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BAZEL
Fast, correct builds for monorepos and ML frameworks
Apache-2.0
ABOUT
Large ML codebases spanning Python, C++, CUDA, and protobuf definitions need a build system that can handle cross-language dependencies, GPU-specific compilation, and massive monorepos without full rebuilds. Bazel provides hermetic, reproducible builds with automatic dependency tracking, remote caching, and distributed execution. TensorFlow, PyTorch, and JAX all use Bazel to manage their complex build graphs spanning CPU/GPU/TPU backends. Without it, ML teams resort to fragile Makefiles or lose days to incremental build breaks in multi-language projects.
INSTALL
npm install -g @bazel/bazeliskINTEGRATION GUIDE
1. Build TensorFlow, PyTorch, or JAX from source with custom hardware backends
2. Manage cross-language monorepos with Python, C++, CUDA, and protocol buffers
3. Leverage remote build caching to accelerate CI pipelines for ML framework development
4. Create hermetic, reproducible builds for ML model serving binaries
TAGS
build-systemcompilationmonorepotensorflowpytorchautomation