{"id":1411,"date":"2021-08-18T02:25:13","date_gmt":"2021-08-17T17:25:13","guid":{"rendered":"http:\/\/www.ecobioinfo.com\/?p=1411"},"modified":"2022-06-05T22:12:22","modified_gmt":"2022-06-05T13:12:22","slug":"%e6%b7%b1%e5%b1%a4%e5%ad%a6%e7%bf%92%e3%80%80ubuntu-20-04-%e3%81%ab-tensorflow-gpu-%e3%82%92%e3%82%a4%e3%83%b3%e3%82%b9%e3%83%88%e3%83%bc%e3%83%ab","status":"publish","type":"post","link":"https:\/\/www.ecobioinfo.com\/?p=1411","title":{"rendered":"\u6df1\u5c64\u5b66\u7fd2\u3000Ubuntu 20.04 \u306b Tensorflow GPU \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb"},"content":{"rendered":"

Tensorflow \u306e\u74b0\u5883\uff08\u53c2\u8003\u300c\u6df1\u5c64\u5b66\u7fd2\u3000Ubuntu 18.04 \u306b Tensorflow \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/a>\u300d\u300c\u6df1\u5c64\u5b66\u7fd2\u3000Ubuntu 18.04 \u306b Tensorflow GPU \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/a>\u300d\uff09\u304c\u53e4\u304f\u306a\u3063\u305f\u306e\u3067\u3001Ubuntu\u306e 20.04 \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066 Tensorflow 2.5.0 \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3057\u305f\u3002
\n\u4ee5\u524d\u306f keras \u306f\u30aa\u30ea\u30b8\u30ca\u30eb\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3066\u3044\u307e\u3057\u305f\u304c\u3001\u4eca\u56de\u306f Tensorflow \u306b\u540c\u68b1\u3055\u308c\u3066\u3044\u308bkeras \u3092\u4f7f\u3046\u3053\u3068\u306b\u3057\u307e\u3059\u3002\uff08\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u4f5c\u696d\u306f 2021\/06\/24\u306b\u884c\u3044\u307e\u3057\u305f\u306e\u3067\u3001\u3053\u3053\u306e\u60c5\u5831\u306f\u305d\u306e\u6642\u70b9\u306e\u3082\u306e\u3067\u3059\u3002\uff09<\/p>\n

\uff1c\uff1c\uff1c\u3000\u30cf\u30fc\u30c9\u30a6\u30a7\u30a2\u3000\uff1e\uff1e\uff1e<\/p>\n

\u30cf\u30fc\u30c9\uff1a\u30de\u30a6\u30b9\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30fc LM-iH700B2N
\nCPU\uff1a Intel\u00ae Core\u2122 i3-8100 CPU @ 3.60GHz \u00d7 4
\n\u30e1\u30e2\u30ea\uff1a 12 GByte
\n\u672c\u4f53\u96fb\u6e90\uff1a 500W
\n\u30b0\u30e9\u30d5\u30a3\u30c3\u30af\u30dc\u30fc\u30c9\uff1aNVIDIA GEFORCE GTX1070 \u30e1\u30e2\u30ea 8GB
\n\u30b9\u30c8\u30ec\u30fc\u30b8\uff1aSSD 480GB<\/p>\n

(1) Ubuntu 20.04 \u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/p>\n

Ubuntu \u306e\u30b5\u30a4\u30c8\u304b\u3089 Ubuntu 20.04.1 LTS \u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3001DVD\u306b\u713c\u3044\u3066\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3002<\/p>\n

https:\/\/www.ubuntulinux.jp\/download\/ja-remix<\/a><\/p>\n

\u300c\u6df1\u5c64\u5b66\u7fd2\u3000Ubuntu 18.04 \u306b Tensorflow \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/span><\/a>\u300d\u3068\u307b\u307c\u540c\u69d8\u3067\u3059\u3002
\n\uff08\u4f46\u3057\u3001\u30b0\u30e9\u30d5\u30a3\u30c3\u30af\u30dc\u30fc\u30c9 NVIDIA\u30c9\u30e9\u30a4\u30d0\u306e\u8a2d\u5b9a\u306f (3)\u3067\u884c\u3063\u3066\u307e\u3059\u3002\uff09<\/p>\n

