\u5c06\u5b89\u88c5\u7248\u672c1.11.0\u8bb0\u5f55\u4e0b\u6765\u3002<\/p>\n
$ <\/span>git checkout refs\/tags\/v1.11.0\r\n$ <\/span>.\/configure\r\n(<\/span>\u5f8c\u8ff0)<\/span>\r\n$ <\/span>bazel build --config<\/span>=<\/span>opt --config<\/span>=<\/span>cuda \/\/tensorflow\/tools\/pip_package:build_pip_package \r\n$ <\/span>bazel-bin\/tensorflow\/tools\/pip_package\/build_pip_package \/tmp\/tensorflow_pkg\r\n$ <\/span>pip install<\/span> \/tmp\/tensorflow_pkg\/tensorflow-1.10.0-cp36-cp36m-linux_x86_64.whl \r\n<\/code><\/pre>\n\u5982\u679c\u5728.\/configure\u4e2d\u8fdb\u884c\u8bbe\u7f6e\uff0c\u5c06\u4f7f\u7528GPU\u800c\u4e0d\u4f7f\u7528GCP\u6216AWS\u3002<\/p>\n
$ <\/span>.\/configure \r\nWARNING: Running Bazel server needs to be killed, because the startup options are different.\r\nWARNING: --batch<\/span> mode is deprecated. Please instead explicitly shut down your Bazel server using the command<\/span> \"bazel shutdown\"<\/span>.<\/span>\r\nYou have bazel 0.16.0 installed.\r\nPlease specify the location of python. [<\/span>Default is \/home\/niz\/anaconda3\/bin\/python]: (<\/span>\u30ea\u30bf\u30fc\u30f3)<\/span>\r\n\r\n\r\nFound possible Python library paths:\r\n \/home\/niz\/anaconda3\/lib\/python3.6\/site-packages\r\n \/home\/niz\/lab\/ext\/tensorflow\/models\/research\r\n \/home\/niz\/lab\/ext\/tensorflow\/models\/research\/slim\r\nPlease input the desired Python library path to use. Default is [<\/span>\/home\/niz\/anaconda3\/lib\/python3.6\/site-packages]\r\n\r\nDo you wish to build TensorFlow with jemalloc as malloc support? [<\/span>Y\/n]: (<\/span>\u30ea\u30bf\u30fc\u30f3)<\/span>\r\njemalloc as malloc support will be enabled for <\/span>TensorFlow.\r\n\r\nDo you wish to build TensorFlow with Google Cloud Platform support? [<\/span>Y\/n]: n\r\nNo Google Cloud Platform support will be enabled for <\/span>TensorFlow.\r\n\r\nDo you wish to build TensorFlow with Hadoop File System support? [<\/span>Y\/n]: n\r\nNo Hadoop File System support will be enabled for <\/span>TensorFlow.\r\n\r\nDo you wish to build TensorFlow with Amazon AWS Platform support? [<\/span>Y\/n]: n\r\nNo Amazon AWS Platform support will be enabled for <\/span>TensorFlow.\r\n\r\nDo you wish to build TensorFlow with Apache Kafka Platform support? [<\/span>Y\/n]: n\r\nNo Apache Kafka Platform support will be enabled for <\/span>TensorFlow.\r\n\r\nDo you wish to build TensorFlow with XLA JIT support? [<\/span>y\/N]: (<\/span>\u30ea\u30bf\u30fc\u30f3)<\/span>\r\nNo XLA JIT support will be enabled for <\/span>TensorFlow.\r\n\r\nDo you wish to build TensorFlow with GDR support? [<\/span>y\/N]: (<\/span>\u30ea\u30bf\u30fc\u30f3)<\/span>\r\nNo GDR support will be enabled for <\/span>TensorFlow.