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توضیحات

Adversarial Robustness Test Bed

ویژگی	مقدار
سیستم عامل	-
نام فایل	armory-testbed-0.9.1
نام	armory-testbed
نسخه کتابخانه	0.9.1
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	-
ایمیل نویسنده	Two Six Technologies <armory@twosixtech.com>
آدرس صفحه اصلی	-
آدرس اینترنتی	https://pypi.org/project/armory-testbed/
مجوز	-

<div align="center"> <img src="/docs/assets/logo.png" width="50%" title="Armory Logo"> </div> [![CI][ci-badge]][ci-url] [![PyPI Status Badge][pypi-badge]][pypi-url] [![PyPI - Python Version][python-badge]][python-url] [![License: MIT][license-badge]][license-url] [![Docs][docs-badge]][docs-url] [![Code style: black][style-badge]][style-url] [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7561756.svg)](https://doi.org/10.5281/zenodo.7561756) # Overview Armory is a testbed for running scalable evaluations of adversarial defenses. Configuration files are used to launch local or cloud instances of the Armory docker containers. Models, datasets, and evaluation scripts can be pulled from external repositories or from the baselines within this project. Our evaluations are created so that attacks and defenses may be interchanged. To do this we standardize all attacks and defenses as subclasses of their respective implementations in the [Adversarial Robustness Toolbox (ART)](https://github.com/Trusted-AI/adversarial-robustness-toolbox) hosted by the LF AI & Data Foundation (LFAI). # Installation & Configuration TLDR: Try Armory [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/twosixlabs/armory/blob/master/notebooks/running_armory_scenarios_interactively.ipynb) or follow the instructions below to install locally. ```bash pip install armory-testbed ``` Upon installing armory, a directory will be created at `~/.armory`. This user specific folder is the default directory for downloaded datasets, model weights, and evaluation outputs. To change these default directories simply run `armory configure` after installation. If installing from the git repo in editable mode, ensure that your pip version is 22+. # Usage There are three ways to interact with Armory's container system. ## armory run * `armory run <path/to/config.json>` This will run a [configuration file](//github.com/twosixlabs/armory/raw/master/docs/configuration_files.md) end to end. Stdout and stderror logs will be displayed to the user, and the container will be removed gracefully upon completion. Results from the evaluation can be found in your output directory. * `armory run <path/to/config.json> --interactive` This will launch the framework-specific container specified in the configuration file, copy the configuration file into the container, and provide the commands to attach to the container in a separate terminal and run the configuration file end to end while attached to the container. A notable use case for this would be to debug using pdb. Similar to non-interactive mode, results from the evaluation can be found in the output directory. To later close the interactive container simply run CTRL+C from the terminal where this command was ran. ## armory launch * `armory launch <armory|pytorch-deepspeech>` This will launch a framework specific container, with appropriate mounted volumes, for the user to attach to for debugging purposes. A command to attach to the container will be returned from this call, and it can be ran in a separate terminal. To later close the interactive container simply run CTRL+C from the terminal where this command was ran. * `armory launch <armory|pytorch-deepspeech> --jupyter`. Similar to the interactive launch, this will spin up a container for a specific framework, but will instead return the web address of a jupyter lab server where debugging can be performed. To close the jupyter server simply run CTRL+C from the terminal where this command was ran. ## armory exec * `armory exec <armory|pytorch-deepspeech> -- <cmd>` This will run a specific command within a framework specific container. A notable use case for this would be to run test cases using pytest. After completion of the command the container will be removed. Note: Since Armory launches Docker containers, the python package must be run on system host (i.e. not inside of a docker container). ### Example usage: ```bash pip install armory-testbed armory configure git clone https://github.com/twosixlabs/armory-example.git cd armory-example armory run official_scenario_configs/cifar10_baseline.json ``` ### What is available in the container: All containers have a pre-installed armory package so that baseline models, datasets, and scenarios can be used. Additionally, volumes (such as your current working directory) will be mounted from your system host so that you can modify code to be run, and retrieve outputs. For more information on these mounts, please see our [Docker documentation](//github.com/twosixlabs/armory/raw/master/docs/docker.md#docker-volume-mounts) # Scenarios Armory provides several baseline threat-model scenarios for various data modalities. When running an armory configuration file, the robustness of a defense will be evaluated against that given scenario. For more information please see our [Scenario Documentation](//github.com/twosixlabs/armory/raw/master/docs/scenarios.md). # FAQs Please see the [frequently asked questions](//github.com/twosixlabs/armory/raw/master/docs/faqs.md) documentation for more information on: * Dataset format and preprocessing * Access to underlying models from wrapped classifiers. # Contributing Armory is an open source project and as such we welcome contributions! Please refer to our [contribution docs](//github.com/twosixlabs/armory/raw/master/.github/CONTRIBUTING.md) for how to get started. # Acknowledgment This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001120C0114. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency (DARPA).   [ci-badge]: https://github.com/twosixlabs/armory/workflows/GitHub%20CI/badge.svg [ci-url]: https://github.com/twosixlabs/armory/actions/ [pypi-badge]: https://badge.fury.io/py/armory-testbed.svg [pypi-url]: https://pypi.org/project/armory-testbed [python-badge]: https://img.shields.io/pypi/pyversions/armory-testbed [python-url]: https://pypi.org/project/armory-testbed [license-badge]: https://img.shields.io/badge/License-MIT-yellow.svg [license-url]: https://opensource.org/licenses/MIT [docs-badge]: https://readthedocs.org/projects/armory/badge/ [docs-url]: https://readthedocs.org/projects/armory/ [style-badge]: https://img.shields.io/badge/code%20style-black-000000.svg [style-url]: https://github.com/ambv/black

