Tensorflow-bin

The behavior is unconfirmed because I do not have C language implementation skills. Official tutorial on Tensorflow C binding generation

Appx1. C-API build procedure Native build procedure of Tensorflow v2.0.0 C API for RaspberryPi / arm64 devices (armhf / aarch64)

Appx2. C-API Usage

$ wget https://raw.githubusercontent.com/PINTO0309/Tensorflow-bin/main/C-library/2.2.0-armhf/install-buster.sh
$ ./install-buster.sh

$ sudo apt-get install -y \
    libhdf5-dev libc-ares-dev libeigen3-dev gcc gfortran \
    libgfortran5 libatlas3-base libatlas-base-dev \
    libopenblas-dev libopenblas-base libblas-dev \
    liblapack-dev cython3 openmpi-bin libopenmpi-dev \
    libatlas-base-dev python3-dev
$ sudo pip3 install pip --upgrade
$ sudo pip3 install keras_applications==1.0.8 --no-deps
$ sudo pip3 install keras_preprocessing==1.1.0 --no-deps
$ sudo pip3 install h5py==2.9.0
$ sudo pip3 install pybind11
$ pip3 install -U --user six wheel mock
$ sudo pip3 uninstall tensorflow
$ wget "https://raw.githubusercontent.com/PINTO0309/Tensorflow-bin/master/previous_versions/download_tensorflow-1.15.0-cp37-cp37m-linux_armv7l.sh"
$ ./download_tensorflow-1.15.0-cp37-cp37m-linux_armv7l.sh
$ sudo pip3 install tensorflow-1.15.0-cp37-cp37m-linux_armv7l.whl

import argparse
import numpy as np
import time

from PIL import Image

# Tensorflow -v1.12.0
#from tensorflow.contrib.lite.python import interpreter as interpreter_wrapper

# Tensorflow v1.13.0+, v2.x.x
from tensorflow.lite.python import interpreter as interpreter_wrapper

def load_labels(filename):
  my_labels = []
  input_file = open(filename, 'r')
  for l in input_file:
    my_labels.append(l.strip())
  return my_labels
if __name__ == "__main__":
  floating_model = False
  parser = argparse.ArgumentParser()
  parser.add_argument("-i", "--image", default="/tmp/grace_hopper.bmp", \
    help="image to be classified")
  parser.add_argument("-m", "--model_file", \
    default="/tmp/mobilenet_v1_1.0_224_quant.tflite", \
    help=".tflite model to be executed")
  parser.add_argument("-l", "--label_file", default="/tmp/labels.txt", \
    help="name of file containing labels")
  parser.add_argument("--input_mean", default=127.5, help="input_mean")
  parser.add_argument("--input_std", default=127.5, \
    help="input standard deviation")
  parser.add_argument("--num_threads", default=1, help="number of threads")
  args = parser.parse_args()

  ### Tensorflow -v2.2.0
  #interpreter = interpreter_wrapper.Interpreter(model_path=args.model_file)
  ### Tensorflow v2.3.0+
  interpreter = interpreter_wrapper.Interpreter(model_path=args.model_file, num_threads=int(args.num_threads))

  interpreter.allocate_tensors()
  input_details = interpreter.get_input_details()
  output_details = interpreter.get_output_details()
  # check the type of the input tensor
  if input_details[0]['dtype'] == np.float32:
    floating_model = True
  # NxHxWxC, H:1, W:2
  height = input_details[0]['shape'][1]
  width = input_details[0]['shape'][2]
  img = Image.open(args.image)
  img = img.resize((width, height))
  # add N dim
  input_data = np.expand_dims(img, axis=0)
  if floating_model:
    input_data = (np.float32(input_data) - args.input_mean) / args.input_std

  ### Tensorflow -v2.2.0
  #interpreter.set_num_threads(int(args.num_threads))
  interpreter.set_tensor(input_details[0]['index'], input_data)

  start_time = time.time()
  interpreter.invoke()
  stop_time = time.time()

  output_data = interpreter.get_tensor(output_details[0]['index'])
  results = np.squeeze(output_data)
  top_k = results.argsort()[-5:][::-1]
  labels = load_labels(args.label_file)
  for i in top_k:
    if floating_model:
      print('{0:08.6f}'.format(float(results[i]))+":", labels[i])
    else:
      print('{0:08.6f}'.format(float(results[i]/255.0))+":", labels[i])

  print("time: ", stop_time - start_time)

