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- <img width =" 300 " src =" ./botorch_logo_lockup.svg " alt =" BoTorch Logo " />
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+ <a href =" https://botorch.org " >
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+ <img width =" 300 " src =" ./botorch_logo_lockup.svg " alt =" BoTorch Logo " />
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+ </a >
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[ ![ Build Status] (
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https://travis-ci.com/facebookexternal/botorch.svg?token=esFvpzSw7sLSsfe1PAr1&branch=master
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)] ( https://travis-ci.com/facebookexternal/botorch )
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- BoTorch is a library for Bayesian Optimization in PyTorch.
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+ BoTorch is a library for Bayesian Optimization built on PyTorch.
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- It is currently an alpha version under active development - be warned!
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+ * BoTorch is currently in alpha and under active development - be warned* !
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- ## Installation
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+ ### Why BoTorch
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+ BoTorch
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+ * Provides a modular and easily extensible interface for composing Bayesian
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+ optimization primitives, including probabilistic models, acquisition functions,
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+ and optimizers.
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+ * Harnesses the power of PyTorch, including auto-differentiation, native support
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+ for highly parallelized modern hardware (e.g. GPUs) using device-agnostic code,
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+ and a dynamic computation graph.
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+ * Supports Monte Carlo-based acquisition functions via the
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+ [ reparameterization trick] ( https://arxiv.org/abs/1312.6114 ) , which makes it
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+ straightforward to implement new ideas without having to impose restrictive
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+ assumptions about the underlying model.
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+ * Enables seamless integration with deep and/or convolutional architectures in PyTorch.
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+ * Has first-class support for state-of-the art probabilistic models in
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+ [ GPyTorch] ( http://www.gpytorch.ai/ ) , including support for multi-task Gaussian
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+ Processes (GPs) deep kernel learning, deep GPs, and approximate inference.
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- ##### Setup Requirements (TODO: Remove once we can use torch Sobol)
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- The following are required to run the setup:
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+ ### Target Audience
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- - Python >= 3.6
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- - numpy
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- - cython
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+ The primary audience for hands-on use of BoTorch are researchers and
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+ sophisticated practitioners in Bayesian Optimization and AI.
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+
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+ We recommend using BoTorch as a low-level API for implementing new algorithms
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+ for [ Ax] ( https://github.com/facebook/Ax ) . Ax has been designed to be
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+ an easy-to-use platform for end-users, which at the same time is flexible enough
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+ for Bayesian Optimization researchers to plug into for handling of feature
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+ transformations, (meta-)data management, storage, etc.
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+ We recommend that end-users who are not actively doing research on Bayesian
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+ Optimization simply use Ax.
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- ##### Installation Requirements
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- - PyTorch >= 1.0.1
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- - gpytorch >= 0.3.0
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+ ## Installation
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+
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+ #### Installation Requirements
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+
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+ - Python >= 3.6
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+ - PyTorch nightly (** TODO:** peg to PyTorch 1.1 once released)
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+ - gpytorch >= 0.3.1 (** TODO:** peg to GPyTorch 0.3.2 once released)
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- scipy
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- ** Important:**
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- You will want to have you PyTorch build link against ** MKL** (the non-optimized
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- version of botorch can be up to an order of magnitude slower). Setting this up
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- manually can be tricky - to make sure this works please use the Anaconda
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- installation instructions on https://pytorch.org/ .
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+ ** Important note for MacOS users:**
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+ * You will want to make sure your PyTorch build is linked against MKL (the
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+ non-optimized version of BoTorch can be up to an order of magnitude slower in
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+ some settings). Setting this up manually on MacOS can be tricky - to ensure
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+ this works properly please follow the
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+ [ PyTorch installation instructions] ( https://pytorch.org/get-started/locally/ ) .
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+ * If you need CUDA on MacOS, you will need to build PyTorch from source. Please
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+ consult the PyTorch installation instructions above.
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- ### Install botorch
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+ #### Installing BoTorch
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- To run the botorch setup, you'll need cython ( ** TODO: ** Remove)
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+ The latest release of BoTorch is easily installed using either pip or conda:
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``` bash
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- pip install cython
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+ pip install botorch
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```
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- We recommend installing botorch using pip via ssh:
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+ ** TODO: Conda install**
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+
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+
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+ If you'd like to try our bleeding edge features (and don't mind running into an
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+ occasional bug here or there), you can install the latest master from GitHub
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+ (this will also require installing the current GPyTorch master)::
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+ ``` bash
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+ pip install git+https://github.com/cornellius-gp/gpytorch.git
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+ pip install git+https://github.com/facebookexternal/botorch.git
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+ ```
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+
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+
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+ #### Installing BoTorch from the private repo ** TODO: REMOVE**
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+
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+ BoTorch is easily installed using pip:
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``` bash
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pip install git+ssh://
[email protected] /facebookexternal/botorch.git
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```
@@ -63,14 +107,9 @@ To customize the installation, you can also run the following instead:
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- ## Installation using conda
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-
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- ** TODO: conda install is unsupported until the repo is public**
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-
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-
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## Contributing
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See the [ CONTRIBUTING] ( CONTRIBUTING.md ) file for how to help out.
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## License
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- botorch is MIT licensed, as found in the LICENSE file.
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+ BoTorch is MIT licensed, as found in the LICENSE file.
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