Julia\uc5d0 TensorFlow\uc744 \uc124\uce58\uc2dc \ud2b9\ud788 GPU \uc9c0\uc6d0 \ubc84\uc804 \uc124\uce58\uc2dc \ub0b4 \uc11c\ubc84\uc5d0 \uc124\uce58\ub41c cuda \ubc84\uc804\uacfc \ucd94\uac00\ud55c tensorflow build \ubc84\uc804\uc774 \ub9de\uc9c0 \uc54a\ub294 \uacbd\uc6b0<\/p>\n\n\n\n
\ucee4\uc2a4\ud140\uc73c\ub85c tensorflow\ub97c \ube4c\ub4dc \ud558\uace0 \ud6c4\uc18d \uc791\uc5c5\uc744 \ud574\uc57c \ud55c\ub2e4.<\/p>\n\n\n\n
\uba3c\uc800 Julia TensorFlow \ud328\ud0a4\uc9c0\ub97c \uc124\uce58 \ud558\uace0 GPU \ubc84\uc804\uc73c\ub85c \ucef4\ud30c\uc77c \ud55c\ub2e4.<\/p>\n\n\n\n
Julia>ENV[\"TF_USE_GPU\"] = \"1\"\nPkg>add TensorFlow#master\nPkg>build TensorFlow<\/code><\/pre>\n\n\n\n\ube4c\ub4dc\ub41c TensorFlow \uc774\ubbf8\uc9c0\ub294 \uc608\ub97c \ub4e4\uc5b4 \/home\/shpark\/.julia\/packages\/TensorFlow\/JljDB\/deps\/usr\/bin \uc5d0 \uc0dd\uc131\ub41c\ub2e4.<\/p>\n\n\n\n
\uc815\uc0c1\uc801\uc73c\ub85c \uc0dd\uc131\ub418\uba74 \uc544\ub798 \ub450\uac1c\uc758 \ud30c\uc77c\uc774 \uc0dd\uc131\ub41c\ub2e4<\/p>\n\n\n\n
libtensorflow.so \nlibtensorflow_framework.so <\/code><\/pre>\n\n\n\nJulia\uc5d0\uc11c \uc544\ub798 \ucf54\ub4dc\ub97c \uc2e4\ud589 \ud588\uc744 \ub54c \uc5d0\ub7ec\ub098 \ud604\uc81c \uc124\uce58\ub41c cuda\ubc84\uc804\uacfc \ub2e4\ub978 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ucc3e\ub294\ub2e4\uba74 \uc704\uc758 .so \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ad\uc81c\ud558\uace0<\/p>\n\n\n\n
\ucee4\uc2a4\ud140 \ube4c\ub4dc\ub97c \ud55c\ub2e4.<\/p>\n\n\n\n
Julia> using TensorFlow\nJulia> TensorFlow.Session()<\/code><\/pre>\n\n\n\nTensorflow 1.10 ~ 1.12 \ubc84\uc804\uc740 Bazel \ubc84\uc804 0.18 \ubc84\uc804\uc73c\ub85c \ube4c\ub4dc \ud55c\ub2e4<\/p>\n\n\n\n
1.3 \uc774 \ud6c4 \ubd80\ucc98 Bazel 0.26.1 \ub85c \ud55c\ub2e4<\/p>\n\n\n\n
\uae30\uc874\uc5d0 \ub192\uc740 \ubc84\uc804\uc758 Bazel\uc774 \uc124\uce58 \ub418\uc5b4 \uc788\ub2e4\uba74 \uc544\ub798\uc640 \uac19\uc774 \uc0ad\uc81c \ud55c\ub2e4.<\/p>\n\n\n\n
bazel shutdown\nrm $HOME\/.cache\/bazel -fr \nsudo rm \/usr\/local\/bin\/bazel \/etc\/bazelrc \/usr\/local\/lib\/bazel -fr<\/code><\/pre>\n\n\n\nBazel 0.26<\/a>.1 \ub2e4\uc6b4\ub85c\ub4dc<\/a> (\ub354 \ub192\uc740 \ubc84\uc804\uc5d0\uc11c\ub294 \ube4c\ub4dc\uac00 \uc548\ub428)<\/p>\n\n\n\nwget https:\/\/releases.bazel.build\/0.26.1\/release\/bazel-0.26.1-installer-linux-x86_64.sh\nchmod +x bazel-0.26.1-installer-linux-x86_64.sh\n.\/bazel-0.26.1-installer-linux-x86_64.sh --user<\/code><\/pre>\n\n\n\nTensorflow 1.13.1\ub2e4\uc6b4\ub85c\ub4dc<\/a>
\ube4c\ub4dc\ub97c \uc704\ud574 nccl\ub3c4 \ud544\uc694 \ud558\ub2e4
nccl \ub2e4\uc6b4\ub85c\ub4dc<\/a> \uc5d0\uc11c \ud30c\uc77c\uc744 \ubc1b\uc544 \ube4c\ub4dc \ud55c\ub2e4. <\/p>\n\n\n\n\ube4c\ub4dc \ud6c4 \uc544\ub798\uc640 \uac19\uc774 nccl \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ubcf5\uc0ac \ud574\uc900\ub2e4<\/p>\n\n\n\n
cd build\nsudo mkdir -p \/usr\/local\/cuda\/nccl\/lib \/usr\/local\/cuda\/nccl\/include\nsudo cp *.txt \/usr\/local\/cuda\/nccl\nsudo cp include\/*.h \/usr\/include\/\nsudo cp lib\/libnccl.so.2.5.6 lib\/libnccl_static.a \/usr\/lib\/x86_64-linux-gnu\/\nsudo ln -s \/usr\/include\/nccl.h \/usr\/local\/cuda\/nccl\/include\/nccl.h\ncd \/usr\/lib\/x86_64-linux-gnu\nsudo ln -s libnccl.so.2.5.6 libnccl.so.2\nsudo ln -s libnccl.so.2 libnccl.so\nfor i in libnccl*; do sudo ln -s \/usr\/lib\/x86_64-linux-gnu\/$i \/usr\/local\/cuda\/nccl\/lib\/$i; done<\/code><\/pre>\n\n\n\n\ubc1b\uc740 Tensorflow\ub97c \ud480\uace0 tensorflow \ud3f4\ub354\ub85c \uc774\ub3d9\ud55c\ub2e4.<\/p>\n\n\n\n