============================================================

Tensorflow v1.11.0

============================================================

Python2.x - Bazel 0.17.2

$ sudo apt-get install -y openmpi-bin libopenmpi-dev libhdf5-dev

$ cd ~
$ git clone https://github.com/tensorflow/tensorflow.git
$ cd tensorflow
$ git checkout -b v1.11.0
$ ./configure

Please specify the location of python. [Default is /usr/bin/python]:


Found possible Python library paths:
  /usr/local/lib/python2.7/dist-packages
  /usr/local/lib
  /home/pi/tensorflow/tensorflow/contrib/lite/tools/make/gen/rpi_armv7l/lib
  /usr/lib/python2.7/dist-packages
  /opt/movidius/caffe/python
Please input the desired Python library path to use.  Default is [/usr/local/lib/python2.7/dist-packages]

Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: y
No jemalloc as malloc support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: n
No Google Cloud Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: n
No Hadoop File System support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Amazon AWS Platform support? [Y/n]: n
No Amazon AWS Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Apache Kafka Platform support? [Y/n]: n
No Apache Kafka Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with XLA JIT support? [y/N]: n
No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with GDR support? [y/N]: n
No GDR support will be enabled for TensorFlow.

Do you wish to build TensorFlow with VERBS support? [y/N]: n
No VERBS support will be enabled for TensorFlow.

Do you wish to build TensorFlow with nGraph support? [y/N]: n
No nGraph support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL support will be enabled for TensorFlow.

Do you wish to build TensorFlow with CUDA support? [y/N]: n
No CUDA support will be enabled for TensorFlow.

Do you wish to download a fresh release of clang? (Experimental) [y/N]: n
Clang will not be downloaded.

Do you wish to build TensorFlow with MPI support? [y/N]: n
No MPI support will be enabled for TensorFlow.

Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:


Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n

$ sudo bazel build --config opt --local_resources 1024.0,0.5,0.5 \
--copt=-mfpu=neon-vfpv4 \
--copt=-ftree-vectorize \
--copt=-funsafe-math-optimizations \
--copt=-ftree-loop-vectorize \
--copt=-fomit-frame-pointer \
--copt=-DRASPBERRY_PI \
--host_copt=-DRASPBERRY_PI \
//tensorflow/tools/pip_package:build_pip_package

$ sudo ./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
$ sudo pip2 install /tmp/tensorflow_pkg/tensorflow-1.11.0-cp27-cp27mu-linux_armv7l.whl

Python3.x- Bazel 0.17.2 + ZRAM + PythonAPI(MultiThread) Feb 23, 2019, Compilation work completed

$ sudo nano /etc/dphys-swapfile
CONF_SWAPFILE=2048
CONF_MAXSWAP=2048

$ sudo systemctl stop dphys-swapfile
$ sudo systemctl start dphys-swapfile

$ wget https://github.com/PINTO0309/Tensorflow-bin/raw/master/zram.sh
$ chmod 755 zram.sh
$ sudo mv zram.sh /etc/init.d/
$ sudo update-rc.d zram.sh defaults
$ sudo reboot

$ sudo apt-get install -y libhdf5-dev libc-ares-dev libeigen3-dev
$ sudo pip3 install keras_applications==1.0.7 --no-deps
$ sudo pip3 install keras_preprocessing==1.0.9 --no-deps
$ sudo pip3 install h5py==2.9.0
$ sudo apt-get install -y openmpi-bin libopenmpi-dev

$ cd ~
$ git clone https://github.com/tensorflow/tensorflow.git
$ cd tensorflow
$ git checkout -b v1.11.0

Modify the program with reference to the following.

