facebookresearch/CompilerGym

Health v2 · maintenance only

Flags: archived

Adoption not part of the score

1021 stars · 136 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

CompilerGym is a Python library of OpenAI Gym-compatible reinforcement learning environments for compiler and program optimization tasks, covering LLVM, GCC, MLIR, and loop_tool. It bundles benchmark datasets, observation and reward spaces, validation, and leaderboards to lower the barrier to applying ML to compiler research.

Use cases

• train reinforcement learning agents to optimize compiler passes
• tune GCC optimization flags with machine learning
• benchmark ML models on compiler optimization tasks
• research autotuning of LLVM IR optimization sequences
• expose custom compiler optimization problems as gym environments
• compare compiler optimization techniques reproducibly

When to choose

• you want to apply RL or search to compiler optimization without writing C++
• you need reproducible compiler benchmarks with datasets and reward signals
• you want a gym-style API for sequential decision-making over compiler choices

When to avoid

• you need a production compiler or general-purpose build tool
• you only need standard code compilation without ML-driven optimization
• you need actively developed cutting-edge features - development has slowed

Facets

library · maturity maintenance

reinforcement-learning compiler benchmarking machine-learning simulation machine-learning compilers performance artificial-intelligence python cli gym-environments compiler-optimization llvm gcc mlir autotuning program-optimization openai-gym research linux macos docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="facebookresearch/CompilerGym")