Question

我有一个具有以下规格的工作站：

Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
Address sizes:       46 bits physical, 48 bits virtual
CPU(s):              16
On-line CPU(s) list: 0-15
Thread(s) per core:  2
Core(s) per socket:  8
Socket(s):           1
NUMA node(s):        1
Vendor ID:           GenuineIntel
CPU family:          6
Model:               79
Model name:          Intel(R) Xeon(R) CPU E5-1660 v4 @ 3.20GHz
Stepping:            1
CPU MHz:             1200.049
CPU max MHz:         3800.0000
CPU min MHz:         1200.0000
BogoMIPS:            6400.08
Virtualization:      VT-x
L1d cache:           32K
L1i cache:           32K
L2 cache:            256K
L3 cache:            20480K
NUMA node0 CPU(s):   0-15
Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts flush_l1d

我已经实现了dask来分发一些计算，并且我以此方式设置了Client()：

if __name__ == '__main__':
    cluster = LocalCluster()
    client = Client(cluster, asyncronous=True, n_workers=8,
                    threads_per_worker=2)
    train()

当我用delayed调用我的dask.compute(*computations, scheduler='distributed')函数时，似乎毫无疑问，dask正在使用所有资源。仪表板看起来像：

现在，如果我继续将Client()更改为：

if __name__ == '__main__':
    cluster = LocalCluster()
    client = Client(cluster, asyncronous=True, n_workers=4,
                    threads_per_worker=2)
    train()

我希望使用我一半的资源，但是正如我在仪表板上看到的那样，情况并非如此。

为什么Client()仍在使用所有资源？我对此表示感谢。

Answer 1

在您尚未指定Client类的情况下，该类将为您创建一个群集。仅当不传递现有集群实例时，Thos关键字才有效。相反，您应该将它们放入对LocalCluster的呼叫中：

cluster = LocalCluster(n_workers=4, threads_per_worker=2)
client = Client(cluster, asynchronous=True)

或者您可以简单地跳过创建集群

client = Client(cluster, asynchronous=True, n_workers=4,
                threads_per_worker=2)

在dask中控制内核/线程数

1 个答案: