Nvidia GPU Cloud

Tensorflow イメージ取得

$ docker login nvcr.io

Username: $oauthtoken
Password: <Your Key>

$ docker pull nvcr.io/nvidia/tensorflow:18.12-py3

Tensorflow イメージの実行

2019-01-14 現在の最新版

atsushi@ai-chan:~$ docker run --runtime=nvidia --rm -ti nvcr.io/nvidia/tensorflow:18.12-py3 /bin/bash

================
== TensorFlow ==
================

NVIDIA Release 18.12 (build 879479)

Container image Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
Copyright 2017-2018 The TensorFlow Authors.  All rights reserved.

Various files include modifications (c) NVIDIA CORPORATION.  All rights reserved.
NVIDIA modifications are covered by the license terms that apply to the underlying project or file.

NOTE: MOFED driver for multi-node communication was not detected.
      Multi-node communication performance may be reduced.

NOTE: The SHMEM allocation limit is set to the default of 64MB.  This may be
   insufficient for TensorFlow.  NVIDIA recommends the use of the following flags:
   nvidia-docker run --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 ...

root@8926a61e0715:/workspace# cat /etc/os-release
NAME="Ubuntu"
VERSION="16.04.5 LTS (Xenial Xerus)"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 16.04.5 LTS"
VERSION_ID="16.04"
HOME_URL="http://www.ubuntu.com/"
SUPPORT_URL="http://help.ubuntu.com/"
BUG_REPORT_URL="http://bugs.launchpad.net/ubuntu/"
VERSION_CODENAME=xenial
UBUNTU_CODENAME=xenial

root@8926a61e0715:/workspace# python --version
Python 3.5.2

root@8926a61e0715:/workspace# nvidia-smi
Mon Jan 14 13:18:40 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.93       Driver Version: 410.93       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  GeForce RTX 2070    Off  | 00000000:01:00.0 Off |                  N/A |
| 19%   20C    P8    19W / 175W |      8MiB /  7952MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+ 

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+

Keras の MNIST サンプルを動かしてみる

root@8926a61e0715:/workspace# pip3 install keras
root@8926a61e0715:/workspace# wget wget https://raw.githubusercontent.com/fchollet/keras/master/examples/mnist_cnn.py
root@8926a61e0715:/workspace# python3 mnist_cnn.py
Using TensorFlow backend.
Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz
11493376/11490434 [==============================] - 308s 27us/step
x_train shape: (60000, 28, 28, 1)
60000 train samples
10000 test samples
Train on 60000 samples, validate on 10000 samples
Epoch 1/12
2019-01-14 13:28:38.105766: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:957] successful NUMA node read from 
SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-01-14 13:28:38.106272: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: GeForce RTX 2070 major: 7 minor: 5 memoryClockRate(GHz): 1.62
pciBusID: 0000:01:00.0
totalMemory: 7.77GiB freeMemory: 7.64GiB
2019-01-14 13:28:38.106290: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-01-14 13:28:38.657944: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor 
with strength 1 edge matrix:
2019-01-14 13:28:38.657973: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0
2019-01-14 13:28:38.657995: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N
2019-01-14 13:28:38.658160: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device 
(/job:localhost/replica:0/task:0/device:GPU:0 with 7353 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2070, 
pci bus id: 0000:01:00.0, compute capability: 7.5)
60000/60000 [==============================] - 7s 123us/step - loss: 0.2643 - acc: 0.9182 - val_loss: 0.0753 - val_acc: 0.9776
Epoch 2/12
60000/60000 [==============================] - 5s 90us/step - loss: 0.0857 - acc: 0.9748 - val_loss: 0.0405 - val_acc: 0.9875
Epoch 3/12
60000/60000 [==============================] - 5s 82us/step - loss: 0.0647 - acc: 0.9806 - val_loss: 0.0325 - val_acc: 0.9893
Epoch 4/12
60000/60000 [==============================] - 4s 74us/step - loss: 0.0518 - acc: 0.9843 - val_loss: 0.0293 - val_acc: 0.9903
Epoch 5/12
60000/60000 [==============================] - 5s 76us/step - loss: 0.0455 - acc: 0.9864 - val_loss: 0.0286 - val_acc: 0.9911
Epoch 6/12
60000/60000 [==============================] - 5s 76us/step - loss: 0.0407 - acc: 0.9873 - val_loss: 0.0298 - val_acc: 0.9901
Epoch 7/12
60000/60000 [==============================] - 5s 80us/step - loss: 0.0361 - acc: 0.9889 - val_loss: 0.0272 - val_acc: 0.9906
Epoch 8/12
60000/60000 [==============================] - 5s 90us/step - loss: 0.0333 - acc: 0.9897 - val_loss: 0.0273 - val_acc: 0.9918
Epoch 9/12
60000/60000 [==============================] - 5s 90us/step - loss: 0.0296 - acc: 0.9911 - val_loss: 0.0275 - val_acc: 0.9916
Epoch 10/12
60000/60000 [==============================] - 5s 90us/step - loss: 0.0295 - acc: 0.9913 - val_loss: 0.0269 - val_acc: 0.9911
Epoch 11/12
60000/60000 [==============================] - 5s 81us/step - loss: 0.0255 - acc: 0.9918 - val_loss: 0.0265 - val_acc: 0.9906
Epoch 12/12
60000/60000 [==============================] - 4s 73us/step - loss: 0.0245 - acc: 0.9920 - val_loss: 0.0245 - val_acc: 0.9920
Test loss: 0.02450078019701341
Test accuracy: 0.992

速!


Deep Learning


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Last-modified: 2019-01-14 (月) 22:34:20 (245d)
ISBN10
ISBN13
9784061426061