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High Memory Usage

Why This Alert Was Triggered​

Your session is using more than 90% of its allocated memory capacity. This can happen when:

  • Your code is processing large datasets
  • Your code has memory leaks or is accumulating data in memory without releasing it
  • The memory allocation for your session is too small for your workload

What This Means​

When memory usage is this high, your session is at risk of:

  • Becoming unresponsive or slow
  • Being terminated by the system if it exceeds its memory limit (Out of Memory kill)
  • Losing unsaved work if the session terminates unexpectedly

Steps to Remedy​

Immediate Actions​

  1. Commit and push any important changes to avoid losing them
  2. Free up memory:
    • Stop any running processes you don't need
    • Clear large variables from memory in your notebooks or scripts (e.g., del variable in Python)
    • Restart your Python kernel if using Jupyter notebooks

Longer-Term Solutions​

  1. Optimise your code:

    • Process data in smaller chunks
    • Use generators or iterators instead of loading full datasets
    • Delete variables you no longer need during execution
    • Use memory-efficient data types and libraries

  2. Request more memory:

    • Pause your current session
    • Modify the session resources to use a resource class with more memory, if available
    • Resume your session
    • See Resource Pools and Classes for more information

  3. Profile memory usage:

    • Use memory profiling tools to identify which parts of your code use the most memory
    • For Python: memory_profiler, tracemalloc, or guppy3
    • For R: pryr::mem_used() or profmem

Prevention​

  • Choose an appropriate resource class when starting sessions based on your expected workload
  • Test code with small datasets first to estimate memory requirements