如何在AWS上维持深度学习进程运行并后续重新连接？

Absolutely, you need to take specific steps to keep your long-running Python scripts alive after disconnecting from SSH—otherwise, the process will be terminated once your session ends. Here are the most reliable, commonly used methods for this scenario:

1. nohup (No Hang Up) #

This is the simplest, no-frills option for backgrounding a process that survives logout.

• How to use it:
  Run your script with the nohup command and append & to send it to the background:

  nohup python your_training_script.py &
  

• Where to find output:
  By default, all stdout/stderr will be saved to a file named nohup.out in your current directory. You can also redirect output to a custom log file for better organization:

  nohup python your_training_script.py > my_task_logs.txt 2>&1 &
  

  The 2>&1 part ensures error messages are also captured in the same log file.
• Manage the process:
  • Check if the process is running: ps aux | grep your_training_script.py
  • Stop the process: Use kill [process_id] (get the PID from the ps command output)

2. screen (Terminal Session Manager) #

If you want to be able to reconnect to your script's terminal later (to check real-time output or interact with it), screen is perfect.

• Setup (if not installed):
  On Debian/Ubuntu-based instances:

  sudo apt update && sudo apt install screen
  

• Basic workflow:
  1. Create a new named screen session:

     screen -S my_deep_learning_job
     

  2. Run your Python script as usual: python your_training_script.py
  3. Detach from the session (leaving it running in the background): Press Ctrl + A, then release both keys and press D
  4. Disconnect from SSH—your script keeps running!
  5. Later, reconnect to the session:

     screen -r my_deep_learning_job
     

• Useful commands:
  • List all active screen sessions: screen -ls
  • Kill a session (when you're done): screen -X -S [session_id] quit (get the ID from screen -ls)

3. tmux (Modern Terminal Multiplexer) #

tmux is similar to screen but more feature-rich (better window splitting, session management, and customization). It's become the go-to for many developers.

• Setup (if not installed):
  On Debian/Ubuntu-based instances:

  sudo apt update && sudo apt install tmux
  

• Basic workflow:
  1. Create a new named tmux session:

     tmux new -s my_long_running_task
     

  2. Run your Python script
  3. Detach from the session: Press Ctrl + B, then release both keys and press D
  4. Disconnect SSH—process continues running
  5. Reconnect later:

     tmux attach -t my_long_running_task
     

• Useful commands:
  • List active sessions: tmux ls
  • Kill a session: tmux kill-session -t my_long_running_task
  • Split the session window (for monitoring logs while running scripts): Ctrl + B then " (horizontal split) or % (vertical split)

Extra Tips for AWS #

• Always save your training checkpoints and outputs to an EBS volume or S3 bucket—if your EC2 instance is terminated accidentally, you won't lose progress.
• For more complex workflows, consider using AWS Batch or SageMaker to manage your jobs—these services handle process persistence, scaling, and monitoring out of the box, but they have a steeper learning curve than the terminal tools above.

内容的提问来源于stack exchange，提问作者user11128