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Use ccache in CI
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@@ -212,22 +212,42 @@ jobs:
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resource_class: gpu.nvidia.small.gen2
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steps:
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- checkout
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- restore_cache:
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keys:
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- mlx-v1-cuda-{{ arch }}-
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- run:
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name: Install Python package
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name: Install dependencies
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command: |
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sudo apt-get update
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sudo apt-get install libcudnn9-dev-cuda-12
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sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
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curl -sL https://github.com/ccache/ccache/releases/download/v4.11.3/ccache-4.11.3-linux-x86_64.tar.xz | tar xJf -
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sudo mv ccache-4.11.3-linux-x86_64/ccache /usr/bin/ccache
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rm -rf ccache-4.11.3-linux-x86_64
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- run:
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name: Install Python package
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command: |
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python3 -m venv env
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source env/bin/activate
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CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_CUDA_COMPILER=`which nvcc`" \
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pip install -e ".[dev]"
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pip install -e ".[dev]" -v
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- run:
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name: Run Python tests
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command: |
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source env/bin/activate
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LOW_MEMORY=1 DEVICE=cpu python -m unittest discover python/tests -v
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LOW_MEMORY=1 DEVICE=gpu python -m tests discover python/tests -v
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- run:
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name: CCache report
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command: |
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ccache --show-stats
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ccache --zero-stats
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ccache --max-size 400MB
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ccache --cleanup
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- save_cache:
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key: mlx-v1-cuda-{{ arch }}-{{ epoch }}
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paths:
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- /home/circleci/.cache/ccache
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build_release:
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parameters:
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3
setup.py
3
setup.py
@@ -113,6 +113,9 @@ class CMakeBuild(build_ext):
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if "CMAKE_BUILD_PARALLEL_LEVEL" not in os.environ:
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build_args += [f"-j{os.cpu_count()}"]
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# Avoid cache miss when building from temporary dirs.
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os.environ["CCACHE_BASEDIR"] = os.path.abspath(self.build_temp)
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subprocess.run(
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["cmake", ext.sourcedir, *cmake_args], cwd=build_temp, check=True
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)
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