Google Cloud ML Engineのメモリを調べてみた

Even though the exact specifications of the machine types are subject to change at any time, you can compare them in terms of relative capability. The following table uses rough "t-shirt" sizing to describe the machine types.

ココらへんがいまいち曖昧なので調べてみた

調べ方

import os

mem_bytes = os.sysconf('SC_PAGE_SIZE') * os.sysconf('SC_PHYS_PAGES')
mem_gib = mem_bytes/(1024.**3)
print(str(mem_gib)+"GB")

# another
meminfo = dict((i.split()[0].rstrip(':'),int(i.split()[1])) for i in open('/proc/meminfo').readlines())
mem_gib = meminfo['MemTotal']/(1024.**2)
print(str(mem_gib)+"GB")

Results

Scale tier

BASIC

single
- 14.6924285889 GB

STANDARD_1

single
- 7.01322555542 GB
master
- 7.01322555542 GB
ps
- 14.6924285889 GB
worker
- 7.01322555542 GB

PREMIUM_1

single
- 14.0992164612 GB
master
- 14.0992164612 GB
ps
- 51.1138572693 GB
worker
- 14.0992164612 GB

BASIC_GPU

single
29.4575538635 GB

CUSTOM

Machine type

standard

14.6924285889 GB

large_model

51.1138572693 GB

complex_model_s

7.01327514648 GB

complex_model_m

14.0991668701 GB

complex_model_l

28.272064209 GB

standard_gpu

29.4575538635 GB

complex_model_m_gpu

118.048339844 GB

complex_model_l_gpu

118.048339844 GB

コードはこちら