\r\n\r\nDo you wish to build TensorFlow with VERBS support? [<\/span>y\/N]: (<\/span>\u30ea\u30bf\u30fc\u30f3)<\/span>\r\nNo VERBS support will be enabled for <\/span>TensorFlow.\r\n\r\nDo you wish to build TensorFlow with nGraph support? [<\/span>y\/N]: (<\/span>\u30ea\u30bf\u30fc\u30f3)<\/span>\r\nNo nGraph support will be enabled for <\/span>TensorFlow.\r\n\r\nDo you wish to build TensorFlow with OpenCL SYCL support? [<\/span>y\/N]: (<\/span>\u30ea\u30bf\u30fc\u30f3)<\/span>\r\nNo OpenCL SYCL support will be enabled for <\/span>TensorFlow.\r\n\r\nDo you wish to build TensorFlow with CUDA support? [<\/span>y\/N]: y\r\nCUDA support will be enabled for <\/span>TensorFlow.\r\n\r\nDo you wish to build TensorFlow with TensorRT support? [<\/span>y\/N]: (<\/span>\u30ea\u30bf\u30fc\u30f3)<\/span>\r\nNo TensorRT support will be enabled for <\/span>TensorFlow.\r\n\r\nPlease specify the NCCL version you want to use. If NCCL 2.2 is not installed, then <\/span>you can use version 1.3 that can be fetched automatically but it may have worse performance with mu\r\nltiple GPUs. [<\/span>Default is 2.2]: 1.3\r\n(<\/span>\u3053\u3053\u30671\u6642\u9593\u7a0b\u5ea6\u30b9\u30bf\u30c3\u30af\u3059\u308b\u30b1\u30fc\u30b9\u304c\u3042\u3063\u305f)<\/span>\r\n\r\nDo you want to use clang as CUDA compiler? [<\/span>y\/N]: (<\/span>\u30ea\u30bf\u30fc\u30f3)<\/span>\r\nnvcc will be used as CUDA compiler.\r\n\r\nPlease specify which gcc should be used by nvcc as the host compiler. [<\/span>Default is \/usr\/bin\/gcc]: (<\/span>\u30ea\u30bf\u30fc\u30f3)<\/span>\r\n\r\n\r\nDo you wish to build TensorFlow with MPI support? [<\/span>y\/N]: (<\/span>\u30ea\u30bf\u30fc\u30f3)<\/span>\r\nNo MPI support will be enabled for <\/span>TensorFlow.\r\n\r\nPlease specify optimization flags to use during compilation when bazel option \"--config=opt\"<\/span> is specified [<\/span>Default is -march<\/span>=<\/span>native]: (<\/span>\u30ea\u30bf\u30fc\u30f3)<\/span>\r\n\r\n\r\nWould you like to interactively configure .\/WORKSPACE for <\/span>Android builds? [<\/span>y\/N]: (<\/span>\u30ea\u30bf\u30fc\u30f3)<\/span>\r\nNot configuring the WORKSPACE for <\/span>Android builds.\r\n\r\nPreconfigured Bazel build configs. You can use any of the below by adding \"--config=<>\"<\/span> to your build command. See tools\/bazel.rc for <\/span>more details.\r\n --config<\/span>=<\/span>mkl # Build with MKL support.<\/span>\r\n --config<\/span>=<\/span>monolithic # Config for mostly static monolithic build.<\/span>\r\nConfiguration finished\r\n<\/code><\/pre>\n\u9996\u5148<\/h2>\n
\u4e3a\u4e86\u5c1d\u8bd5\u65e5\u76ca\u53d1\u5e03\u7684\u65b0\u578b\u53f7\uff0c\u6211\u66f4\u65b0\u4e86\u673a\u5668\u5b66\u4e60\u6846\u67b6\u5e76\u8fdb\u884c\u4e86\u5347\u7ea7\u3002\u7136\u800c\uff0c\u6b63\u5f53\u6211\u8fd9\u6837\u505a\u65f6\uff0c\u5374\u9677\u5165\u4e86\u8fd9\u6837\u4e00\u79cd\u7c7b\u4f3c\u8bc5\u5492\u822c\u7684\u72b6\u6001\u3002<\/p>\n
