gpumonitor

TF 2.x and PyTorch Lightning Callbacks for GPU monitoring

Showing:

3 Versions

0.1.2

0.1.1

Small release to improve some parts of the code

  • Indicators are aggregated on the fly, instead of storing every entry points of the epoch
  • Indicators are now integers to improve readbility

0.1.0

Initial release of gpumonitor. It includes the GpuMonitor element and 2 callbacks: one for TensorFlow and one for PyTorch Lightning. Usage is the following.

For custom script

If you want to monitor your custom script, you can simply execute it with a GpuMonitor initialized:

from gpumonitor.monitor import GpuMonitor

monitor = GpuMonitor(delay=1)

# Your own script here

monitor.stop()
monitor.display_average_stats_per_gpu()
Callbacks

For TensorFlow,

from gpumonitor.callbacks.tf import TFGpuMonitorCallback

model.fit(x, y, callbacks=[TFGpuMonitorCallback(delay=0.5)])

For PyTorch Lightning,

from gpumonitor.callbacks.lightning import PyTorchGpuMonitorCallback

trainer = pl.Trainer(callbacks=[PyTorchGpuMonitorCallback(delay=0.5)])
trainer.fit(model)