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43 行
1.4 KiB
43 行
1.4 KiB
from typing import Optional
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import os
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from mlagents_envs.logging_util import get_logger
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logger = get_logger(__name__)
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def get_num_threads_to_use() -> Optional[int]:
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"""
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Gets the number of threads to use. For most problems, 4 is all you
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need, but for smaller machines, we'd like to scale to less than that.
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By default, PyTorch uses 1/2 of the available cores.
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"""
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num_cpus = _get_num_available_cpus()
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print("NUM_CPU ", num_cpus)
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logger.info("\n\n\n\n\n NUMCPU " + str(num_cpus) + "\n\n\n\n\n")
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return max(min(num_cpus // 2, 4), 1) if num_cpus is not None else None
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def _get_num_available_cpus() -> Optional[int]:
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"""
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Returns number of CPUs using cgroups if possible. This accounts
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for Docker containers that are limited in cores.
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"""
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period = _read_in_integer_file("/sys/fs/cgroup/cpu/cpu.cfs_period_us")
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logger.info("period "+str(period))
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quota = _read_in_integer_file("/sys/fs/cgroup/cpu/cpu.cfs_quota_us")
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logger.info("quota " + str(quota))
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shares = _read_in_integer_file("/sys/fs/cgroup/cpu/cpu.shares")
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logger.info("shares "+str(shares))
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if period > 0 and quota > 0:
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return int(quota // period)
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else:
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return os.cpu_count()
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def _read_in_integer_file(filename: str) -> int:
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try:
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with open(filename) as f:
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return int(f.read().rstrip())
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except FileNotFoundError:
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return -1
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