2018-05-02 18:32:35.789145: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1423] Adding visible gpu devices: 0, 1\r\n : (\u4e2d\u7565)\r\n2018-05-02 18:32:36.865023: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1041] Created TensorFlow device (\/job:localhost\/replica:0\/task:0\/device:GPU:0 with 10222 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:17:00.0, compute capability: 6.1)\r\n2018-05-02 18:32:36.913837: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1041] Created TensorFlow device (\/job:localhost\/replica:0\/task:0\/device:GPU:1 with 10222 MB memory) -> physical GPU (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:65:00.0, compute capability: 6.1)\r\n2018-05-02 18:32:36.914640: E tensorflow\/stream_executor\/cuda\/cuda_driver.cc:936] failed to allocate 9.98G (10719158528 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY\r\n2018-05-02 18:32:36.915037: E tensorflow\/stream_executor\/cuda\/cuda_driver.cc:936] failed to allocate 8.98G (9647242240 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY\r\n2018-05-02 18:32:36.915467: E tensorflow\/stream_executor\/cuda\/cuda_driver.cc:936] failed to allocate 8.09G (8682517504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY\r\n2018-05-02 18:32:36.915867: E tensorflow\/stream_executor\/cuda\/cuda_driver.cc:936] failed to allocate 7.28G (7814265344 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY\r\n : (\u4e2d\u7565)\r\n2018-05-02 18:32:36.925404: E tensorflow\/stream_executor\/cuda\/cuda_driver.cc:936] failed to allocate 594.44M (623316480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY\r\n2018-05-02 18:32:36.928568: E tensorflow\/stream_executor\/cuda\/cuda_driver.cc:936] failed to allocate 535.00M (560984832 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY\r\nINFO:tensorflow:Restoring parameters from model2\/model.ckpt-215411\r\nINFO:tensorflow:Restoring parameters from model2\/model.ckpt-215411\r\n : (\u4e2d\u7565)\r\n2018-05-02 18:33:06.947264: E tensorflow\/stream_executor\/cuda\/cuda_dnn.cc:403] could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR\r\n2018-05-02 18:33:06.947325: E tensorflow\/stream_executor\/cuda\/cuda_dnn.cc:370] could not destroy cudnn handle: CUDNN_STATUS_BAD_PARAM\r\n2018-05-02 18:33:06.947337: F tensorflow\/core\/kernels\/conv_ops.cc:712] Check failed: stream->parent()->GetConvolveAlgorithms( conv_parameters.ShouldIncludeWinogradNonfusedAlgo<T>(), &algorithms)\r\n<\/code><\/pre>\n\u65e0\u8bba\u505a\u4ec0\u4e48\uff0c\u90fd\u4f1a\u5f97\u5230”F”\uff0c\u7136\u540e\u4e2d\u9014\u5931\u8d25\u3002<\/p>\n
\n- \n
\u518d\u8d77\u52d5\u3057\u3066\u3082\u3001<\/ul>\n<\/li>\n<\/ul>\n <\/p>\n
\n- \n
