Colaboratory是Google的一个免费的深度学习云端环境，提供免费的GPU和TPU，除了需要科学上网，用起来还是非常舒服的。
Colab地址：点击前往
进入Google云端硬盘，右键→ \rightarrow → → \rightarrow →
Colab的用法和Jupyter notebook一样，可以直接执行Python代码，也可以通过!+command执行linux命令。
执行Python代码：
import numpy as np
a = np. random. random( ( 3 , 3 ) )
print ( a)
print ( "sum: " , np. sum ( a) )
[ [ 0.24678976 0.47023166 0.00604048 ]
[ 0.81917307 0.9650736 0.9974302 ]
[ 0.88696709 0.92203368 0.31132943 ] ]
sum : 5.625068960056541
使用命令查看Ubuntu的版本：
!lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 18.04.2 LTS
Release: 18.04
Codename: bionic
执行下面代码，点击输出链接，登陆Google账号，获取授权码，粘贴到输入框中，链接到云端硬盘，硬盘挂载在/content/drive下。
from google. colab import drive
drive. mount( '/content/drive' )
使用ls命令查看云端硬盘中的文件：
!ls "/content/drive/My Drive"
Cifar10
'Colab Notebooks'
CornerNet
deep-learning-keras-tensorflow
keras_Realtime_Multi-Person_Pose_Estimation-master
MCM
pose-residual-network-pytorch
也可以展开侧边栏查看文件：
点击 代码执行程序→ \rightarrow → → \rightarrow →
执行：
from tensorflow. python. client import device_lib
device_lib. list_local_devices( )
[ name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 677841662562579601 , name: "/device:XLA_CPU:0"
device_type: "XLA_CPU"
memory_limit: 17179869184
locality {
}
incarnation: 9258589908592365980
physical_device_desc: "device: XLA_CPU device" , name: "/device:XLA_GPU:0"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 10896633435725669876
physical_device_desc: "device: XLA_GPU device" , name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 14800692839
locality {
bus_id: 1
links {
}
}
incarnation: 711525362527462258
physical_device_desc: "device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5" ]
用nvidia命令查看显卡使用情况：
!nvidia-smi
Sun May 5 11:51:49 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.56 Driver Version: 410.79 CUDA Version: 10.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |
| N/A 71C P0 31W / 70W | 221MiB / 15079MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
为了操作方便，先将路径换成云端硬盘的路径：
import os
os. chdir( '/content/drive/My Drive/' )
执行mnist_train：
!python test_code/mnist_train.py
step 0, training accuracy 0.171875
step 900, training accuracy 0.96875
step 1900, training accuracy 1
step 3900, training accuracy 0.984375
step 4900, training accuracy 1
测试结果：
!python test_code/mnist_test.py
accuracy is: 0.9778
完整教程代码： 点击前往
Colab对想搞深度学习没有好卡的人是一个非常好的工具，环境配置简单，自带tensorflow，还可以安装其他的深度学习的框架，还可以直接从github导入，虽然每次最多只能连续用12小时，但对于学习来说已经非常够用了。更重要的一点，对于动辄几百上千的GPU云服务器，colab是免费的！
转播
