CompilerGym is a toolkit for exposing compiler optimization problems for reinforcement learning. It allows machine learning researchers to experiment with program optimization techniques without requiring any experience in compilers, and provides a framework for compiler developers to expose new optimization problems for AI.
Starting with CompilerGym is simple. If you not already familiar with the gym interface, refer to the getting started guide for an overview of the key concepts.
Install the latest CompilerGym release using:
$ pip install compiler_gym
The binary works on macOS and Linux (on Ubuntu 18.04, Fedora 28, Debian 10 or newer equivalents).
If you prefer, you may build from source. This requires a modern C++ toolchain. On macOS you can use the system compiler. On linux, install the required toolchain using:
$ sudo apt install clang libtinfo5 patchelf
$ export CC=clang
$ export CXX=clang++
We recommend using conda to manage the remaining build dependencies. First create a conda environment with the required dependencies:
$ conda create -n compiler_gym python=3.8 bazel=3.1.0 cmake pandoc
$ conda activate compiler_gym
Then clone the CompilerGym source code using:
$ git clone https://github.com/facebookresearch/CompilerGym.git
$ cd CompilerGym
Install the python development dependencies using:
$ make init
Then run the test suite to confirm that everything is working:
$ make test
To build and install the python package, run:
$ make install
When you are finished, you can deactivate and delete the conda environment using:
$ conda deactivate
$ conda env remove -n compiler_gym
In Python, import compiler_gym to use the environments:
>>> import gym
>>> import compiler_gym # imports the CompilerGym environments
>>> env = gym.make("llvm-autophase-ic-v0") # starts a new environment
>>> env.require_dataset("npb-v0") # downloads a set of programs
>>> env.reset() # starts a new compilation session with a random program
>>> env.render() # prints the IR of the program
>>> env.step(env.action_space.sample()) # applies a random optimization, updates state/reward/actionsSee the documentation website for tutorials, further details, and API reference.
We welcome contributions to CompilerGym. If you are interested in contributing please see this document.
If you use CompilerGym in any of your work, please cite:
@Misc{CompilerGym,
author = {Cummins, Chris and Leather, Hugh and Steiner, Benoit and He, Horace and Chintala, Soumith},
title = {{CompilerGym}: A Reinforcement Learning Toolkit for Compilers},
howpublished = {\url{https://github.com/facebookresearch/CompilerGym/}},
year = {2020}
}