\u300cCUDA_ERROR_OUT_OF_MEMORY\u300d\u304c\u51fa\u3066\u3057\u307e\u3046\u539f\u56e0\u306f\u672c\u6765\u5225\u306b\u3042\u308b\u3088\u3046\u3067\u3001\u305d\u306e\u65b9\u6cd5\u3092\u8a66\u3057\u3066\u3082\u3001<\/ul>\n<\/li>\n<\/ul>\n <\/p>\n
Tensorflow\u3060\u3051\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u76f4\u3057\u3066\u3082(pip)\u3001\u540c\u3058\u3088\u3046\u306bFail\u3057\u3066\u3057\u307e\u3046\u2026<\/ul>\n\u7ec8\u4e8e<\/p>\n
CUDA Toolkit\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u76f4\u3057\u3066\u6b63\u5e38\u5316<\/ul>\n\u65e2\u7136\u5982\u6b64\uff0c\u7d22\u6027<\/p>\n
W tensorflow\/core\/platform\/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.\r\n<\/code><\/pre>\n\u6839\u636e\u8fd9\u6761\u6d88\u606f\u663e\u793a\uff0c\u6211\u5df2\u7ecf\u4ece\u6e90\u4ee3\u7801\u6784\u5efa\u60a8\u7684\u673a\u5668\u4ee5\u8fbe\u5230\u6700\u4f73\u72b6\u6001\u3002<\/p>\n
\u8fd9\u7bc7\u6587\u7ae0\u662f\u4e3a\u4e86\u5bf9\u7c7b\u4f3c\u7684\u4eba\u63d0\u4f9b\u53c2\u8003\u800c\u8bb0\u5f55\u4e0b\u6765\u7684\u3002<\/p>\n
\u94fe\u63a5\u5230\u76f8\u4f3c\u524d\u4eba\u7684\u8bb0\u5f55\u3002<\/p>\n
\n- \n
\u62e1\u5f35\u547d\u4ee4\u30bb\u30c3\u30c8\u3067TensorFlow\u3092\u30b3\u30f3\u30d1\u30a4\u30eb\u3059\u308b\u65b9\u6cd5@yijyun-wang<\/ul>\n<\/li>\n<\/ul>\nPython: Keras\/TensorFlow \u306e\u5b66\u7fd2\u3092 CPU \u306e\u62e1\u5f35\u547d\u4ee4\u3067\u9ad8\u901f\u5316\u3059\u308b (Mac OS X)@CUBE SUGAR CONTAINER \u7d0420-30%\u9ad8\u901f\u5316\u3057\u305f\u305d\u3046\u3067\u3059\u3002<\/p>\n
\u8303\u56f4<\/h2>\n
\u6211\u4f1a\u8bb0\u4e0b\u4ee5\u4e0b\u4e09\u70b9\u3002<\/p>\n
\n- \n
\u5b89\u88c5CUDA 9.0\uff0ccuDNN 7.0<\/ol>\n<\/li>\n<\/ol>\n <\/p>\n
\n- \n
\u4eceTensorflow\u6e90\u7801\u8fdb\u884c\u6784\u5efa\u548c\u5b89\u88c5<\/ol>\n<\/li>\n<\/ol>\n <\/p>\n
\u6784\u5efa\u548c\u7b80\u6613\u5b89\u88c5Tensorboard<\/ol>\n\u203b Tensorflow \/ Tensorboard\u4e0d\u662f\u4e00\u4e2a\u7a33\u5b9a\u7684\u5206\u652f\uff0c\u800c\u662f\u6784\u5efa\u65f6\u7684\u6700\u65b0\u7248\u672c\u3002<\/p>\n
\u5b89\u88c5CUDA 9.0\u548ccuDNN 7.0\u3002<\/h2>\n# CUDA\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3002\r\nwget https:\/\/developer.nvidia.com\/compute\/cuda\/9.0\/Prod\/local_installers\/cuda_9.0.176_384.81_linux-run\r\nwget https:\/\/developer.nvidia.com\/compute\/cuda\/9.0\/Prod\/patches\/1\/cuda_9.0.176.1_linux-run\r\nwget https:\/\/developer.nvidia.com\/compute\/cuda\/9.0\/Prod\/patches\/2\/cuda_9.0.176.2_linux-run\r\n\r\n# NVIDIA\u306e\u30c9\u30e9\u30a4\u30d0\u30fc\u3092apt\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u3044\u308b\u5834\u5408\u3001\u4e8b\u524d\u306bpurge\u3059\u308b\u3002\r\nsudo apt purge nvidia*\r\n# X\u3092\u6b62\u3081\u308b\u3002\r\nsudo service lightdm stop\r\n# CUDA Toolkit\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3002\r\nsudo sh .