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        "description": "<img src=\"https://dragonfly.github.io/images/dragonfly_bigwords.png\"/>\n\n---\n\n\nDragonfly is an open source python library for scalable Bayesian optimisation.\n\nBayesian optimisation is used for optimising black-box functions whose evaluations are\nusually expensive. Beyond vanilla optimisation techniques, Dragonfly provides an array of tools to\nscale up Bayesian optimisation to expensive large scale problems.\nThese include features/functionality that are especially suited for\nhigh dimensional optimisation (optimising for a large number of variables),\nparallel evaluations in synchronous or asynchronous settings (conducting multiple\nevaluations in parallel), multi-fidelity optimisation (using cheap approximations\nto speed up the optimisation process), and multi-objective optimisation (optimising\nmultiple functions simultaneously).\n\nDragonfly is compatible with Python2 (>= 2.7) and Python3 (>= 3.5) and has been tested\non Linux, macOS, and Windows platforms.\nFor documentation, installation, and a getting started guide, see our\n[readthedocs page](https://dragonfly-opt.readthedocs.io). For more details, see\nour [paper](https://arxiv.org/abs/1903.06694).\n\n&nbsp;\n\n## Installation\n\nSee \n[here](https://dragonfly-opt.readthedocs.io/en/master/install/)\nfor detailed instructions on installing Dragonfly and its dependencies.\n\n**Quick Installation:**\nIf you have done this kind of thing before, you should be able to install\nDragonfly via `pip`.\n\n```bash\n$ sudo apt-get install python-dev python3-dev gfortran # On Ubuntu/Debian\n$ pip install numpy\n$ pip install dragonfly-opt -v\n```\n\n\n**Testing the Installation**:\nYou can import Dragonfly in python to test if it was installed properly.\nIf you have installed via source, make sure that you move to a different directory \n to avoid naming conflicts.\n```bash\n$ python\n>>> from dragonfly import minimise_function\n>>> # The first argument below is the function, the second is the domain, and the third is the budget.\n>>> min_val, min_pt, history = minimise_function(lambda x: x ** 4 - x**2 + 0.1 * x, [[-10, 10]], 10);  \n...\n>>> min_val, min_pt\n(-0.32122746026750953, array([-0.7129672]))\n```\nDue to stochasticity in the algorithms, the above values for `min_val`, `min_pt` may be\ndifferent. If you run it for longer (e.g.\n`min_val, min_pt, history = minimise_function(lambda x: x ** 4 - x**2 + 0.1 * x, [[-10, 10]], 100)`),\nyou should get more consistent values for the minimum. \n\n\nIf the installation fails or if there are warning messages, see detailed instructions\n[here](https://dragonfly-opt.readthedocs.io/en/master/install/).\n\n\n&nbsp;\n\n## Quick Start\n\nDragonfly can be\nused directly in the command line by calling\n[`dragonfly-script.py`](bin/dragonfly-script.py)\nor be imported in python code via the `maximise_function` function in the main library.\nTo help get started, we have provided some examples in the\n[`examples`](examples) directory.\nSee our readthedocs getting started pages\n([command line](https://dragonfly-opt.readthedocs.io/en/master/getting_started_cli/),\n[Python](https://dragonfly-opt.readthedocs.io/en/master/getting_started_py/))\nfor examples and use cases.\n\n**Command line**:\nBelow is an example usage in the command line.\n```bash\n$ cd examples\n$ dragonfly-script.py --config synthetic/branin/config.json --options options_files/options_example.txt\n```\n\n**In Python code**:\nThe main APIs for Dragonfly are defined in\n[`dragonfly/apis`](dragonfly/apis).\nFor their definitions and arguments, see\n[`dragonfly/apis/opt.py`](dragonfly/apis/opt.py) and\n[`dragonfly/apis/moo.py`](dragonfly/apis/moo.py).\nYou can import the main API in python code via,\n```python\nfrom dragonfly import minimise_function, maximise_function\nfunc = lambda x: x ** 4 - x**2 + 0.1 * x\ndomain = [[-10, 10]]\nmax_capital = 100\nmin_val, min_pt, history = minimise_function(func, domain, max_capital)\nprint(min_val, min_pt)\nmax_val, max_pt, history = maximise_function(lambda x: -func(x), domain, max_capital)\nprint(max_val, max_pt)\n```\nHere, `func` is the function to be maximised,\n`domain` is the domain over which `func` is to be optimised,\nand `max_capital` is the capital available for optimisation.\nThe domain can be specified via a JSON file or in code.\nSee\n[here](examples/synthetic/branin/in_code_demo.py),\n[here](examples/synthetic/hartmann6_4/in_code_demo.py),\n[here](examples/synthetic/discrete_euc/in_code_demo_1.py),\n[here](examples/synthetic/discrete_euc/in_code_demo_2.py),\n[here](examples/synthetic/hartmann3_constrained/in_code_demo.py),\n[here](examples/synthetic/park1_constrained/in_code_demo.py),\n[here](examples/synthetic/borehole_constrained/in_code_demo.py),\n[here](examples/synthetic/multiobjective_branin_currinexp/in_code_demo.py),\n[here](examples/synthetic/multiobjective_hartmann/in_code_demo.py),\n[here](examples/tree_reg/in_code_demo.py),\nand\n[here](examples/nas/demo_nas.py)\nfor more detailed examples.\n\n\n\nFor a comprehensive list of uses cases, including multi-objective optimisation,\nmulti-fidelity optimisation, neural architecture search, and other optimisation\nmethods (besides Bayesian optimisation), see our readthe docs pages\n([command line](https://dragonfly-opt.readthedocs.io/en/master/getting_started_cli/),\n[Python](https://dragonfly-opt.readthedocs.io/en/master/getting_started_py/)).\n\n\n&nbsp;\n\n### Contributors\n\nKirthevasan Kandasamy: [github](https://github.com/kirthevasank),\n[webpage](http://www.cs.cmu.edu/~kkandasa/)  \nKarun Raju Vysyaraju: [github](https://github.com/karunraju),\n[linkedin](https://www.linkedin.com/in/karunrajuvysyaraju)  \nWillie Neiswanger: [github](https://github.com/willieneis),\n[webpage](http://www.cs.cmu.edu/~wdn/)  \nBiswajit Paria: [github](https://github.com/biswajitsc),\n[webpage](https://biswajitsc.github.io/)  \nChris Collins: [github](https://github.com/crcollins/),\n[webpage](https://www.crcollins.com/)\n\n\n### Acknowledgements\nResearch and development of the methods in this package were funded by\nDOE grant DESC0011114, NSF grant IIS1563887, the DARPA D3M program, and AFRL.\n\n\n### Citation\nIf you use any part of this code in your work, please cite\n[this manuscript](https://arxiv.org/pdf/1903.06694.pdf).\n\n```\n@article{kandasamy2019tuning,\n    title={{Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian\n            Optimisation with Dragonfly}},\n    author={Kandasamy, Kirthevasan and Vysyaraju, Karun Raju and Neiswanger,\n            Willie and Paria, Biswajit and Collins, Christopher R. and Schneider, Jeff and\n            Poczos, Barnabas and Xing, Eric P},\n    journal={arXiv preprint arXiv:1903.06694},\n    year={2019}\n  }\n```\n\n### License\nThis software is released under the MIT license. For more details, please refer\n[LICENSE.txt](https://github.com/dragonfly/dragonfly/blob/master/LICENSE.txt).\n\nFor questions, please email kandasamy@cs.cmu.edu.\n\n\"Copyright 2018-2019 Kirthevasan Kandasamy\"",
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