Memory Profiling

Peak memory usage is a common concern in GPU programming because GPU memory is typically smaller than available CPU memory. To easily identify memory hotspots, cuDF provides a memory profiler. It comes with an overhead so avoid using it in performance-sensitive code.

Enabling Memory Profiling

First, enable memory profiling in RMM by calling rmm.statistics.enable_statistics(). This adds a statistics resource adaptor to the current RMM memory resource, which enables cuDF to access memory profiling information. See the RMM documentation for more details.

Second, enable memory profiling in cuDF by setting the memory_profiling option to True. Use cudf.set_option() or set the environment variable CUDF_MEMORY_PROFILING=1 prior to the launch of the Python interpreter.

To get the result of the profiling, use cudf.utils.performance_tracking.print_memory_report() or access the raw profiling data by using: cudf.utils.performance_tracking.get_memory_records().

Example

In the following, we enable profiling, do some work, and then print the profiling results:

>>> import cudf
>>> from cudf.utils.performance_tracking import print_memory_report
>>> from rmm.statistics import enable_statistics
>>> enable_statistics()
>>> cudf.set_option("memory_profiling", True)
>>> cudf.DataFrame({"a": [1, 2, 3]})  # Some work
   a
0  1
1  2
2  3
>>> print_memory_report()  # Pretty print the result of the profiling
Memory Profiling
================

Legends:
ncalls       - number of times the function or code block was called
memory_peak  - peak memory allocated in function or code block (in bytes)
memory_total - total memory allocated in function or code block (in bytes)

Ordered by: memory_peak

ncalls memory_peak memory_total filename:lineno(function)
     1          32           32 cudf/core/dataframe.py:690(DataFrame.__init__)
     2           0            0 cudf/core/index.py:214(RangeIndex.__init__)
     6           0            0 cudf/core/index.py:424(RangeIndex.__len__)