.\/configure\nDo you wish to build TensorFlow with CUDA support? [y\/N]:y<\/code><\/pre>\n\n\n\nTensorflow build<\/p>\n\n\n\n
bazel build --config=opt --config=cuda --cxxopt=\"-D_GLIBCXX_USE_CXX11_ABI=0\" \/\/tensorflow:libtensorflow.so<\/code><\/pre>\n\n\n\nBuild\uc644\ub8cc \ud6c4 (Tensorflow package\uc758 \ub77c\uc774\ube0c\ub7ec\ub9ac\uac00 \ubcf5\uc0ac\ub418\ub294 \uacf3(\uc608\uc2dc) \u00a0\/home\/shpark\/.julia\/packages\/TensorFlow\/JljDB\/deps\/usr\/bin )<\/p>\n\n\n\n
bazel shutdown\ncd bazel-bin\/tensorflow\ncp -a * \/home\/shpark\/.julia\/packages\/TensorFlow\/JljDB\/deps\/usr\/bin\ncd \/home\/shpark\/.julia\/packages\/TensorFlow\/JljDB\/deps\/usr\/bin\ncd ln -s libtensorflow_framework.so.2 libtensorflow_framework.so\nls -al\nlrwxrwxrwx 1 shpark shpark 28 Dec 4 02:33 libtensorflow_framework.so -> libtensorflow_framework.so.2*\nlrwxrwxrwx 1 shpark shpark 32 Dec 4 02:26 libtensorflow_framework.so.2 -> libtensorflow_framework.so.2.0.0*\n-r-xr-xr-x 1 shpark shpark 34342328 Dec 4 02:26 libtensorflow_framework.so.2.0.0*\n-r-xr-xr-x 1 shpark shpark 22121 Dec 4 01:54 libtensorflow_framework.so.2.0.0-2.params*\nlrwxrwxrwx 1 shpark shpark 18 Dec 4 02:27 libtensorflow.so -> libtensorflow.so.2*\nlrwxrwxrwx 1 shpark shpark 22 Dec 4 02:27 libtensorflow.so.2 -> libtensorflow.so.2.0.0*\n-r-xr-xr-x 1 shpark shpark 539673528 Dec 4 02:27 libtensorflow.so.2.0.0*\n-r-xr-xr-x 1 shpark shpark 143213 Dec 4 01:55 libtensorflow.so.2.0.0-2.params*<\/code><\/pre>\n\n\n\nJulia\uc5d0\uc11c \uc544\ub798\ucf54\ub4dc \uc2e4\ud589\uc2dc<\/p>\n\n\n\n
Julia> using TensorFlow\nJulia> TensorFlow.Session()<\/code><\/pre>\n\n\n\n\uc544\ub798\uc640 \uac19\uc774 GPU \uc815\ubcf4\uac00 \ub098\uc624\uba74 \uc131\uacf5 \ud55c\uac83\uc784<\/p>\n\n\n\n
2019-12-04 02:38:29.094119: I tensorflow\/core\/platform\/profile_utils\/cpu_utils.cc:94] CPU Frequency: 2598210000 Hz\n2019-12-04 02:38:29.096951: I tensorflow\/compiler\/xla\/service\/service.cc:168] XLA service 0x3569360 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n2019-12-04 02:38:29.096994: I tensorflow\/compiler\/xla\/service\/service.cc:176] StreamExecutor device (0): Host, Default Version\n2019-12-04 02:38:29.100743: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n2019-12-04 02:38:29.648171: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1551] Found device 0 with properties:\nname: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582\npciBusID: 0000:05:00.0\n2019-12-04 02:38:29.649442: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1551] Found device 1 with properties:\nname: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582\npciBusID: 0000:06:00.0\n2019-12-04 02:38:29.650699: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1551] Found device 2 with properties:\nname: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582\npciBusID: 0000:09:00.0\n2019-12-04 02:38:29.651941: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1551] Found device 3 with properties:\nname: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582\npciBusID: 0000:0a:00.0\n2019-12-04 