10 examples of 'python get number of cores' in Python

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def get_cpu_cores(hyperthreads=False):
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    """Returns the number of cores on the node.
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    If hyperthreads is true, this is the logical cpu cores, else
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    the physical cores are returned.
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    Note: This returns cores available on the current node - will
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    not work for systems of multiple node types
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    """
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    try:
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        import psutil
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        ranks_per_node = psutil.cpu_count(logical=hyperthreads)
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    except ImportError:
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        # logger
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        if hyperthreads:
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            import multiprocessing
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            ranks_per_node = multiprocessing.cpu_count()
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        else:
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            try:
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                ranks_per_node = _cpu_count_physical()
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            except Exception as e:
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                logger.warning("Could not detect physical cores - Logical cores (with hyperthreads) returned - specify ranks_per_node to override. Exception {}".format(e))
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                import multiprocessing
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                ranks_per_node = multiprocessing.cpu_count()
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    return ranks_per_node  # This is ranks available per node

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def get_cores_available():
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    if not is_numa_capable():
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        cpu_list = range(multiprocessing.cpu_count())
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        cpus = {0: CPUNode(cpu_list)}
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    else:
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        cpus = get_numa_nodes()
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    return cpus

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def cpu_count():
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    '''
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    Returns the number of CPUs in the system
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    '''
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    if sys.platform == 'win32' or (sys.platform == 'cli' and os.name == 'nt'):
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        try:
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            num = int(os.environ['NUMBER_OF_PROCESSORS'])
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        except (ValueError, KeyError):
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            num = 0
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    elif 'bsd' in sys.platform or sys.platform == 'darwin':
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        comm = '/sbin/sysctl -n hw.ncpu'
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        if sys.platform == 'darwin':
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            comm = '/usr' + comm
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        try:
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            with os.popen(comm) as p:
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                num = int(p.read())
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        except ValueError:
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            num = 0
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    else:
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        try:
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            num = os.sysconf('SC_NPROCESSORS_ONLN')
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        except (ValueError, OSError, AttributeError):
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            num = 0
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    if num >= 1:
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        return num
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    else:
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        raise NotImplementedError('cannot determine number of cpus')

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def _get_processes(procs):
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    """Choose the best number of processes."""
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    cpus = cpu_count()
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    if cpus == 1:
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        return cpus
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    else:
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        if not 0 < procs < cpus:
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            return cpus - 1
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        else:
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            return procs

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def get_n_gpu():
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    detected_n_gpu = mp.RawValue('i', 0)
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    p = mp.Process(target=n_gpu_subprocess, args=(detected_n_gpu,))
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    p.start()
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    p.join()
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    n_gpu = int(detected_n_gpu.value)
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    if n_gpu == -1:
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        raise ImportError("Must be able to import pygpu to use GPUs.")
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    return n_gpu

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def cpu_count():
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    return rffi.cast(lltype.Signed, _cpu_count())

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def get_num_cpus():
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    """ Returns the number of CPUs on the emulated system
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        :return: The number of CPUs on the emulated system
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        :rtype: int
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    """
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    import c_api
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    # If this function call fails, it will raise an exception.
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    # Given that the exception is self explanatory, we just let it propagate
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    # upwards
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    return c_api.get_num_cpus()

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def coresReserved(self):
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    """The number of cores reserved for this frame.
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    :rtype:  float
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    :return: Cores reserved for the running frame"""
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    return self.data.reserved_cores

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@property
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def cores_per_node(self) -> Optional[int]:
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    """Number of cores to provision per node.
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    Providers which set this property should ask for cores_per_node cores
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    when provisioning resources, and set the corresponding environment
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    variable PARSL_CORES before executing submitted commands.
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    If this property is set, executors may use it to calculate how many tasks can
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    run concurrently per node. This information is used by dataflow.Strategy to estimate
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    the resources required to run all outstanding tasks.
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    """
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    return self._cores_per_node

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@property
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def maximum_number_of_cores(self):
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    """
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    Get maximum number of cores for an individual job
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    Returns:
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        int: minimum number of cores
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    """
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    return self._maximum_number_of_cores