 <\/p>\n

(2) Anaconda + Python \u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/p>\n

Anaconda\u306e\u30b5\u30a4\u30c8\u304b\u3089\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9<\/p>\n

https:\/\/www.anaconda.com\/products\/individual#download<\/a><\/p>\n

\u30da\u30fc\u30b8\u4e0b\u306e\u65b9\u306e Anaconda Installers \u3067 Linux 64-Bit (x86) Installer (544 MB) \u3092\u9078\u629e\u3002<\/p>\n

Anaconda3-2021.05-Linux-x86_64.sh \u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9<\/p>\n

\u53f3\u30af\u30ea\u30c3\u30af\u300c\u7aef\u672b\u3092\u958b\u304f\u300d\u3067\u30bf\u30fc\u30df\u30ca\u30eb\u3092\u958b\u3044\u3066\u5b9f\u884c\u3002<\/p>\n

~$ chmod +x Anaconda3-2021.05-Linux-x86_64.sh
\n~$ .\/Anaconda3-2021.05-Linux-x86_64.sh<\/p>\n

\u30d1\u30b9\u8a2d\u5b9a
\n~$ source .bashrc<\/p>\n

\u78ba\u8a8d<\/p>\n

~$ python -V
\nPython 3.8.8<\/p>\n

~$ conda -V
\nconda 4.10.1<\/p>\n

 <\/p>\n

(3) NVIDIA\u30c9\u30e9\u30a4\u30d0\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/p>\n

\u300c\u6df1\u5c64\u5b66\u7fd2\u3000Ubuntu 18.04 \u306b Tensorflow GPU \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/span><\/a>\u300d<\/span><\/a>\u3068\u540c\u69d8\u306b\u3001\u30bb\u30ad\u30e5\u30a2\u30d6\u30fc\u30c8\u3092\u5916\u3057\u3066\u3001\u30c9\u30e9\u30a4\u30d0\u66f4\u65b0\u3092\u884c\u3046\u3002<\/p>\n

~$ sudo apt-get remove nvidia-*
\n~$ sudo apt-get remove cuda-*<\/p>\n

~$ sudo add-apt-repository ppa:graphics-drivers\/ppa
\n~$ sudo apt update
\n~$ sudo apt install nvidia-driver-470<\/p>\n

\u518d\u8d77\u52d5<\/p>\n

\u78ba\u8a8d
\n~$ nvidia-smi
\nSat Jul 24 14:42:22 2021
\n+—————————————————————————–+
\n| NVIDIA-SMI 470.42.01\u00a0\u00a0\u00a0 Driver Version: 470.42.01\u00a0\u00a0\u00a0 CUDA Version: 11.4\u00a0\u00a0\u00a0\u00a0 |
\n|——————————-+———————-+———————-+
\n| GPU\u00a0 Name\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Persistence-M| Bus-Id\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Disp.A | Volatile Uncorr. ECC |
\n| Fan\u00a0 Temp\u00a0 Perf\u00a0 Pwr:Usage\/Cap|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Memory-Usage | GPU-Util\u00a0 Compute M. |
\n|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 MIG M. |
\n|===============================+======================+======================|
\n|\u00a0\u00a0 0\u00a0 NVIDIA GeForce …\u00a0 On\u00a0\u00a0 | 00000000:01:00.0\u00a0 On |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 N\/A |
\n| 27%\u00a0\u00a0 40C\u00a0\u00a0\u00a0 P8\u00a0\u00a0\u00a0 10W \/ 151W |\u00a0\u00a0\u00a0 143MiB \/\u00a0 8111MiB |\u00a0\u00a0\u00a0\u00a0\u00a0 0%\u00a0\u00a0\u00a0\u00a0\u00a0 Default |
\n|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 N\/A |
\n+——————————-+———————-+———————-+<\/p>\n