02:38:29.652168: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2019-12-04 02:38:29.653514: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n2019-12-04 02:38:29.654750: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n2019-12-04 02:38:29.654994: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n2019-12-04 02:38:29.656261: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n2019-12-04 02:38:29.656903: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n2019-12-04 02:38:29.659538: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n2019-12-04 02:38:29.671761: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1679] Adding visible gpu devices: 0, 1, 2, 3\n2019-12-04 02:38:29.671796: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2019-12-04 02:38:29.677085: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1092] Device interconnect StreamExecutor with strength 1 edge matrix:\n2019-12-04 02:38:29.677102: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1098] 0 1 2 3\n2019-12-04 02:38:29.677111: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1111] 0: N Y Y Y\n2019-12-04 02:38:29.677117: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1111] 1: Y N Y Y\n2019-12-04 02:38:29.677124: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1111] 2: Y Y N Y\n2019-12-04 02:38:29.677130: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1111] 3: Y Y Y N\n2019-12-04 02:38:29.687338: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1237] Created TensorFlow device (\/job:localhost\/replica:0\/task:0\/device:GPU:0 with 11436 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:05:00.0, compute capability: 6.1)\n2019-12-04 02:38:29.690090: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1237] Created TensorFlow device (\/job:localhost\/replica:0\/task:0\/device:GPU:1 with 11439 MB memory) -> physical GPU (device: 1, name: TITAN Xp, pci bus id: 0000:06:00.0, compute capability: 6.1)\n2019-12-04 02:38:29.692745: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1237] Created TensorFlow device (\/job:localhost\/replica:0\/task:0\/device:GPU:2 with 11439 MB memory) -> physical GPU (device: 2, name: TITAN Xp, pci bus id: 0000:09:00.0, compute capability: 6.1)\n2019-12-04 02:38:29.695417: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1237] Created TensorFlow device (\/job:localhost\/replica:0\/task:0\/device:GPU:3 with 11439 MB memory) -> physical GPU (device: 3, name: TITAN Xp, pci bus id: 0000:0a:00.0, compute capability: 6.1)\nSession(Ptr{Nothing} @0x00007ff5a081c070)\n<\/code><\/pre>\n\n\n\n\uadf8\ub9ac\uace0 \uc544\ub798\uc640 \uac19\uc774 \ud655\uc778 \ud574\ubcfc \uc218\ub3c4 \uc788\ub2e4. \uc704 \ucf54\ub4dc\ub97c \uc2e4\ud589 \ud558\uba74 julia\uc544 GPU\uba54\ubaa8\ub9ac\ub97c \ucc28\uc9c0 \ud55c\uac83\uc774 \ubcf4\uc778\ub2e4.<\/p>\n\n\n\n
$ nvidia-smi\nWed Dec 4 02:57:46 2019\n+-----------------------------------------------------------------------------+\n| NVIDIA-SMI 418.87.00 Driver Version: 418.87.00 CUDA Version: 10.1 |\n|-------------------------------+----------------------+----------------------+\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage\/Cap| Memory-Usage | GPU-Util Compute M. |\n|===============================+======================+======================|\n| 0 TITAN Xp On | 00000000:05:00.0 Off | N\/A |\n| 0% 21C P8 15W \/ 250W | 155MiB \/ 12193MiB | 0% Default |\n+-------------------------------+----------------------+----------------------+\n| 1 TITAN Xp On | 00000000:06:00.0 Off | N\/A |\n| 0% 21C P8 8W \/ 250W | 155MiB \/ 12196MiB | 0% Default |\n+-------------------------------+----------------------+----------------------+\n| 2 TITAN Xp On | 00000000:09:00.0 Off | N\/A |\n| 0% 22C P8 8W \/ 250W | 155MiB \/ 12196MiB | 0% Default |\n+-------------------------------+----------------------+----------------------+\n| 3 TITAN Xp On | 00000000:0A:00.0 Off | N\/A |\n| 0% 18C P8 7W \/ 250W | 155MiB \/ 12196MiB | 0% Default |\n+-------------------------------+----------------------+----------------------+\n\n+-----------------------------------------------------------------------------+\n| Processes: GPU Memory |\n| GPU PID Type Process name Usage |\n|=============================================================================|\n| 0 17252 C julia 145MiB |\n| 1 17252 C julia 145MiB |\n| 2 17252 C julia 145MiB |\n| 3 17252 C julia 145MiB |\n+-----------------------------------------------------------------------------+\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"Julia\uc5d0 TensorFlow\uc744 \uc124\uce58\uc2dc \ud2b9\ud788 GPU \uc9c0\uc6d0 \ubc84\uc804 \uc124\uce58\uc2dc \ub0b4 \uc11c\ubc84\uc5d0 \uc124\uce58\ub41c cuda \ubc84\uc804\uacfc \ucd94\uac00\ud55c tensorflow build \ubc84\uc804\uc774 \ub9de\uc9c0 \uc54a\ub294 \uacbd\uc6b0 \ucee4\uc2a4\ud140\uc73c\ub85c tensorflow\ub97c \ube4c\ub4dc \ud558\uace0 \ud6c4\uc18d \uc791\uc5c5\uc744 \ud574\uc57c \ud55c\ub2e4. \uba3c\uc800 Julia TensorFlow \ud328\ud0a4\uc9c0\ub97c \uc124\uce58 \ud558\uace0 GPU \ubc84\uc804\uc73c\ub85c \ucef4\ud30c\uc77c \ud55c\ub2e4. \ube4c\ub4dc\ub41c TensorFlow \uc774\ubbf8\uc9c0\ub294 \uc608\ub97c \ub4e4\uc5b4 \/home\/shpark\/.julia\/packages\/TensorFlow\/JljDB\/deps\/usr\/bin \uc5d0 \uc0dd\uc131\ub41c\ub2e4. \uc815\uc0c1\uc801\uc73c\ub85c \uc0dd\uc131\ub418\uba74 \uc544\ub798 \ub450\uac1c\uc758 \ud30c\uc77c\uc774 \uc0dd\uc131\ub41c\ub2e4 Julia\uc5d0\uc11c \uc544\ub798 \ucf54\ub4dc\ub97c […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center 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center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[24],"tags":[],"_links":{"self":[{"href":"https:\/\/julialang.kr\/index.php?rest_route=\/wp\/v2\/posts\/2380"}],"collection":[{"href":"https:\/\/julialang.kr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/julialang.kr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/julialang.kr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/julialang.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2380"}],"version-history":[{"count":3,"href":"https:\/\/julialang.kr\/index.php?rest_route=\/wp\/v2\/posts\/2380\/revisions"}],"predecessor-version":[{"id":2383,"href":"https:\/\/julialang.kr\/index.php?rest_route=\/wp\/v2\/posts\/2380\/revisions\/2383"}],"wp:attachment":[{"href":"https:\/\/julialang.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2380"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/julialang.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2380"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/julialang.kr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2380"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}