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