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        "author": "Facebook AI Research",
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        "description": "[![CircleCI](https://circleci.com/gh/facebookresearch/nevergrad/tree/master.svg?style=svg)](https://circleci.com/gh/facebookresearch/nevergrad/tree/master)\n\n# Nevergrad - A gradient-free optimization platform\n\n`nevergrad` is a Python 3.6+ library. It can be installed with:\n\n```\npip install nevergrad\n```\n\nYou can also install the master branch instead of the latest release with:\n\n```\npip install git+https://github.com/facebookresearch/nevergrad@master#egg=nevergrad\n```\n\nAlternatively, you can clone the repository and run `pip install -e .` from inside the repository folder.\n\nBy default, this only installs requirements for the optimization and instrumentation subpackages. If you are also interested in the benchmarking part,\nyou should install with the `[benchmark]` flag (example: `pip install 'nevergrad[benchmark]'`), and if you also want the test tools, use\nthe `[all]` flag (example: `pip install -e '.[all]'`)\n\n\n## Goals and structure\n\nThe goals of this package are to provide:\n- **gradient/derivative-free optimization algorithms**, including algorithms able to handle noise.\n- **tools to instrument any code**, making it painless to optimize your parameters/hyperparameters, whether they are continuous, discrete or a mixture of continuous and discrete variables.\n- **functions** on which to test the optimization algorithms.\n- **benchmark routines** in order to compare algorithms easily.\n\nThe structure of the package follows its goal, you will therefore find subpackages:\n- `optimization`: implementing optimization algorithms\n- `instrumentation`: tooling to convert code into a well-defined function to optimize.\n- `functions`: implementing both simple and complex benchmark functions\n- `benchmark`: for running experiments comparing the algorithms on benchmark functions\n- `common`: a set of tools used throughout the package\n\n![Example of optimization](https://raw.githubusercontent.com/facebookresearch/nevergrad/master/TwoPointsDE.gif)\n\n*Convergence of a population of points to the minima with two-points DE.*\n\n\n## Documentation\n\nThe following README is very general, here are links to find more details on:\n- [how to perform optimization](https://github.com/facebookresearch/nevergrad/blob/master/docs/optimization.md) using `nevergrad`, including using parallelization and a few recommendation on which algorithm should be used depending on the settings\n- [how to instrument](https://github.com/facebookresearch/nevergrad/blob/master/docs/instrumentation.md) functions with any kind of parameters in order to convert them into a function defined on a continuous vectorial space where optimization can be performed. It also provides a tool to instantiate a script or non-python code in order into a Python function and be able to tune some of its parameters.\n- [how to benchmark](https://github.com/facebookresearch/nevergrad/blob/master/docs/benchmarking.md) all optimizers on various test functions.\n- [benchmark results](https://github.com/facebookresearch/nevergrad/blob/master/docs/benchmarks.md) of some standard optimizers an simple test cases.\n- examples of [optimization for machine learning](https://github.com/facebookresearch/nevergrad/blob/master/docs/machinelearning.md).\n- how to [contribute](https://github.com/facebookresearch/nevergrad/blob/master/.github/CONTRIBUTING.md) through issues and pull requests and how to setup your dev environment.\n- guidelines of how to contribute by [adding a new algorithm](https://github.com/facebookresearch/nevergrad/blob/master/docs/adding_an_algorithm.md).\n\n\n## Basic optimization example\n\n**All optimizers assume a centered and reduced prior at the beginning of the optimization (i.e. 0 mean and unitary standard deviation). They are however able to find solutions far from this initial prior.**\n\n\nOptimizing (minimizing!) a function using an optimizer (here `OnePlusOne`) can be easily run with:\n\n```python\nimport nevergrad as ng\n\ndef square(x):\n    return sum((x - .5)**2)\n\noptimizer = ng.optimizers.OnePlusOne(instrumentation=2, budget=100)\nrecommendation = optimizer.optimize(square)\nprint(recommendation)  # optimal args and kwargs\n>>> Candidate(args=(array([0.500, 0.499]),), kwargs={})\n```\n\n`recommendation` holds the optimal attributes `args` and `kwargs` found by the optimizer for the provided function.\nIn this example, the optimal value will be found in `recommendation.args[0]` and will be a `np.ndarray` of size 2.\n\n`instrumentation=n` is a shortcut to state that the function has only one variable, of dimension `n`,\nSee the [instrumentation tutorial](https://github.com/facebookresearch/nevergrad/blob/master/docs/instrumentation.md) for more complex instrumentations.\n\n\nYou can print the full list of optimizers with:\n```python\nimport nevergrad as ng\nprint(list(sorted(ng.optimizers.registry.keys())))\n```\n\nThe [optimization documentation](https://github.com/facebookresearch/nevergrad/blob/master/docs/optimization.md) contains more information on how to use several workers, take full control of the optimization through the `ask` and `tell` interface and some pieces of advice on how to choose the proper optimizer for your problem.\n\n## Citing\n\n```bibtex\n@misc{nevergrad,\n    author = {J. Rapin and O. Teytaud},\n    title = {{Nevergrad - A gradient-free optimization platform}},\n    year = {2018},\n    publisher = {GitHub},\n    journal = {GitHub repository},\n    howpublished = {\\url{https://GitHub.com/FacebookResearch/Nevergrad}},\n}\n```\n\n## License\n\n`nevergrad` is released under the MIT license. See [LICENSE](https://github.com/facebookresearch/nevergrad/blob/master/LICENSE) for additional details.\n\n\n",
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