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        "author": "Roman Lutz",
        "author_email": "rolutz@microsoft.com",
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        "description": "[![Build Status](https://img.shields.io/azure-devops/build/responsibleai/tempeh/19/master?failed_label=bad&passed_label=good&label=GatedCheckin%3ADev)](https://dev.azure.com/responsibleai/tempeh/_build/latest?definitionId=19&branchName=master) ![MIT license](https://img.shields.io/badge/License-MIT-blue.svg) ![pypi badge](https://img.shields.io/pypi/v/tempeh?color=blue)\n\n\n# tempeh\n\ntempeh is a framework to\n\n**TE**st\n\n**M**achine learning\n\n**PE**rformance\n\nex**H**austively\n\nwhich includes tracking memory usage and run time. This is particularly useful as a pluggable tool for your repository's performance tests. Typically, people want to run them periodically over various datasets and/or with a number of models to catch regressions with respect to run time or memory consumption. This should be as easy as\n\n```python\nimport pytest\nfrom time import time\nfrom tempeh.configurations import datasets, models\n\n@pytest.mark.parametrize('Dataset', datasets.values())\n@pytest.mark.parametrize('Model', models.values())\ndef test_fit_predict_regression(Dataset, Model):\n    dataset = Dataset()\n    X_train, X_test = dataset.get_X()\n    y_train, y_test = dataset.get_y()\n    model = Model()\n    max_execution_time = get_max_execution_time(dataset, model)\n    if model.compatible_with_dataset(dataset):\n        start_time = time()\n        model.fit(X_train, y_train)\n        model.predict(X_test)\n        duration = time() - start_time\n\n        assert duration < max_execution_time\n```\n\n## Installation\n\ntempeh depends on various packages to provide models, including `tensorflow`, `torch`, `xgboost`, `lightgbm`. To install a release version of `tempeh` just run\n\n```python\npip install tempeh\n```\n\n<details>\n<summary>\n<strong>\n<em>\nCommon issues\n</em>\n</strong>\n</summary>\n\n- If you're using a 32-bit Python version you might need to switch to a 64-bit Python version first to successfully install tensorflow.\n- If the installation of `torch` fails try using the recommendation from the [pytorch website](https://pytorch.org/get-started/locally/) for stable versions without CUDA for your python version on your operating system.\n- If the installation of `lightgbm` or `xgboost` fails try to use a pip version less than 20.0 until their bug is resolved.\n</details>\n\n## Structure\n\n### Datasets\n\nDatasets (located in the `datasets/` directory) encapsulate different datasets used for testing.\n\n#### To add a new one\n\n+ Create a python file in the `datasets/` directory with naming convention `[name]_datasets.py`\n+ Subclass `BasePerformanceDatasetWrapper`. The naming convention is `[dataset_name]PerformanceDatasetWrapper`\n+ In `__init__` load the dataset and call `super().__init__(data, targets, size)`\n+ Add the class to `__init__.py`\n+ Make sure the class contains class variables `task`, `data_type`, `size`\n+ Add an entry to the `datasets` dictionary in `configurations.py`.\n\n### Models\n\nModels (`models/` directory) wrap different machine learning models.\n\n#### To add a new one\n\n+ Create a python file in the `models/` directory with naming convention `[name]_model.py`\n+ Subclass `BaseModelWrapper` and name the class `[name]ModelWrapper`\n+ In `__init__` train the model; we expect format `__init__(self, ...)`\n+ Models must contain `tasks` and `algorithm`\n+ Add an entry to the `models` dictionary in `configurations.py`.\n\n\n## Maintainers\n\nIn alphabetical order:\n\n- [Eduardo de Leon](https://github.com/eedeleon)\n- [Ilya Matiach](https://github.com/imatiach-msft)\n- [Roman Lutz](https://github.com/romanlutz)\n\n\n# Contributing\n\nTo contribute please check our [Contributing Guide](CONTRIBUTING.md).\n\n# Issues\n\n## Regular (non-Security) Issues\nPlease submit a report through [Github issues](https://github.com/microsoft/tempeh/issues). A maintainer will respond within a reasonable period of time to handle the issue as follows:\n- bug: triage as `bug` and provide estimated timeline based on severity\n- feature request: triage as `feature request` and provide estimated timeline\n- question or discussion: triage as `question` and respond or notify/identify a suitable expert to respond\n\nMaintainers are supposed to link duplicate issues when possible.\n\n\n## Reporting Security Issues\n\nPlease take a look at our guidelines for reporting [security issues](SECURITY.md).\n\n\n",
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