How to monitor PyTorch running status on CentOS
Apr 14, 2025 pm 03:54 PMTo efficiently monitor PyTorch operation status on CentOS system, you can adopt the following strategies to choose the appropriate solution for different needs:
-
GPU monitoring (nvidia-smi): If you use an NVIDIA GPU and have CUDA and cuDNN installed,
nvidia-smi
command is an ideal tool for monitoring GPU resource utilization, memory footprint, and temperature. Real-time monitoring can be done using thewatch
command:watch -n 1 nvidia-smi
This will update the GPU status display every second.
-
System-level process monitoring (htop):
htop
is an interactive process viewer that can intuitively display the resource consumption of all processes, including your PyTorch process. Installation method:sudo yum install htop
Run
htop
to view detailed process information. -
Process monitoring (top/ps):
top
andps
commands can also view process resource usage. For example, useps
in conjunction withgrep
to find PyTorch process:ps aux | grep python
This lists all the processes that contain "python" from which you need to find your PyTorch process.
PyTorch built-in exception detection: PyTorch's
torch.autograd.set_detect_anomaly(True)
can help detect gradient calculation exceptions during backpropagation and assist in troubleshooting problems.Custom logging: Add logging function to PyTorch code to record key indicators during training, such as loss value, accuracy, etc., in order to track the progress of model training.
-
TensorBoard Visualization: While TensorBoard is a tool for TensorFlow, it can also be used in conjunction with PyTorch. The
torch.utils.tensorboard
module allows you to log training data to TensorBoard for visual monitoring and analysis through the browser interface.from torch.utils.tensorboard import SummaryWriter writer = SummaryWriter('runs/experiment-1') # Record data in training loop writer.add_scalar('Loss/train', loss.item(), epoch) writer.close()
Then run:
tensorboard --logdir=runs
Visit
http://localhost:6006
to view the monitoring interface. Third-party monitoring tools (Prometheus/Grafana): For more advanced monitoring needs, third-party tools such as Prometheus and Grafana can monitor various system indicators, including CPU, memory, disk I/O, etc., providing more comprehensive system-level monitoring.
Which monitoring method to choose depends on your specific needs and the type of information you want to monitor. Generally, a combination of methods can be combined to obtain more comprehensive and accurate monitoring of PyTorch operation status.
The above is the detailed content of How to monitor PyTorch running status on CentOS. For more information, please follow other related articles on the PHP Chinese website!

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