在AWS Ubuntu实例运行Python Pickle程序遇Killed错误，本地Mac正常

Hey there! Let's break down why your script is crashing with a "killed" message on your AWS instance—this is super common, and it's almost always tied to resource limits on cloud servers vs. your local Mac.

Most Likely Cause: Not Enough Memory #

AWS free-tier instances (like t2.micro) only have 1GB of RAM, which is way less than the typical Mac's memory. Loading large pickle files or processing NLTK data can quickly eat up all available RAM, and the Linux kernel will kill the Python process to prevent the system from freezing.

How to Confirm & Fix This #

• First, check your current memory usage:

  free -h
  

  If you see Mem usage is near 100% before running the script, that's your problem.
• Quick fixes:
  1. Upgrade your instance temporarily: Switch to a t2.small (2GB RAM) or larger instance while running the script, then switch back if you want to save costs.
  2. Add swap space (virtual memory):

     # Create a 2GB swap file
     sudo fallocate -l 2G /swapfile
     sudo chmod 600 /swapfile
     sudo mkswap /swapfile
     sudo swapon /swapfile
     

     After your script finishes, you can disable swap with sudo swapoff /swapfile if you don't need it.

Other Possible Issues to Check #

• File permissions: Make sure your script has read access to documents.pickle and write access to save new pickle files. Run ls -l to check permissions, and adjust with chmod 644 documents.pickle if needed.
• Corrupted pickle file: Sometimes files get messed up during transfer to AWS. Try re-uploading documents.pickle using scp or AWS S3, then test loading it with a simple script:

  import pickle
  with open("documents.pickle", "rb") as f:
      docs = pickle.load(f)
  print(f"Loaded {len(docs)} documents successfully")
  

• Missing dependencies: Double-check that NLTK and scikit-learn are installed correctly on Ubuntu:

  pip3 install --upgrade nltk scikit-learn
  python3 -m nltk.downloader punkt averaged_perceptron_tagger
  

内容的提问来源于stack exchange，提问作者Aswin