How to monitor PyTorch running status on CentOS

To efficiently monitor PyTorch operation status on CentOS system, you can adopt the following strategies to choose the appropriate solution for different needs:

1. 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 the watch command:

    watch -n 1 nvidia-smi

   This will update the GPU status display every second.

2. 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.

3. Process monitoring (top/ps): top and ps commands can also view process resource usage. For example, use ps in conjunction with grep 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.

4. 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.

5. 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.

6. 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.

7. 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.

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