filprofiler 0.8.0

A memory profiler for data batch processing applications.

The Fil memory profiler for Python

Fil a memory profiler designed for data processing applications. At the moment it only runs on Linux and macOS.

Your code reads some data, processes it, and—uses too much memory. What you need to reduce is peak memory usage.

And that's exactly what Fil will help you find: exactly which code was responsible for allocating memory at peak memory usage.

For more information see https://pythonspeed.com/products/filmemoryprofiler/

What Fil tracks

Fil will track memory allocated by:

• Normal Python code.
• C code using malloc()/calloc()/realloc().
• C++ code using new (including via aligned_alloc).
• Anonymous mmap()s.
• Fortran 90 explicitly allocated memory (tested with gcc's gfortran).

Still not supported, but planned:

• mremap() (resizing of mmap()).
• File-backed mmap(). The usage here is inconsistent since the OS can swap it in or out, so probably supporting this will involve a different kind of resource usage.
• Other forms of shared memory, need to investigate if any of them allow sufficient allocation.
• Anonymous mmap()s created via /dev/zero (not common, since it's not cross-platform, e.g. macOS doesn't support this).
• posix_memalign(), memalign, valloc(), pvalloc(), reallocarray().
• memfd_create().

Installation

Prerequisites

To install the latest version of Fil you'll need Pip 19 or newer. You can check like this:

$ pip --version
pip 20.0.2

If you're using something older than that, do:

$ pip install --upgrade pip

If that doesn't work, try running that a virtualenv or Conda env.

Installing Fil

Assuming you're on macOS or Linux, and are using Python 3.6 or later:

$ pip install filprofiler

Measuring peak (high-water mark) memory usage

Instead of doing:

$ python yourscript.py --input-file=yourfile

Just do:

$ fil-profile run yourscript.py --input-file=yourfile

And it will generate a report.

Debugging out-of-memory crashes

First, run free to figure out how much memory is available—in this case about 6.3GB—and then set a corresponding limit on virtual memory with ulimit:

$ free -h
       total   used   free  shared  buff/cache  available
Mem:   7.7Gi  1.1Gi  6.3Gi    50Mi       334Mi      6.3Gi
Swap:  3.9Gi  3.0Gi  871Mi
$ ulimit -Sv 6300000

Then, run your program under Fil, and it will generate a SVG at the point in time when memory runs out:

$ fil-profile run oom.py 
...
=fil-profile= Wrote memory usage flamegraph to fil-result/2020-06-15T12:37:13.033/out-of-memory.svg

You've found where memory usage is coming from—now what?

If you're using data processing or scientific computing libraries, I have written a relevant guide to reducing memory usage.

License

Copyright 2020 Hyphenated Enterprises LLC

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

 http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.