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There are mysterious memory usage growth in Ray clusters that disappear when running with jemalloc. Before we are able to figure out the root cause, it seems using jemalloc by default can be a good walkaround. Because of its efficiency, using jemalloc by default can be beneficial, but we need to run more benchmarks to verify.
14 lines
470 B
YAML
14 lines
470 B
YAML
base_image: {{ env["RAY_IMAGE_ML_NIGHTLY_GPU"] | default("anyscale/ray-ml:nightly-py37-gpu") }}
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env_vars: {"LD_PRELOAD": "/usr/lib/x86_64-linux-gnu/libjemalloc.so"}
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debian_packages:
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- libjemalloc-dev
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python:
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pip_packages: []
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conda_packages: []
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post_build_cmds:
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- pip3 install tqdm || true
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- pip3 uninstall ray -y && pip3 install -U {{ env["RAY_WHEELS"] | default("ray") }}
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- {{ env["RAY_WHEELS_SANITY_CHECK"] | default("echo No Ray wheels sanity check") }}
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