CompilerGym
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About
CompilerGym is a library providing easy-to-use reinforcement learning environments for compiler tasks. It allows machine learning researchers to tackle important optimization problems in compilers without needing extensive compiler knowledge. Its main goal is to serve as a catalyst for enhancing compiler efficiency using machine learning. CompilerGym features various environments including LLVM and GCC, and supports both Python and C++ APIs for development. The library is designed to simplify experimentation for AI researchers and contributes to improving compiler performance by exposing optimization processes.
Platform
Features
• allows control over compiler decision processes
• facilitates ai research in compiler optimization
• easy-to-use python and c++ apis
• supports llvm, gcc, and custom environments
• reinforcement learning environments for compilers
FAQs
What can I do with this?
CompilerGym lets you control the decisions that a compiler makes when optimizing a program. You can manage optimization pass selections, flag settings for compilers, and control optimization processes.
Do I have to use reinforcement learning?
No, while CompilerGym is structured around a gym interface popular in reinforcement learning, you can use any technique such as genetic algorithms or supervised learning.
Where does CompilerGym store files?
CompilerGym uses specific directories for caching datasets and storing user data. Check the documentation for environment variable settings to customize storage.
How do I debug crashes or errors?
First create a minimal reproducible example and check the logging output. You can also enable verbose logging for more detailed debug information.
I found a bug. How do I report it?
Please file an issue on the GitHub issue tracker.
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