如何正确运行ray? [英] How to run ray correctly?
问题描述
试图了解如何正确使用ray进行编程.
Trying to understand how to correctly program with ray.
以下结果似乎与ray的性能改进不一致，如此处.
The results below do not seem to agree with the performance improvement of ray as explained here.
环境:
- Python版本:3.6.10
- ray版本:0.7.4
这是机器规格:
>>> import psutil
>>> psutil.cpu_count(logical=False)
4
>>> psutil.cpu_count(logical=True)
8
>>> mem = psutil.virtual_memory()
>>> mem.total
33707012096 # 32 GB
首先，使用Queue(multiproc_function.py)进行传统的python多重处理:
First, the traditional python multiprocessing with Queue (multiproc_function.py):
import time
from multiprocessing import Process, Queue
N_PARALLEL = 8
N_LIST_ITEMS = int(1e8)
def loop(n, nums, q):
print(f"n = {n}")
s = 0
start = time.perf_counter()
for e in nums:
s += e
t_taken = round(time.perf_counter() - start, 2)
q.put((n, s, t_taken))
if __name__ == '__main__':
results = []
nums = list(range(N_LIST_ITEMS))
q = Queue()
procs = []
for i in range(N_PARALLEL):
procs.append(Process(target=loop, args=(i, nums, q)))
for proc in procs:
proc.start()
for proc in procs:
n, s, t_taken = q.get()
results.append((n, s, t_taken))
for proc in procs:
proc.join()
for r in results:
print(r)
结果是:
$ time python multiproc_function.py
n = 0
n = 1
n = 2
n = 3
n = 4
n = 5
n = 6
n = 7
(0, 4999999950000000, 11.12)
(1, 4999999950000000, 11.14)
(2, 4999999950000000, 11.1)
(3, 4999999950000000, 11.23)
(4, 4999999950000000, 11.2)
(6, 4999999950000000, 11.22)
(7, 4999999950000000, 11.24)
(5, 4999999950000000, 11.54)
real 0m19.156s
user 1m13.614s
sys 0m24.496s
在运行期间检查htop时，内存从2.6 GB的基本消耗增加到8 GB，并且所有8个处理器都被完全消耗.另外，从user+sys> real可以清楚地看到并行处理正在发生.
When inspecting htop during the run, the memory went from 2.6 GB base consumption to 8 GB, and had all the 8 processors fully consumed. Also, it is clear from user+sys > real that parallel processing is happening.
这是射线测试代码(ray_test.py):
Here is the ray test code (ray_test.py):
import time
import psutil
import ray
N_PARALLEL = 8
N_LIST_ITEMS = int(1e8)
use_logical_cores = False
num_cpus = psutil.cpu_count(logical=use_logical_cores)
if use_logical_cores:
print(f"Setting num_cpus to # logical cores = {num_cpus}")
else:
print(f"Setting num_cpus to # physical cores = {num_cpus}")
ray.init(num_cpus=num_cpus)
@ray.remote
def loop(nums, n):
print(f"n = {n}")
s = 0
start = time.perf_counter()
for e in nums:
s += e
t_taken = round(time.perf_counter() - start, 2)
return (n, s, t_taken)
if __name__ == '__main__':
nums = list(range(N_LIST_ITEMS))
list_id = ray.put(nums)
results = ray.get([loop.remote(list_id, i) for i in range(N_PARALLEL)])
for r in results:
print(r)
结果是:
$ time python ray_test.py
Setting num_cpus to # physical cores = 4
2020-04-28 16:52:51,419 INFO resource_spec.py:205 -- Starting Ray with 18.16 GiB memory available for workers and up to 9.11 GiB for objects. You can adjust these settings with ray.remote(memory=<bytes>, object_store_memory=<bytes>).
(pid=78483) n = 2
(pid=78485) n = 1
(pid=78484) n = 3
(pid=78486) n = 0
(pid=78484) n = 4
(pid=78483) n = 5
(pid=78485) n = 6
(pid=78486) n = 7
(0, 4999999950000000, 5.12)
(1, 4999999950000000, 5.02)
(2, 4999999950000000, 4.8)
(3, 4999999950000000, 4.43)
(4, 4999999950000000, 4.64)
(5, 4999999950000000, 4.61)
(6, 4999999950000000, 4.84)
(7, 4999999950000000, 4.99)
real 0m45.082s
user 0m22.163s
sys 0m10.213s
real时间比python多处理时间长得多.此外，real大于user+sys.检查htop时，内存高达30 GB，并且内核也没有完全饱和.所有这些似乎与ray应该做什么相反.
The real time is much longer than that of python multiprocessing. Also, real is greater than user+sys. When inspecting htop, the memory went up to 30 GB and the cores were also not fully saturated. All these seem to contradict what ray is supposed to do.
然后将use_logical_cores设置为True.由于内存不足，运行被终止:
Then I set use_logical_cores to True. The run gets killed due to out of memory:
$ time python ray_test.py
Setting num_cpus to # logical cores = 8
2020-04-28 16:27:43,709 INFO resource_spec.py:205 -- Starting Ray with 17.29 GiB memory available for workers and up to 8.65 GiB for objects. You can adjust these settings with ray.remote(memory=<bytes>, object_store_memory=<bytes>).
Killed
real 0m25.205s
user 0m15.056s
sys 0m4.028s
我在这里做错什么了吗?
Am I doing something wrong here?
推荐答案
首先，Ray不保证CPU亲和力或资源隔离.这可能是其CPU使用率不饱和的原因. (不过，我不确定100％).您可以尝试使用psutil设置cpu亲和力，然后查看核心是否仍未饱和. ( https://psutil.readthedocs.io/en/latest/#psutil .Process.cpu_affinity ).
Firstly, Ray doesn't guarantee the CPU affinity or resource isolation. That could be the reason why it have non-saturated CPU usage. (I am not 100% sure though). You can try setting cpu affinity using psutil and see if the cores are still not saturated. (https://psutil.readthedocs.io/en/latest/#psutil.Process.cpu_affinity).
关于结果，您介意尝试使用最新版本的Ray吗?在性能和性能方面取得了相当不错的进步从0.7.4版本开始的Ray中的内存管理.
About the result, would you mind trying the newest version of Ray? There was pretty good progress in performance & memory management in Ray from the version 0.7.4.
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