+—————————————————————————–+
\n| Processes:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 |
\n|\u00a0 GPU\u00a0\u00a0 GI\u00a0\u00a0 CI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 PID\u00a0\u00a0 Type\u00a0\u00a0 Process name\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 GPU Memory |
\n|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ID\u00a0\u00a0 ID\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Usage\u00a0\u00a0\u00a0\u00a0\u00a0 |
\n|=============================================================================|
\n|\u00a0\u00a0\u00a0 0\u00a0\u00a0 N\/A\u00a0 N\/A\u00a0\u00a0\u00a0\u00a0\u00a0 1103\u00a0\u00a0\u00a0\u00a0\u00a0 G\u00a0\u00a0 \/usr\/lib\/xorg\/Xorg\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 23MiB |
\n|\u00a0\u00a0\u00a0 0\u00a0\u00a0 N\/A\u00a0 N\/A\u00a0\u00a0\u00a0\u00a0\u00a0 1638\u00a0\u00a0\u00a0\u00a0\u00a0 G\u00a0\u00a0 \/usr\/lib\/xorg\/Xorg\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 32MiB |
\n|\u00a0\u00a0\u00a0 0\u00a0\u00a0 N\/A\u00a0 N\/A\u00a0\u00a0\u00a0\u00a0\u00a0 1844\u00a0\u00a0\u00a0\u00a0\u00a0 G\u00a0\u00a0 \/usr\/bin\/gnome-shell\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 75MiB |
\n+—————————————————————————–+<\/p>\n

 <\/p>\n

(4) CUDA\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/p>\n

CUDA Toolkit 11.4 \u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9<\/p>\n

https:\/\/developer.nvidia.com\/cuda-toolkit-archive<\/a><\/p>\n

https:\/\/developer.nvidia.com\/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=20.04&target_type=deb_local<\/a><\/p>\n

Lunux , X86_64 , Ubuntu , 20.04 , deb (local) \u3092\u9078\u629e\u3002<\/p>\n

\u300cDownload Installer for Linux Ubuntu 20.04 x86_64\u300d\u6b04\u306e\u8a18\u8ff0\u3069\u304a\u308a\u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3002<\/p>\n

~$ wget https:\/\/developer.download.nvidia.com\/compute\/cuda\/repos\/ubuntu2004\/x86_64\/cuda-ubuntu2004.pin
\n~$ sudo mv cuda-ubuntu2004.pin \/etc\/apt\/preferences.d\/cuda-repository-pin-600
\n~$ wget https:\/\/developer.download.nvidia.com\/compute\/cuda\/11.4.0\/local_installers\/cuda-repo-ubuntu2004-11-4-local_11.4.0-470.42.01-1_amd64.deb
\n~$ sudo dpkg -i cuda-repo-ubuntu2004-11-4-local_11.4.0-470.42.01-1_amd64.deb
\n~$ sudo apt-key add \/var\/cuda-repo-ubuntu2004-11-4-local\/7fa2af80.pub
\n~$ sudo apt-get update
\n~$ sudo apt-get -y install cuda<\/p>\n

\u9014\u4e2d\u3067\u3001\u300c\u30e1\u30c7\u30a3\u30a2\u5909\u66f4:
\n‘Ubuntu 20.04.1 _Focal Fossa_ – Release amd64(20200808.1)’
\n\u3068\u30e9\u30d9\u30eb\u306e\u4ed8\u3044\u305f\u30c7\u30a3\u30b9\u30af\u3092\u30c9\u30e9\u30a4\u30d6 ‘\/cdrom\/’ \u306b\u5165\u308c\u3066 [Enter] \u30ad\u30fc\u3092\u62bc\u3057\u3066\u304f\u3060\u3055\u3044\u300d
\n\u3068\u306e\u30e1\u30c3\u30bb\u30fc\u30b8\u304c\u51fa\u305f\u306e\u3067\u3001\/cdrom\/\u306b Ubuntu 20.04.1 LTS \u306e DVD\u3092\u30de\u30a6\u30f3\u30c8\u3057\u3066\u518d\u5b9f\u884c\u3057\u307e\u3057\u305f\u3002<\/p>\n

