Skip to main content

A simple IPython extension for monitoring memory usage of Jupyter notebook cells.

Project description

memprofiler

PyPI Binder

memprofiler is a simple extension for monitoring memory usage of Jupyter notebook cells.

Installation

It can be installed as a typical Python source package from PyPi using pip:

pip install memprofiler

Usage

A basic example of how to use this extension can be found in this interactive Jupyter notebook.

Reference

mprof_run

%mprof_run [-q] [-i INTERVAL] [-p] [profile_id]

Run memory profiler during cell execution. (cell_magic)

  • positional arguments:

    • profile_id
      Profile label. You can specify up to two keywords by separating them with && (keyword0&&keyword1). Only profile_ids with two keywords can be used in plot-related functions.
  • optional arguments:

    • -q, --quiet
      Suppress verbosity.

    • -i INTERVAL, --interval INTERVAL
      Sampling period (in seconds), default 0.01.

    • -p, --plot
      Plot the memory profile.

mprof_plot

%mprof_plot [-t TITLE] [--groupby {0,1}] profile_ids [profile_ids ...]

Plot detailed memory profiler results. (line_magic)

  • positional arguments:

    • profile_ids
      Profile identifiers made by mprof_run. Supports regex.
  • optional arguments:

    • -t TITLE, --title TITLE
      String shown as plot title.

    • --groupby <{0,1}>
      Identifier number used to group the results, default 1.

mprof_barplot

%mprof_barplot [-t TITLE] [--variable {time,memory}] [--barmode {group,stack}] [--groupby {0,1}] profile_ids [profile_ids ...]

Plot only-memory or only-time results in a bar chart. (line_magic)

  • positional arguments:

    • profile_ids
      Profile labels made by mprof_run. Supports regex.
  • optional arguments:

    • -t TITLE, --title TITLE
      String shown as plot title.

    • --variable <{time,memory}>
      Variable to plot, default 'memory'.

    • --barmode <{group,stack}>
      Bar char mode, default 'group'.

    • --groupby <{0,1}>
      Identifier number used to group the results, default 1.

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated!

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

Distributed under the BSD 3-Clause License. See LICENSE for more information.

Acknowledgements

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

memprofiler-0.1.5.tar.gz (5.5 kB view details)

Uploaded Source

File details

Details for the file memprofiler-0.1.5.tar.gz.

File metadata

  • Download URL: memprofiler-0.1.5.tar.gz
  • Upload date:
  • Size: 5.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.0.0.post20201207 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.8.5

File hashes

Hashes for memprofiler-0.1.5.tar.gz
Algorithm Hash digest
SHA256 e67919076bc20a2a770258ab7d5ff127cf673db026a318faaf8d802b1a033252
MD5 31824e4927fa6c7bb44185ba7a004032
BLAKE2b-256 696f31aebc3a58e6c9745fb72ad9af1545c0143e89cbe3541c15db823f1e49de

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page