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A memory profiler for data batch processing applications.

Project description

The Fil memory profiler for Python

Your code reads some data, processes it, and uses too much memory. In order to reduce memory usage, you need to figure out:

  1. Where peak memory usage is, also known as the high-water mark.
  2. What code was responsible for allocating the memory that was present at that peak moment.

That's exactly what Fil will help you find. Fil an open source memory profiler designed for data processing applications written in Python, and includes native support for Jupyter.

At the moment it only runs on Linux and macOS.

"Within minutes of using your tool, I was able to identify a major memory bottleneck that I never would have thought existed. The ability to track memory allocated via the Python interface and also C allocation is awesome, especially for my NumPy / Pandas programs."

—Derrick Kondo

For more information, including an example of the output, see

Fil vs. other Python memory tools

There are two distinct patterns of Python usage, each with its own source of memory problems.

In a long-running server, memory usage can grow indefinitely due to memory leaks. That is, some memory is not being freed.

  • If the issue is in Python code, tools like tracemalloc and Pympler can tell you which objects are leaking and what is preventing them from being leaked.
  • If you're leaking memory in C code, you can use tools like Valgrind.

Fil, however, is not aimed at memory leaks, but at the other use case: data processing applications. These applications load in data, process it somehow, and then finish running.

The problem with these applications is that they can, on purpose or by mistake, allocate huge amounts of memory. It might get freed soon after, but if you allocate 16GB RAM and only have 8GB in your computer, the lack of leaks doesn't help you.

Fil will therefore tell you, in an easy to understand way:

  1. Where peak memory usage is, also known as the high-water mark.
  2. What code was responsible for allocating the memory that was present at that peak moment.
  3. This includes C/Fortran/C++/whatever extensions that don't use Python's memory allocation API (tracemalloc only does Python memory APIs).

This allows you to optimize that code in a variety of ways.


Assuming you're on macOS or Linux, and are using Python 3.6 or later, you can use either Conda or pip (or any tool that is pip-compatible and can install manylinux2010 wheels).


To install on Conda:

$ conda install -c conda-forge filprofiler


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

$ pip --version
pip 19.3.0

If you're using something older than v19, you can upgrade by doing:

$ pip install --upgrade pip

If that doesn't work, try running that in a virtualenv.

Assuming you have a new enough version of pip:

$ pip install filprofiler

Using Fil

Measuring peak (high-water mark) memory usage in Jupyter

To measure memory usage of some code in Jupyter you need to do three things:

  1. Use an alternative kernel, "Python 3 with Fil". You can choose this kernel when you create a new notebook, or you can switch an existing notebook in the Kernel menu; there should be a "Change Kernel" option in there in both Jupyter Notebook and JupyterLab.
  2. Load the extension by doing %load_ext filprofiler.
  3. Add the %%filprofile magic to the top of the cell with the code you wish to profile.

Screenshot of JupyterLab

Measuring peak (high-water mark) memory usage for Python scripts

Instead of doing:

$ python --input-file=yourfile

Just do:

$ fil-profile run --input-file=yourfile

And it will generate a report.

As of version 0.11, you can also run it like this:

$ python -m filprofiler run --input-file=yourfile

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

Reducing memory usage in your code

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.

How Fil works

Fil uses the LD_PRELOAD/DYLD_INSERT_LIBRARIES mechanism to preload a shared library at process startup. This shared library captures all memory allocations and deallocations and keeps track of them.

At the same time, the Python tracing infrastructure (used e.g. by cProfile and to figure out which Python callstack/backtrace is responsible for each allocation.

Fil and threading, with notes on NumPy and Zarr {#threading}

In general, Fil will track allocations in threads correctly.

First, if you start a thread via Python, running Python code, that thread will get its own callstack for tracking who is responsible for a memory allocation.

Second, if you start a C thread, the calling Python code is considered responsible for any memory allocations in that thread. This works fine... except for thread pools. If you start a pool of threads that are not Python threads, the Python code that created those threads will be responsible for all allocations created during the thread pool's lifetime.

Therefore, in order to ensure correct memory tracking, Fil disables thread pools in BLAS (used by NumPy), BLOSC (used e.g. by Zarr), OpenMP, and numexpr. They are all set to use 1 thread, so calls should run in the calling Python thread and everything should be tracked correctly.

This has some costs:

  1. This can reduce performance in some cases, since you're doing computation with one CPU instead of many.
  2. Insofar as these libraries allocate memory proportional to number of threads, the measured memory usage might be wrong.

Fil does this for the whole program when using fil-profile run. When using the Jupyter kernel, anything run with the %%filprofile magic will have thread pools disabled, but other code should run normally.

What Fil tracks

Fil will track memory allocated by:

  • Normal Python code.
  • C code using malloc()/calloc()/realloc()/posix_memalign().
  • 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 semantics are somewhat different than normal allocations or anonymous mmap(), since the OS can swap it in or out from disk transparently, so supporting this will involve a different kind of resource usage and reporting.
  • 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).
  • memfd_create(), a Linux-only mechanism for creating in-memory files.
  • Possibly memalign, valloc(), pvalloc(), reallocarray(). These are all rarely used, as far as I can tell.


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

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.

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