\/cuda_9.0.176_384.81_linux-run\r\nsudo sh .\/cuda_9.0.176.1_linux-run\r\nsudo sh .\/cuda_9.0.176.2_linux-run\r\n\r\n\u301c\u3053\u3053\u307e\u3067\u306e\u9593\u6570\u56de\u518d\u8d77\u52d5\u304c\u5fc5\u8981\u306a\u5c40\u9762\u304c\u3042\u308a\u307e\u3057\u305f\u301c\r\n\r\n# \u6b63\u5e38\u306b\u8868\u793a\u3055\u308c\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002\r\nnvidia-smi\r\n\r\n# \u30c9\u30e9\u30a4\u30d0\u30fc\u304c\u3069\u3046\u3057\u3066\u3082\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u306a\u3044\u3068\u304d\u3001\r\nsudo add-apt-repository ppa:graphics-drivers\/ppa\r\nsudo apt update\r\nsudo apt install nvidia-384\r\n# \u4ee5\u4e0aapt\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u5f8c\u3001\r\nnvidia-smi\r\n# \u6b63\u5e38\u306a\u8868\u793a\u3092\u78ba\u8a8d\u3057\u305f\u3089\u3001\u3082\u3046\u4e00\u5ea6\u300cX\u3092\u6b62\u3081\u308b\u300d\u304b\u3089CUDA Toolkit\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3002\r\n\r\n# cuDNN\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3002\r\ntar xf cudnn-9.0-linux-x64-v7.tgz\r\nsudo mv cuda\/lib64\/libcudnn* \/usr\/local\/cuda\/lib64\r\nsudo mv cuda\/include\/cudnn.h \/usr\/local\/cuda\/include\r\nrm -fr cuda\r\n# \u3053\u3053\u306froot\u306b\u306a\u308b\u5fc5\u8981\u3042\u308a\u3002\r\n(sudo bash) root$ echo \"\/usr\/local\/cuda\/lib64\" > \/etc\/ld.so.conf.d\/cuda.conf\r\n# root\u304b\u3089\u623b\u308a\u3001\r\nsudo ldconfig\r\n\r\n# \u78ba\u8a8d -> MNIST\u304c\u6b63\u5e38\u306bGPU\u3067\u5b66\u7fd2\u3067\u304d\u305f\u3089\u554f\u984c\u306a\u3044\u3053\u3068\u304c\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002\r\ncd to\/your\/tensorflow\/tensorflow\/examples\/tutorials\/mnist\r\npython mnist_deep.py\r\n<\/code><\/pre>\n\u65b0) 2. \u4eceTensorFlow\u6e90\u7801\u8fdb\u884c\u6784\u5efa\u548c\u5b89\u88c5<\/h2>\n# bazel\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3002\r\nwget https:\/\/github.com\/bazelbuild\/bazel\/releases\/download\/0.13.0\/bazel-0.13.0-installer-linux-x86_64.sh\r\nchmod +x bazel-0.13.0-installer-linux-x86_64.sh\r\n.\/bazel-0.13.0-installer-linux-x86_64.sh --user\r\n\r\n# tensorflow\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3002\r\n.\/configure\r\n# \u4e0b\u8a18\u30e1\u30c3\u30bb\u30fc\u30b8\u3092\u53c2\u8003\u306b\u5165\u529b\u3059\u308b\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u4ee5\u5916\u3092\u5165\u529b\u3057\u305f\u7b87\u6240\u306f\u300c***\u300d\u3092\u53c2\u7167\u3057\u3066\u4e0b\u3055\u3044\u3002\r\nYou have bazel 0.13.0 installed.