import argparse
import numpy as np
import time

from PIL import Image

from tensorflow.contrib.lite.python import interpreter as interpreter_wrapper
def load_labels(filename):
  my_labels = []
  input_file = open(filename, 'r')
  for l in input_file:
    my_labels.append(l.strip())
  return my_labels
if __name__ == "__main__":
  floating_model = False
  parser = argparse.ArgumentParser()
  parser.add_argument("-i", "--image", default="/tmp/grace_hopper.bmp", \
    help="image to be classified")
  parser.add_argument("-m", "--model_file", \
    default="/tmp/mobilenet_v1_1.0_224_quant.tflite", \
    help=".tflite model to be executed")
  parser.add_argument("-l", "--label_file", default="/tmp/labels.txt", \
    help="name of file containing labels")
  parser.add_argument("--input_mean", default=127.5, help="input_mean")
  parser.add_argument("--input_std", default=127.5, \
    help="input standard deviation")
  parser.add_argument("--num_threads", default=1, help="number of threads")
  args = parser.parse_args()

  interpreter = interpreter_wrapper.Interpreter(model_path=args.model_file)
  interpreter.allocate_tensors()
  input_details = interpreter.get_input_details()
  output_details = interpreter.get_output_details()
  # check the type of the input tensor
  if input_details[0]['dtype'] == np.float32:
    floating_model = True
  # NxHxWxC, H:1, W:2
  height = input_details[0]['shape'][1]
  width = input_details[0]['shape'][2]
  img = Image.open(args.image)
  img = img.resize((width, height))
  # add N dim
  input_data = np.expand_dims(img, axis=0)
  if floating_model:
    input_data = (np.float32(input_data) - args.input_mean) / args.input_std

  interpreter.set_num_threads(int(args.num_threads))
  interpreter.set_tensor(input_details[0]['index'], input_data)

  start_time = time.time()
  interpreter.invoke()
  stop_time = time.time()

  output_data = interpreter.get_tensor(output_details[0]['index'])
  results = np.squeeze(output_data)
  top_k = results.argsort()[-5:][::-1]
  labels = load_labels(args.label_file)
  for i in top_k:
    if floating_model:
      print('{0:08.6f}'.format(float(results[i]))+":", labels[i])
    else:
      print('{0:08.6f}'.format(float(results[i]/255.0))+":", labels[i])

  print("time: ", stop_time - start_time)

#Add the following two lines to the last line

  def set_num_threads(self, i):
    return self._interpreter.SetNumThreads(i)

//Corrected the vicinity of the last line as follows

  PyObject* InterpreterWrapper::ResetVariableTensors() {
  TFLITE_PY_ENSURE_VALID_INTERPRETER();
  TFLITE_PY_CHECK(interpreter_->ResetVariableTensors());
  Py_RETURN_NONE;
}

PyObject* InterpreterWrapper::SetNumThreads(int i) {
  interpreter_->SetNumThreads(i);
  Py_RETURN_NONE;
}

}  // namespace interpreter_wrapper
}  // namespace tflite

//Modified the middle of the logic as follows

  // should be the interpreter object providing the memory.
  PyObject* tensor(PyObject* base_object, int i);

  PyObject* SetNumThreads(int i);

 private:
  // Helper function to construct an `InterpreterWrapper` object.
  // It only returns InterpreterWrapper if it can construct an `Interpreter`.

$ ./configure

Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3


Found possible Python library paths:
  /usr/local/lib
  /usr/lib/python3/dist-packages
  /usr/local/lib/python3.5/dist-packages
  /opt/movidius/caffe/python
Please input the desired Python library path to use.  Default is [/usr/local/lib] /usr/local/lib/python3.5/dist-packages

Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: y
No jemalloc as malloc support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: n
No Google Cloud Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: n
No Hadoop File System support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Amazon AWS Platform support? [Y/n]: n
No Amazon AWS Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Apache Kafka Platform support? [Y/n]: n
No Apache Kafka Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with XLA JIT support? [y/N]: n
No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with GDR support? [y/N]: n
No GDR support will be enabled for TensorFlow.

Do you wish to build TensorFlow with VERBS support? [y/N]: n
No VERBS support will be enabled for TensorFlow.

Do you wish to build TensorFlow with nGraph support? [y/N]: n
No nGraph support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL support will be enabled for TensorFlow.

Do you wish to build TensorFlow with CUDA support? [y/N]: n
No CUDA support will be enabled for TensorFlow.

Do you wish to download a fresh release of clang? (Experimental) [y/N]: n
Clang will not be downloaded.

Do you wish to build TensorFlow with MPI support? [y/N]: n
No MPI support will be enabled for TensorFlow.

Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:


Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.

Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See tools/bazel.rc for more details.
    --config=mkl            # Build with MKL support.
    --config=monolithic     # Config for mostly static monolithic build.
Configuration finished

$ sudo bazel build --config opt --local_resources 1024.0,0.5,0.5 \
--copt=-mfpu=neon-vfpv4 \
--copt=-ftree-vectorize \
--copt=-funsafe-math-optimizations \
--copt=-ftree-loop-vectorize \
--copt=-fomit-frame-pointer \
--copt=-DRASPBERRY_PI \
--host_copt=-DRASPBERRY_PI \
//tensorflow/tools/pip_package:build_pip_package

$ sudo -s
# ./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
# exit
$ sudo pip3 install /tmp/tensorflow_pkg/tensorflow-1.11.0-cp35-cp35m-linux_armv7l.whl

Python3.x + jemalloc + MPI + MultiThread [C++ Only]

Edit tensorflow/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc Line139 / Line140, Line261.

  MPIRendezvousMgr* mgr =
      reinterpret_cast<MPIRendezvousMgr*>(this->rendezvous_mgr_);
- mgr->QueueRequest(parsed.FullKey().ToString(), step_id_,
-                   std::move(request_call), rendezvous_call);
+ mgr->QueueRequest(string(parsed.FullKey()), step_id_, std::move(request_call),
+                   rendezvous_call);
}
 MPIRemoteRendezvous::~MPIRemoteRendezvous() {}


        std::function<MPISendTensorCall*()> res = std::bind(
            send_cb, status, send_args, recv_args, val, is_dead, mpi_send_call);
-       SendQueueEntry req(parsed.FullKey().ToString().c_str(), std::move(res));
+       SendQueueEntry req(string(parsed.FullKey()), std::move(res));
         this->QueueSendRequest(req);

Edit tensorflow/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h Line74

  void Init(const Rendezvous::ParsedKey& parsed, const int64 step_id,
            const bool is_dead) {
-   mRes_.set_key(parsed.FullKey().ToString());
+   mRes_.set_key(string(parsed.FullKey()));
    mRes_.set_step_id(step_id);
    mRes_.mutable_response()->set_is_dead(is_dead);
    mRes_.mutable_response()->set_send_start_micros(

Edit tensorflow/tensorflow/contrib/lite/interpreter.cc Line127.

-  context_.recommended_num_threads = -1;
+  context_.recommended_num_threads = 4;

$ sudo apt-get install -y libhdf5-dev
$ sudo pip3 install keras_applications==1.0.4 --no-deps
$ sudo pip3 install keras_preprocessing==1.0.2 --no-deps
$ sudo pip3 install h5py==2.8.0
$ sudo apt-get install -y openmpi-bin libopenmpi-dev

$ cd ~
$ git clone https://github.com/tensorflow/tensorflow.git
$ cd tensorflow
$ git checkout -b v1.11.0
$ ./configure

Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3


Found possible Python library paths:
  /usr/local/lib
  /usr/lib/python3/dist-packages
  /usr/local/lib/python3.5/dist-packages
  /opt/movidius/caffe/python
Please input the desired Python library path to use.  Default is [/usr/local/lib] /usr/local/lib/python3.5/dist-packages

Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: y
jemalloc as malloc support will be enabled for Tensorflow.

Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: n
No Google Cloud Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: n
No Hadoop File System support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Amazon AWS Platform support? [Y/n]: n
No Amazon AWS Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Apache Kafka Platform support? [Y/n]: n
No Apache Kafka Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with XLA JIT support? [y/N]: n
No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with GDR support? [y/N]: n
No GDR support will be enabled for TensorFlow.

Do you wish to build TensorFlow with VERBS support? [y/N]: n
No VERBS support will be enabled for TensorFlow.

Do you wish to build TensorFlow with nGraph support? [y/N]: n
No nGraph support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL support will be enabled for TensorFlow.

Do you wish to build TensorFlow with CUDA support? [y/N]: n
No CUDA support will be enabled for TensorFlow.

Do you wish to download a fresh release of clang? (Experimental) [y/N]: n
Clang will not be downloaded.

Do you wish to build TensorFlow with MPI support? [y/N]: y
MPI support will be enabled for Tensorflow.

Please specify the MPI toolkit folder. [Default is /usr]: /usr/lib/arm-linux-gnueabihf/openmpi

Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:


Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.

Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See tools/bazel.rc for more details.
    --config=mkl            # Build with MKL support.
    --config=monolithic     # Config for mostly static monolithic build.
Configuration finished

$ sudo bazel build --config opt --local_resources 1024.0,0.5,0.5 \
--copt=-mfpu=neon-vfpv4 \
--copt=-ftree-vectorize \
--copt=-funsafe-math-optimizations \
--copt=-ftree-loop-vectorize \
--copt=-fomit-frame-pointer \
--copt=-DRASPBERRY_PI \
--host_copt=-DRASPBERRY_PI \
//tensorflow/tools/pip_package:build_pip_package

Python3.x + jemalloc + XLA JIT (Build impossible)

$ sudo apt-get install -y libhdf5-dev
$ sudo pip3 install keras_applications==1.0.4 --no-deps
$ sudo pip3 install keras_preprocessing==1.0.2 --no-deps
$ sudo pip3 install h5py==2.8.0
$ sudo apt-get install -y openmpi-bin libopenmpi-dev
$ JAVA_OPTIONS=-Xmx256M

$ cd ~
$ git clone https://github.com/tensorflow/tensorflow.git
$ cd tensorflow
$ git checkout -b v1.11.0
$ ./configure

Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3


Found possible Python library paths:
  /usr/local/lib
  /usr/lib/python3/dist-packages
  /usr/local/lib/python3.5/dist-packages
  /opt/movidius/caffe/python
Please input the desired Python library path to use.  Default is [/usr/local/lib] /usr/local/lib/python3.5/dist-packages

Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: y
jemalloc as malloc support will be enabled for Tensorflow.

Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: n
No Google Cloud Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: n
No Hadoop File System support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Amazon AWS Platform support? [Y/n]: n
No Amazon AWS Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Apache Kafka Platform support? [Y/n]: n
No Apache Kafka Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with XLA JIT support? [y/N]: y
No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with GDR support? [y/N]: n
No GDR support will be enabled for TensorFlow.

Do you wish to build TensorFlow with VERBS support? [y/N]: n
No VERBS support will be enabled for TensorFlow.

Do you wish to build TensorFlow with nGraph support? [y/N]: n
No nGraph support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL support will be enabled for TensorFlow.

Do you wish to build TensorFlow with CUDA support? [y/N]: n
No CUDA support will be enabled for TensorFlow.

Do you wish to download a fresh release of clang? (Experimental) [y/N]: n
Clang will not be downloaded.

Do you wish to build TensorFlow with MPI support? [y/N]: n
MPI support will be enabled for Tensorflow.

Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:


Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.

Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See tools/bazel.rc for more details.
    --config=mkl            # Build with MKL support.
    --config=monolithic     # Config for mostly static monolithic build.
Configuration finished

$ sudo bazel build --config opt --local_resources 1024.0,0.5,0.5 \
--copt=-mfpu=neon-vfpv4 \
--copt=-ftree-vectorize \
--copt=-funsafe-math-optimizations \
--copt=-ftree-loop-vectorize \
--copt=-fomit-frame-pointer \
--copt=-DRASPBERRY_PI \
--host_copt=-DRASPBERRY_PI \
//tensorflow/tools/pip_package:build_pip_package

Python3.x + TX2 aarch64 - Bazel 0.18.1 (JetPack-L4T-3.3-linux-x64_b39)

- L4T R28.2.1（TX2 / TX2i）
- L4T R28.2（TX1）
- CUDA 9.0
- cuDNN 7.1.5
- TensorRT 4.0
- VisionWorks 1.6

https://github.com/tensorflow/tensorflow/issues/21574#issuecomment-429758923 https://github.com/tensorflow/serving/issues/832 https://docs.nvidia.com/deeplearning/sdk/nccl-archived/nccl_2213/nccl-install-guide/index.html

