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        "author": "Shun Iwase, Linsho Kaku",
        "author_email": "s@sh8.io, kaku.l.aa@rio.gsic.titech.ac.jp",
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            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Intended Audience :: Developers",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6"
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        "description": "# Polaris : Hyperparamter optimization library.\n\n[![Documentation Status](https://readthedocs.org/projects/polaris/badge/?version=latest)](https://polaris.readthedocs.io/en/latest/?badge=latest)\n\nPolaris is a hyperparamter optimization library.  \nWe plan to support every state-of-art tuning method as follows.\n\n-  Random Search\n-  Bayesian Optimization\n-  Tree-structured Parzen Estimators (TPE)\n\n## Documentation\nPolaris' documentation can be found at [https://polaris.readthedocs.io/](https://polaris.readthedocs.io/)\n\n## Prerequisites\n- Python >= 3.6\n- RabbitMQ (Only for parallel execution)\n\n## Installation\n\n```shell\n$ pip install polaris-py\n```\n\n## Examples\n\n### Sequential Execution\n\n```python\nfrom polaris import Polaris, STATUS_SUCCESS, Trials, Bounds\n\n\ndef pseudo_train(params, exp_info):\n    lr_squared = (params['lr'] - 0.006) ** 2\n    weight_decay_squared = (params['weight_decay'] - 0.02) ** 2\n    loss = lr_squared + weight_decay_squared\n    return {\n        'loss':  loss,\n        'status': STATUS_SUCCESS,\n    }\n\n\nif __name__ == '__main__':\n    bounds = [\n        Bounds('lr', 0.001, 0.01),\n        Bounds('weight_decay', 0.0002, 0.04),\n    ]\n    trials = Trials()\n    polaris = Polaris(\n            pseudo_train, bounds, 'bayesian_opt',\n            trials, max_evals=100, exp_key='this_is_test')\n    best_params = polaris.run()\n    print(best_params)\n```\n\n### Parallel Execution\n\n#### Single Process\n\n1. Run `rabbitmq-server`\n1. Set `RABBITMQ_URL` (ampq://guest:guest@localhost//)\n1. Run `polaris-worker --exp-key this_is_test`\n1. Run codes as follows\n\n### Multiple Processes (Use MPI)\n\n1. Run `rabbitmq-server`\n1. Set `RABBITMQ_URL` (ampq://guest:guest@localhost//)\n1. Run `mpirun -n 4 polaris-worker --mpi --exp-key this_is_test`\n1. Run codes as follows\n\n\n```python\n\n...\n\nif __name__ == '__main__':\n    bounds = [\n        Bounds('lr', 0.001, 0.01),\n        Bounds('weight_decay', 0.0002, 0.04),\n    ]\n    trials = Trials()\n    polaris = Polaris(\n            pseudo_train, bounds, 'bayesian_opt',\n            trials, max_evals=100, exp_key='this_is_test')\n    best_params = polaris.run_parallel()\n    print(best_params)\n```\n\n\n",
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