TF 2.x and PyTorch Lightning Callbacks for GPU monitoring
Changes:
Small release to improve some parts of the code
Initial release of gpumonitor. It includes the GpuMonitor
element and 2 callbacks: one for TensorFlow and one for PyTorch Lightning. Usage is the following.
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()
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)