path/to/output/data_0.csv

Executable of the data_visualizer component is located at:

./data_visualizer/run.py

Its usage doc is listed below:

usage: run.py [-h] [output_file_path_format] [input_data]

Visualize device data

positional arguments:
  output_file_path_format
                        format of output file paths (default:
                        monitor.{suffix}.png)
  input_data            path to device data file (default: -)

optional arguments:
  -h, --help            show this help message and exit

As stated in the doc, the component accepts 2 optional positional arguments:

• path format for output files, where {suffix} will be replaced with a suffix like full, 0, 1 and so on (see docs/monitor.*.png for output examples).
• path to data filtered by device_data_filter component.

Usage plotting data for device 0:

./data_visualizer/run.py '/path/to/output/monitor.{suffix}.csv' /path/to/output/data_0.csv