\r\nPlease specify the location of python. [Default is \/home\/foo\/anaconda3\/bin\/python]:\r\nFound possible Python library paths:\r\n \/home\/foo\/lab\/ext\/tensorflow\/models\/research\r\n \/home\/foo\/lab\/ext\/tensorflow\/models\/research\/slim\r\n \/home\/foo\/anaconda3\/lib\/python3.6\/site-packages\r\nPlease input the desired Python library path to use. Default is [\/home\/foo\/lab\/ext\/tensorflow\/models\/research]\r\n\/home\/foo\/anaconda3\/lib\/python3.6\/site-packages <=== *** \u4f55\u6545\u304b\u3053\u3046\u306a\u3063\u3066\u3057\u307e\u3046\u305f\u3081\r\nDo you wish to build TensorFlow with jemalloc as malloc support? [Y\/n]: \r\njemalloc as malloc support will be enabled for TensorFlow.\r\nDo you wish to build TensorFlow with Google Cloud Platform support? [Y\/n]: \r\nGoogle Cloud Platform support will be enabled for TensorFlow.\r\nDo you wish to build TensorFlow with Hadoop File System support? [Y\/n]: \r\nHadoop File System support will be enabled for TensorFlow.\r\nDo you wish to build TensorFlow with Amazon S3 File System support? [Y\/n]: n <=== *** \u660e\u3089\u304b\u306b\u4f7f\u308f\u306a\u3044\u306e\u3067\r\nNo Amazon S3 File System support will be enabled for TensorFlow.\r\nDo you wish to build TensorFlow with Apache Kafka Platform support? [Y\/n]: n <=== *** \u660e\u3089\u304b\u306b\u4f7f\u308f\u306a\u3044\u306e\u3067\r\nNo Apache Kafka Platform support will be enabled for TensorFlow.\r\nDo you wish to build TensorFlow with XLA JIT support? [y\/N]: \r\nNo XLA JIT support will be enabled for TensorFlow.\r\nDo you wish to build TensorFlow with GDR support? [y\/N]: \r\nNo GDR support will be enabled for TensorFlow.\r\nDo you wish to build TensorFlow with VERBS support? [y\/N]: \r\nNo VERBS support will be enabled for TensorFlow.\r\nDo you wish to build TensorFlow with OpenCL SYCL support? [y\/N]: \r\nNo OpenCL SYCL support will be enabled for TensorFlow.\r\nDo you wish to build TensorFlow with CUDA support? [y\/N]: y <=== *** CUDA\u3092\u4f7f\u3046\u306e\u3067\r\nCUDA support will be enabled for TensorFlow.\r\nPlease specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 9.0]: \r\nPlease specify the location where CUDA 9.0 toolkit is installed. Refer to README.md for more details. [Default is \/usr\/local\/cuda]:\r\nPlease specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]:\r\nPlease specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is \/usr\/local\/cuda]:\r\nDo you wish to build TensorFlow with TensorRT support? [y\/N]: \r\nNo TensorRT support will be enabled for TensorFlow.\r\nPlease specify the NCCL version you want to use. [Leave empty to default to NCCL 1.3]:\r\nPlease specify a list of comma-separated Cuda compute capabilities you want to build with.\r\nYou can find the compute capability of your device at: https:\/\/developer.nvidia.com\/cuda-gpus.\r\nPlease note that each additional compute capability significantly increases your build time and binary size. [Default is: 3.5,5.2]\r\nDo you want to use clang as CUDA compiler? [y\/N]: \r\nnvcc will be used as CUDA compiler.