\u203b\/cdrom\/\u306b\u30de\u30a6\u30f3\u30c8\u3059\u308b
\n~$ sudo mount \/dev\/dvd \/cdrom<\/p>\n

\u30d1\u30b9\u8a2d\u5b9a
\n.bashrc\u3000\u306b\u8ffd\u52a0\u3002
\nexport PATH=\u201d\/usr\/local\/cuda-11.4\/bin:$PATH\u201d
\nexport LD_LIBRARY_PATH=\u201d\/usr\/local\/cuda\/lib64:$LD_LIBRARY_PATH\u201d<\/p>\n

~$ source .bashrc<\/p>\n

 <\/p>\n

(5) cuCNN\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/p>\n

cuCNN\u306e\u30b5\u30a4\u30c8
\nhttps:\/\/developer.nvidia.com\/rdp\/cudnn-download<\/a><\/p>\n

Instraction\u306e\u30da\u30fc\u30b8
\nhttps:\/\/docs.nvidia.com\/deeplearning\/cudnn\/install-guide\/index.html<\/a><\/p>\n

\u30a2\u30ab\u30a6\u30f3\u30c8\u306e\u306a\u3044\u65b9\u306f\u3001\u30a2\u30ab\u30a6\u30c8\u3092\u4f5c\u6210\u3057\u3066\u30ed\u30b0\u30a4\u30f3\u3057\u307e\u3059\u3002<\/p>\n

Download cuDNN v8.2.2 (July 6th, 2021), for CUDA 11.4 \u3092\u9078\u629e\u3002<\/p>\n

\u4ee5\u4e0b\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9
\ncuDNN Runtime Library for Ubuntu20.04 x86_64 (Deb)
\ncuDNN Developer Library for Ubuntu20.04 x86_64 (Deb)
\ncuDNN Code Samples and User Guide for Ubuntu20.04 x86_64 (Deb)<\/p>\n

\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb
\n~$ sudo dpkg -i libcudnn8_8.2.2.26-1+cuda11.4_amd64.deb
\n~$ sudo dpkg -i libcudnn8-dev_8.2.2.26-1+cuda11.4_amd64.deb
\n~$ sudo dpkg -i libcudnn8-samples_8.2.2.26-1+cuda11.4_amd64.deb<\/p>\n

 <\/p>\n

(7) tensorflow-gpu\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/p>\n

~$ pip install –upgrade tensorflow-gpu<\/p>\n

tensorflow\u3068keras\u306e\u78ba\u8a8d<\/p>\n

~$ python
\n>>> import tensorflow as tf
\n>>> import tensorflow.keras as ke
\n>>>
\n>>> print(tf.__version__)
\n2.5.0
\n>>> print(ke.__version__)
\n2.5.0
\n>>><\/p>\n

GPU\u306e\u78ba\u8a8d<\/p>\n

>>> from tensorflow.python.client import device_lib
\n>>> device_lib.list_local_devices()
\n2021-07-24 15:22:46.645889: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1418] Created TensorFlow device (\/device:GPU:0 with 7280 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1)
\n[name: “\/device:CPU:0”
\ndevice_type: “CPU”
\nmemory_limit: 268435456
\nlocality {
\n}
\nincarnation: 9413427757515000154
\n, name: “\/device:GPU:0”
\ndevice_type: “GPU”
\nmemory_limit: 7634616320
\nlocality {
\nbus_id: 1
\nlinks {
\n}
\n}
\nincarnation: 11483819985304158075
\nphysical_device_desc: “device: 0, name: NVIDIA GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1”<\/p>\n

