Tensorflow Ubuntu - eiichiromomma/CVMLAB GitHub Wiki
(Tensorflow) Ubuntu
今は
pip install tensorflow-gpu
または
pip install tensorflow
で直ぐ使える。
CUDA環境の構築はDeepLearning CUDAを参照。
旧情報
[Ubuntu]] LTS(14.04)での[Anaconda用Tensorflow導入メモ (古い)
CUDA
nVIDIAの解説通り
$ sudo dpkg -i cuda-repo-ubuntu1404_7.5-18_amd64.deb
$ sudo aptitude update
$ sudo aptitude install cuda
あと、.bashrcにでも
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64"
export CUDA_HOME=/usr/local/cuda
を入れておいてsource .bashrcしておく。
但し/etc/modprobe.d/blacklist.confに
blacklist vga16fb
blacklist nouveau
blacklist rivafb
blacklist nvidiafb
blacklist rivatv
blacklist lbm-nouveua
options nouveau modeset=0
alias nouveau off
alias lbm-nouveau off
を入れておく。/usr/local/cuda/samples/1_Utilities/deviceQueryで
$ ./deviceQuery
を実行して認識されてれば成功。こんな感じの結果になる。
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce GTX 960"
CUDA Driver Version / Runtime Version 7.5 / 7.5
CUDA Capability Major/Minor version number: 5.2
Total amount of global memory: 2047 MBytes (2146762752 bytes)
( 8) Multiprocessors, (128) CUDA Cores/MP: 1024 CUDA Cores
GPU Max Clock rate: 1178 MHz (1.18 GHz)
Memory Clock rate: 3505 Mhz
Memory Bus Width: 128-bit
L2 Cache Size: 1048576 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 2 copy engine(s)
Run time limit on kernels: No
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 4 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 7.5, CUDA Runtime Version = 7.5, NumDevs = 1, Device0 = GeForce GTX 960
Result = PASS
あとcudnnもnVIDIAから持ってきて/usr/local/cudaに入れておく。
[Anaconda](Python Anaconda)
Linux用のシェルスクリプトをダウンロードして実行すればよい。
bazel
ここの通りにやれば良い。
$ sudo add-apt-repository ppa:webupd8team/java
$ sudo apt-get update
$ sudo apt-get install oracle-java8-installer
$ sudo apt-get install pkg-config zip g++ zlib1g-dev unzip
ここからスクリプトをダウンロードして実行する。
$ wget https://github.com/bazelbuild/bazel/releases/download/0.2.0/bazel-0.2.0-installer-linux-x86_64.sh
$ chmod +x bazel-0.2.0-installer-linux-x86_64.sh
$ ./bazel-0.2.0-installer-linux-x86_64.sh --user
のようにする(バージョン番号は適宜合わせる)。$HOME/binにインストールされるので.bashrcに
export PATH=$HOME/bin:$PATH
を入れてsourceしておく。
ビルドとインストール
swigを入れておく。
$ sudo apt-get install swig
ソースを持ってきてconfigure
$ git clone --recurse-submodules https://github.com/tensorflow/tensorflow
$ cd tensorflow
$ ./configure
基本的にEnterを押しておけば良いがcapabilityは自分のグラボのにだけしておけばビルドが速い。
$ bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer
で動くのを確認できたら仮想環境をactivateして、パッケージを作ってインストールする。
$ conda create -n tensorflow python=2.7 anaconda
$ source activate tensorflow
$ bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
$ pip install /tmp/tensorflow_pkg/tensorflow-0.7.1-py2-none-any.whl
インストールが成功したらサンプルを実行してみる。
$ cd tensorflow/models/image/mnist/
$ python convolutional.py
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally
Extracting data/train-images-idx3-ubyte.gz
Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:900] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties:
name: GeForce GTX 960
major: 5 minor: 2 memoryClockRate (GHz) 1.1775
pciBusID 0000:04:00.0
Total memory: 2.00GiB
Free memory: 1.95GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:718] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 960, pci bus id: 0000:04:00.0)
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 256B
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 512B
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 1.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 2.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 4.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 8.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 16.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 32.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 64.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 128.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 256.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 512.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 1.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 2.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 4.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 8.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 16.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 32.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 64.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 128.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:53] Creating bin of max chunk size 256.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:107] Allocating 1.75GiB bytes.
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:118] GPU 0 memory begins at 0x902780000 extends to 0x97298f000
のような感じになる。仮想環境から抜けるときは
$ source deactivate