\r\nPlease specify which gcc should be used by nvcc as the host compiler. [Default is \/usr\/bin\/gcc]:\r\nDo you wish to build TensorFlow with MPI support? [y\/N]: \r\nNo MPI support will be enabled for TensorFlow.\r\nPlease specify optimization flags to use during compilation when bazel option \"--config=opt\" is specified [Default is -march=native]:\r\nWould you like to interactively configure .\/WORKSPACE for Android builds? [y\/N]: \r\nNot configuring the WORKSPACE for Android builds.\r\nPreconfigured Bazel build configs. You can use any of the below by adding \"--config=<>\" to your build command. See tools\/bazel.rc for more details.\r\n --config=mkl # Build with MKL support.\r\n --config=monolithic # Config for mostly static monolithic build.\r\nConfiguration finished\r\n\r\n# tensorflow\u3092\u30d3\u30eb\u30c9\u3001\u5ff5\u306e\u305f\u3081cuda\u3092\u6307\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002\r\nbazel build --config=opt --config=cuda \/\/tensorflow\/tools\/pip_package:build_pip_package\r\n\u301c\u4e0b\u8a18\u306f\u30d3\u30eb\u30c9\u7d42\u4e86\u6642\u306e\u69d8\u5b50\u301c\r\nStarting local Bazel server and connecting to it...\r\n......\r\n : (massive messages \u2026)\r\nTarget \/\/tensorflow\/tools\/pip_package:build_pip_package up-to-date:\r\n bazel-bin\/tensorflow\/tools\/pip_package\/build_pip_package\r\nINFO: Elapsed time: 4412.763s, Critical Path: 227.12s\r\nINFO: 10224 processes, local.\r\nINFO: Build completed successfully, 13154 total actions\r\n<\/code><\/pre>\n\u5728\u6784\u5efa\u8fc7\u7a0b\u4e2d\uff0c\u6709\u65f6\u4f1a\u51fa\u73b0\u627e\u4e0d\u5230CUDA\u5e93\u7684\u9519\u8bef\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u4ee5\u4e0b\u65b9\u6cd5\u5df2\u7ecf\u89e3\u51b3\u4e86\u8be5\u95ee\u9898\u3002\u203b\u867d\u7136\u8fd9\u4e2a\u6b65\u9aa4\u5df2\u7ecf\u5305\u542b\u5728\u4e0a\u8ff0CUDA\u5b89\u88c5\u6b65\u9aa4\u4e2d\uff0c\u4f46\u662f\u5982\u679c\u5fd8\u8bb0\u4e86\u8fd9\u4e00\u6b65\uff0c\u4f1a\u5bfc\u81f4\u9519\u8bef\u3002<\/p>\n
# \u3053\u3053\u306froot\u306b\u306a\u308b\u5fc5\u8981\u3042\u308a\u3002\r\n(sudo bash) root$ echo \"\/usr\/local\/cuda\/lib64\" > \/etc\/ld.so.conf.d\/cuda.conf\r\n# root\u304b\u3089\u623b\u308a\u3001\r\nsudo ldconfig\r\n<\/code><\/pre>\n\u5f53\u6784\u5efa\u5b8c\u6210\u540e\uff0c\u60a8\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u5305\u5e76\u4f7f\u7528pip\u8fdb\u884c\u5b89\u88c5\u3002<\/p>\n
bazel-bin\/tensorflow\/tools\/pip_package\/build_pip_package \/tmp\/tensorflow_pkg\r\npip install \/tmp\/tensorflow_pkg\/tensorflow-1.8.0rc1-cp36-cp36m-linux_x86_64.whl\r\n<\/code><\/pre>\n3. Tensorboard\u7684\u6784\u5efa\u548c\u7b80\u6613\u5b89\u88c5<\/h2>\n