نیازمندی

مقدار	نام
-	docker
-	jsonschema
-	loguru
-	pytest
-	pyyaml
-	requests
-	setuptools-scm
-	toml
xtr	armory-testbed[datasets];
xtr	armory-testbed[developer];
xtr	armory-testbed[engine];
xtr	armory-testbed[jupyter];
xtr	armory-testbed[math];
xtr	armory-testbed[pytorch];
xtr	armory-testbed[tensorflow];
xtr	armory-testbed[tensorflow];
xtr	protobuf;
xtr	tensorflow-datasets>=4.6.0;
xtr	apache-beam>=2.22.0;
xtr	armory-testbed[datasets,engine];
xtr	armory-testbed[datasets,engine,math,pytorch];
xtr	google-cloud-storage;
xtr	librosa;
xtr	python-levenshtein;
xtr	pytorch-lightning<1.5.0;
xtr	sox;
xtr	torchmetrics<0.8.0;
xtr	transformers;
xtr	bandit[toml];
xtr	black[jupyter]==22.*;
xtr	flake8;
xtr	hatch;
xtr	hydra-core;
xtr	isort;
xtr	mkdocs;
xtr	wheel;
xtr	adversarial-robustness-toolbox==1.13.0;
xtr	boto3;
xtr	botocore;
xtr	ffmpeg-python;
xtr	opencv-python-headless==4.5.5.62;
xtr	opencv-python==4.5.5.62;
xtr	pillow;
xtr	pydub;
xtr	tensorboardx;
xtr	tidecv;
xtr	ipywidgets;
xtr	jupyterlab;
xtr	jupyterlab-widgets;
xtr	matplotlib;
xtr	numpy;
xtr	pandas;
xtr	scikit-learn<1.1.0;
xtr	scipy>=1.4.1;
xtr	armory-testbed[datasets,engine,math];
xtr	torch;
xtr	torchaudio;
xtr	torchvision;
xtr	armory-testbed[datasets,engine,math];
xtr	tensorflow>=2.10.0;
xtr	tf-models-official;

زبان مورد نیاز

مقدار	نام
>=3.8	Python

نحوه نصب

نصب پکیج whl armory-testbed-0.9.1:

pip install armory-testbed-0.9.1.whl

نصب پکیج tar.gz armory-testbed-0.9.1:

pip install armory-testbed-0.9.1.tar.gz