Initial commit

MIT License

Copyright (c) 2022

Permission is hereby granted, free of charge, to any person obtaining a copy

of this software and associated documentation files (the "Software"), to deal

in the Software without restriction, including without limitation the rights

to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

copies of the Software, and to permit persons to whom the Software is

furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all

copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

SOFTWARE.

# GTrace

Code and dataset for GTrace.

## Evaluation of Accuracy

We provide dataset B for evaluation. The dataset is under the `dataset` folder.

- Install Python 3.8+ on your system.

- Run `pip3 install -r requirements.txt` to install the dependencies.

- Run `python3 -m tracegnn.models.gtrace.main` to start training. The evaluation will automatically starts after training.

- If you want to run on GPU, you can modify the `device` in `tracegnn/models/config.py`.

## Evaluation of Time Efficiency

We provide the code for the `Anomaly Detection` module and `Graph Building` module.

To evaluate the time efficiency, we provide a minimal example and a trained model that can be run directly on your local device without deployment:

- Run `cd deployment`.

- Install `GCC 9.3.0+`, `make` and `CMake 3.2+` on your device. Run `bash build.sh` to download and build the dependencies.

- Run `sh run_local.sh` to evaluate the time efficiency.

- Install `Intel SVML` to get better performance on Intel CPU. (See https://numba.readthedocs.io/en/stable/user/performance-tips.html#intel-svml).

## Visualization Tool

- Run `python3 -m tracegnn.visualization.webviewer_server`.

- Visit `http://localhost:12312/0` or `http://localhost:12312/1` to see the visualization results for two example cases.

# Reference

- LRUCache11: https://github.com/mohaps/lrucache11.git

- Kubernetes: https://kubernetes.io/

- PyTorch: https://pytorch.org/

- DGL: https://dgl.ai/

- CMake: https://cmake.org/

[run]

branch = True

source = mltk

[report]

exclude_lines =

    pragma: no cover

    if self\.debug

    raise AssertionError

    raise NotImplementedError

    if 0:

    if __name__ == .__main__.:

ignore_errors = True

omit =

    tests/*

    scripts/*

    examples/*

    setup.py

.idea

*.iml

*.lock

.DS_Store

/debug.py

/results

# Created by https://www.gitignore.io/api/python

# Edit at https://www.gitignore.io/?templates=python

### Python ###

# Byte-compiled / optimized / DLL files

__pycache__/

*.py[cod]

*$py.class

# C extensions

*.so

# Distribution / packaging

.Python

build/

develop-eggs/

dist/

downloads/

eggs/

.eggs/

lib/

lib64/

parts/

sdist/

var/

wheels/

pip-wheel-metadata/

share/python-wheels/

*.egg-info/

.installed.cfg

*.egg

MANIFEST

# PyInstaller

#  Usually these files are written by a python script from a template

#  before PyInstaller builds the exe, so as to inject date/other infos into it.

*.manifest

*.spec

# Installer logs

pip-log.txt

pip-delete-this-directory.txt

# Unit test / coverage reports

htmlcov/

.tox/

.nox/

.coverage

.coverage.*

.cache

nosetests.xml

coverage.xml

*.cover

.hypothesis/

.pytest_cache/

# Translations

*.mo

*.pot

# Django stuff:

*.log

local_settings.py

db.sqlite3

# Flask stuff:

instance/

.webassets-cache

# Scrapy stuff:

.scrapy

# Sphinx documentation

docs/_build/

# PyBuilder

target/

# Jupyter Notebook

.ipynb_checkpoints

# IPython

profile_default/

ipython_config.py

# pyenv

.python-version

# pipenv

#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.

#   However, in case of collaboration, if having platform-specific dependencies or dependencies

#   having no cross-platform support, pipenv may install dependencies that don’t work, or not

#   install all needed dependencies.

#Pipfile.lock

# celery beat schedule file

celerybeat-schedule

# SageMath parsed files

*.sage.py

# Environments

.env

.venv

env/

venv/

ENV/

env.bak/

venv.bak/

# Spyder project settings

.spyderproject

.spyproject

# Rope project settings

.ropeproject

# mkdocs documentation

/site

# mypy

.mypy_cache/

.dmypy.json

dmypy.json

# Pyre type checker

.pyre/

# End of https://www.gitignore.io/api/python

Copyright 2019 ML Essentials Contributors

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.