\u4e5f\u8bb8\u4f60\u53ef\u4ee5\u7b80\u5355\u5730\u901a\u8fc7pip\u5b89\u88c5Tensorboard\uff0c\u4f46\u7531\u4e8e\u6211\u4eec\u5efa\u8bae\u4fdd\u6301\u7248\u672c\u4e00\u81f4\uff0c\u6240\u4ee5\u8fd9\u662f\u4e00\u4e2a\u4ece\u6e90\u4ee3\u7801\u6784\u5efa\u7684\u5907\u5fd8\u5f55\u3002<\/p>\n
git clone https:\/\/github.com\/tensorflow\/tensorboard.git\r\ncd tensorboard\/\r\nbazel build \/\/tensorboard\r\n\u301c\u4e0b\u8a18\u306f\u30d3\u30eb\u30c9\u7d42\u4e86\u6642\u306e\u69d8\u5b50\u301c\r\ntensorflow\/tensorboard$ bazel build \/\/tensorboard\r\nStarting local Bazel server and connecting to it...\r\n......................................\r\n : (log messages here)\r\nTarget \/\/tensorboard:tensorboard up-to-date:\r\n bazel-bin\/tensorboard\/tensorboard\r\nINFO: Elapsed time: 326.553s, Critical Path: 187.92s\r\nINFO: 619 processes: 456 linux-sandbox, 12 local, 151 worker.\r\nINFO: Build completed successfully, 1268 total actions\r\n<\/code><\/pre>\n\u5982\u679c\u60a8\u7684tensorflow\u6210\u529f\u8fdb\u884c\u6784\u5efa\u548c\u5b89\u88c5\uff0c\u90a3\u4e48\u8fd9\u4e2a\u6784\u5efa\u8fc7\u7a0b\u5e94\u8be5\u662f\u6ca1\u6709\u95ee\u9898\u7684\u3002\u7136\u800c\uff0c\u4e0etensorflow\u4e0d\u540c\u7684\u662f\uff0c\u4f3c\u4e4e\u6ca1\u6709\u63d0\u4f9b\u6807\u51c6\u5316\u6253\u5305\u7684\u65b9\u6cd5\u3002\u6ca1\u529e\u6cd5\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u5728\u5df2\u7ecf\u8bbe\u7f6e\u597d\u8def\u5f84\u7684\u4f4d\u7f6e\u521b\u5efa\u94fe\u63a5\u7b49\u65b9\u5f0f\u6765\u7ed5\u8fc7\u8fd9\u4e2a\u95ee\u9898\u3002<\/p>\n
ln -s `pwd`\/bazel-bin\/tensorboard\/tensorboard ~\/bin\r\nln -s `pwd`\/bazel-bin\/tensorboard\/tensorboard.runfiles ~\/bin\r\n<\/code><\/pre>\n\u7d50\u675f<\/h2>\n
\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0a\u52a8\u6001\u73af\u5883\u5728\u81ea\u5df1\u7684\u8bbe\u5907\u4e0a\u8fdb\u884c\u64cd\u4f5c\u3002<\/p>\n
\u53e6\u5916\uff0c\u5982\u679c\u8981\u5c06tensorflow\u4f7f\u7528\u7684\u68c0\u67e5\u70b9\u8f6c\u6362\u4e3a.pb\u6587\u4ef6\uff0c\u6216\u8005\u9700\u8981\u51bb\u7ed3\u5de5\u5177\u4ee5\u5728\u73b0\u573a\u4f7f\u7528\u65f6\uff0c\u5fc5\u987b\u4f7f\u7528bazel\u6765\u6784\u5efa\u5de5\u5177\u3002<\/p>\n
\u6240\u4ee5\uff0c\u5982\u679c\u7ecf\u5e38\u4f7f\u7528\uff0c\u53ef\u80fd\u6700\u597d\u81ea\u5df1\u6784\u5efa\uff0c\u4ee5\u4fbf\u505a\u597d\u76f8\u5e94\u7684\u51c6\u5907\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
2018\u5e7410\u670831\u65e5\u7684TensorFlow\u6784\u5efa\u7248\u672c\u662f\u4e07\u5723\u8282\u6700\u65b0\u7248\u7684\u3002 \u5c06\u5b89\u88c5\u7248\u672c1.11.0\u8bb0\u5f55\u4e0b\u6765\u3002 $ […]<\/p>\n","protected":false},"author":9,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-47249","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"\n
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