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IREE
MLIR-based ML compiler for cross-platform model deployment
Apache-2.0
ABOUT
Deploying ML models across diverse hardware — from mobile CPUs to desktop GPUs to AI accelerators — requires platform-specific tuning and separate runtime backends. IREE solves this with an MLIR-based compiler that takes models from TensorFlow, PyTorch, and JAX and generates optimized code for any target. Its HAL (Hardware Abstraction Layer) runtime dispatches to Vulkan, CUDA, Metal, ROCm, and CPU backends with near-native performance. Models are compiled once and deployed across platforms without hardware-specific retuning, and the runtime is lightweight enough for embedded use cases.
INSTALL
pip install iree-base-compiler iree-base-runtimeINTEGRATION GUIDE
1. Compile a PyTorch model once and deploy it on NVIDIA GPU, AMD GPU, and CPU from the same binary
2. Run a TensorFlow model on a Vulkan-capable mobile device without platform-specific code
3. Build a cross-platform AI application that uses Metal on macOS, Vulkan on Android, and CUDA on Linux
4. Optimize a JAX model for low-latency inference on edge hardware using MLIR-based fusion passes
5. Deploy compiled model binaries to embedded devices with the minimal IREE runtime footprint
TAGS
compilermliroptimizationinferencecross-platformgputensorflowpytorchjax