 <\/p>\n

(8) \u4eee\u60f3\u74b0\u5883\u3068 Jupyter notebook \u8d77\u52d5\u30b7\u30a7\u30eb<\/p>\n

\u4eee\u60f3\u74b0\u5883\u306e\u4f5c\u6210\u3092\u4f5c\u6210\u3057\u3066 Jupyter notebook \u8d77\u52d5\u30b7\u30a7\u30eb\u3092\u4f5c\u308a\u307e\u3057\u305f\u3002
\n~$ conda create -n tf<\/p>\n

\u4eee\u60f3\u74b0\u5883\u3067 Jupyter notebook \u3092\u8d77\u52d5\u3059\u308b shell
\ncd \/home\/(username)\/anaconda3\/envs\/tf\/
\nconda activate tf
\njupyter notebook<\/p>\n

 <\/p>\n

(9)\u52d5\u4f5c\u78ba\u8a8d<\/p>\n

\u300c\u6df1\u5c64\u5b66\u7fd2\u3000Keras\uff08TensorFlow\u7528\uff09\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3001\uff08\uff12\uff09\u5b66\u7fd2\u3068\u30c6\u30b9\u30c8 (MNIST\u3092\u8a66\u3059)<\/a>\u300d\u306e\u30bd\u30fc\u30b9\u3067\u30c6\u30b9\u30c8\u3002
\nkeras\u306ftensorflow\u306e\u3082\u306e\u3092\u4f7f\u3046\u306e\u3067\u30a4\u30f3\u30dd\u30fc\u30c8\u3092\u5909\u66f4\u3057\u307e\u3057\u305f\u3002<\/p>\n

\u30c6\u30b9\u30c8\u30d7\u30ed\u30b0\u30e9\u30e0\uff1a(\u30c6\u30ad\u30b9\u30c8\u30d5\u30a1\u30a4\u30eb\u3001\u6587\u5b57\u30b3\u30fc\u30c9 UTF-8)
\nkeras_mnist_test.py<\/a><\/p>\n

———————–
\n\u5b9f\u884c\u7d50\u679c(\u4e00\u90e8\u629c\u7c8b)
\n540\/540 [==============================] – 1s 1ms\/step – loss: 0.2568 – accuracy: 0.9283 – val_loss: 0.1641 – val_accuracy: 0.9577
\nEpoch 20\/20
\n540\/540 [==============================] – 1s 1ms\/step – loss: 0.2489 – accuracy: 0.9294 – val_loss: 0.1605 – val_accuracy: 0.9587
\n[7 2 1 … 4 5 6]
\n[7 2 1 … 4 5 6]
\n0.9474
\n———————–<\/p>\n

\u30c6\u30b9\u30c8\u3057\u305f\u7bc4\u56f2\u3067\u306f\u4e92\u63db\u554f\u984c\u3082\u306a\u304f\u4f7f\u3048\u3066\u307e\u3059\u3002<\/p>\n

(S.Onda 2021\/8\/18)<\/p>\n","protected":false},"excerpt":{"rendered":"Tensorflow \u306e\u74b0\u5883\uff08\u53c2\u8003\u300c\u6df1\u5c64\u5b66\u7fd2\u3000Ubuntu 18.04 \u306b Tensorflow \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u300d\u300c\u6df1\u5c64\u5b66\u7fd2\u3000Ubuntu 18.04 \u306b Tensorflow GPU \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u300d\uff09\u304c\u53e4\u304f\u306a\u3063\u305f\u306e\u3067 …","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-1411","post","type-post","status-publish","format-standard","hentry","category-ai_ecobio"],"_links":{"self":[{"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/posts\/1411","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1411"}],"version-history":[{"count":16,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/posts\/1411\/revisions"}],"predecessor-version":[{"id":1427,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/posts\/1411\/revisions\/1427"}],"wp:attachment":[{"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1411"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1411"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1411"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}