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A Python library for analyzing hierarchical performance data

Project description

hatchet Hatchet

Build Status Read the Docs codecov Code Style: Black

Hatchet is a Python-based library that allows Pandas dataframes to be indexed by structured tree and graph data. It is intended for analyzing performance data that has a hierarchy (for example, serial or parallel profiles that represent calling context trees, call graphs, nested regions’ timers, etc.). Hatchet implements various operations to analyze a single hierarchical data set or compare multiple data sets, and its API facilitates analyzing such data programmatically.

To use hatchet, install it with pip:

$ pip install llnl-hatchet

Or, if you want to develop with this repo directly, run the install script from the root directory, which will build the cython modules and add the cloned directory to your PYTHONPATH:

$ source install.sh

Documentation

See the Getting Started page for basic examples and usage. Full documentation is available in the User Guide.

Examples of performance analysis using hatchet are available here.

The hatchet tutorial provides a step by step introduction to hatchet features through a live interactive environment in BinderHub.

Interactive Visualization

Hatchet now provides an interactive visualization which can be run inside of your Jupyter notebooks. It is dependant on different mechanisms for building which we describe here:

The software in the hatchet/vis subdirectory requires Node.js and Node Package Manager for the development and building of javascript code.

Installing node packages

One you have Node and NPM installed on your system, you can install all necessary node packages by running the following line in your terminal from the hatchet/vis/ directory:

npm install

Building out javascript code with webpack

To build out javascript into the static bundles used by the Jupyter visualizations, run the following line from the hatchet/vis directory in your terminal:

npm run build

Alterntinatively if you are developing a visualization, you can run the following line to force bundles to automatically update when you change the javascript source code:

npm run watch

Contributing

Hatchet is an open source project. We welcome contributions via pull requests, and questions, feature requests, or bug reports via issues.

Authors

Many thanks go to Hatchet's contributors.

Citing Hatchet

If you are referencing Hatchet in a publication, please cite the following paper:

  • Abhinav Bhatele, Stephanie Brink, and Todd Gamblin. Hatchet: Pruning the Overgrowth in Parallel Profiles. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '19). ACM, New York, NY, USA. DOI

License

Hatchet is distributed under the terms of the MIT license.

All contributions must be made under the MIT license. Copyrights in the Hatchet project are retained by contributors. No copyright assignment is required to contribute to Hatchet.

See LICENSE and NOTICE for details.

SPDX-License-Identifier: MIT

LLNL-CODE-741008

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SHA256 190c31b8cddf24a9d022db1f021efbc9386f56d264600edcc41ccb9435595a6b
MD5 995523598413e418cc22e2b9e935c405
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MD5 a3c3d68b9490dfef468f8108781b3399
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SHA256 b103aa9d9b60f374bcfe42f85c4de920cf4c6c55e11d26c93fec2614e48b3624
MD5 3c07931a9554e318345882d33c3a4193
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SHA256 c9fd3665ce24aea0c77567bba945efb344c4e6a94d2809ea875f5dd0a222c2fa
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SHA256 fce3ca0db4590950b81d869419ae83ecd1f2f858105b38379f1a74cb4bfb41c2
MD5 808f127bf569f49ca2f2ca03f1550ec4
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SHA256 df9ed3361c65fe7c70a5b5679678e796eb88b9bde759ddc281cfcd774e01e9b2
MD5 b1aa23d5edcccf93a557e83d7b7afa8f
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SHA256 1886c1ff6b3de991a7f52409980f0f6bef5655f1e88ef28fb5d02fc434cf6fbc
MD5 0f73464ccfa78c922b7529cdcdfc2009
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SHA256 68093ff83311828c75dd3bf0c8f1495ca8d8a47645acd79bc889d75e64a6c2c5
MD5 309b3f733cd3e9a1d8db477efdbc79f6
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SHA256 fce24ecb0deaaf9428d86396dda7e35ebc2d4ca4df4d9d5847bca0caa942e792
MD5 23d3ddb6c48412d26929a0f2cda1d198
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