CompilerGym
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About
CompilerGym is a library of reinforcement learning environments focused on compiler optimization tasks. It enables machine learning researchers to engage with compiler optimization issues in a familiar language, bridging the gap between compiler development and machine learning research. By providing easy-to-use APIs and an open-source environment, it aims to foster advancements in compiler performance using AI techniques. The tool aims to simplify experimentation with compiler optimizations and improve their implementation across various programming contexts.
Platform
Features
• reinforcement learning environments for compiler tasks
• support for multiple compilers such as llvm and gcc
• experimentation toolkit for ai researchers
• integration with openai gym interface
• apis for python and c++
FAQs
What can I do with this?
CompilerGym lets you control compiler decisions during program optimization, steering it towards the best compiled program based on reward signals.
Do I have to use reinforcement learning?
No, you can utilize any technique such as genetic algorithms, supervised learning, or deep reinforcement learning.
Where does CompilerGym store files?
CompilerGym uses specific directories for caching datasets and storing additional files, which can be overridden with environment variables.
Do I need to call env.close()?
Yes, you must call env.close() to clean up resources after using an environment.
How do I debug crashes or errors?
Produce a minimal reproducible example and check CompilerGym’s logging output for debugging.
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