build --action_env PYTHON_BIN_PATH="/usr/bin/python3"
build --action_env PYTHON_LIB_PATH="/usr/local/lib/python3.5/dist-packages"
build --python_path="/usr/bin/python3"
build --define with_jemalloc=true
build:gcp --define with_gcp_support=true
build:hdfs --define with_hdfs_support=true
build:aws --define with_aws_support=true
build:kafka --define with_kafka_support=true
build:xla --define with_xla_support=true
build:gdr --define with_gdr_support=true
build:verbs --define with_verbs_support=true
build:ngraph --define with_ngraph_support=true
build --action_env TF_NEED_OPENCL_SYCL="0"
build --action_env TF_NEED_CUDA="1"
build --action_env CUDA_TOOLKIT_PATH="/usr/local/cuda-9.0"
build --action_env TF_CUDA_VERSION="9.0"
build --action_env CUDNN_INSTALL_PATH="/usr/lib/aarch64-linux-gnu"
build --action_env TF_CUDNN_VERSION="7"
build --action_env NCCL_INSTALL_PATH="/usr/local"
build --action_env TF_NCCL_VERSION="2"
build --action_env TF_CUDA_COMPUTE_CAPABILITIES="3.5,7.0"
build --action_env LD_LIBRARY_PATH="/usr/local/cuda-9.0/lib64:../src/.libs"
build --action_env TF_CUDA_CLANG="0"
build --action_env GCC_HOST_COMPILER_PATH="/usr/bin/gcc"
build --config=cuda
test --config=cuda
build --define grpc_no_ares=true
build:opt --copt=-march=native
build:opt --host_copt=-march=native
build:opt --define with_default_optimizations=true

$ sudo apt-get install -y libhdf5-dev
$ sudo pip3 install keras_applications==1.0.4 --no-deps
$ sudo pip3 install keras_preprocessing==1.0.2 --no-deps
$ sudo pip3 install h5py==2.8.0
$ sudo apt-get install -y openmpi-bin libopenmpi-dev
$ bazel build -c opt --config=cuda --local_resources 3072.0,4.0,1.0 --verbose_failures //tensorflow/tools/pip_package:build_pip_package

============================================================

Tensorflow v1.12.0 - Bazel 0.18.1

============================================================

Python3.x (Nov 15, 2018 Under construction)

• tensorflow/BUILD

config_setting(
    name = "no_aws_support",
    define_values = {"no_aws_support": "false"},
    visibility = ["//visibility:public"],
)

config_setting(
    name = "no_gcp_support",
    define_values = {"no_gcp_support": "false"},
    visibility = ["//visibility:public"],
)

config_setting(
    name = "no_hdfs_support",
    define_values = {"no_hdfs_support": "false"},
    visibility = ["//visibility:public"],
)

config_setting(
    name = "no_ignite_support",
    define_values = {"no_ignite_support": "false"},
    visibility = ["//visibility:public"],
)

config_setting(
    name = "no_kafka_support",
    define_values = {"no_kafka_support": "false"},
    visibility = ["//visibility:public"],
)

• bazel.rc

# Options to disable default on features
build:noaws --define=no_aws_support=true
build:nogcp --define=no_gcp_support=true
build:nohdfs --define=no_hdfs_support=true
build:nokafka --define=no_kafka_support=true
build:noignite --define=no_ignite_support=true

• configure.py

  #set_build_var(environ_cp, 'TF_NEED_IGNITE', 'Apache Ignite',
  #              'with_ignite_support', True, 'ignite')


  ## On Windows, we don't have MKL support and the build is always monolithic.
  ## So no need to print the following message.
  ## TODO(pcloudy): remove the following if check when they make sense on Windows
  #if not is_windows():
  #  print('Preconfigured Bazel build configs. You can use any of the below by '
  #        'adding "--config=<>" to your build command. See tools/bazel.rc for '
  #        'more details.')
  #  config_info_line('mkl', 'Build with MKL support.')
  #  config_info_line('monolithic', 'Config for mostly static monolithic build.')
  #  config_info_line('gdr', 'Build with GDR support.')
  #  config_info_line('verbs', 'Build with libverbs support.')
  #  config_info_line('ngraph', 'Build with Intel nGraph support.')
  print('Preconfigured Bazel build configs. You can use any of the below by '
        'adding "--config=<>" to your build command. See .bazelrc for more '
        'details.')
  config_info_line('mkl', 'Build with MKL support.')
  config_info_line('monolithic', 'Config for mostly static monolithic build.')
  config_info_line('gdr', 'Build with GDR support.')
  config_info_line('verbs', 'Build with libverbs support.')
  config_info_line('ngraph', 'Build with Intel nGraph support.')

  print('Preconfigured Bazel build configs to DISABLE default on features:')
  config_info_line('noaws', 'Disable AWS S3 filesystem support.')
  config_info_line('nogcp', 'Disable GCP support.')
  config_info_line('nohdfs', 'Disable HDFS support.')
  config_info_line('noignite', 'Disable Apacha Ignite support.')
  config_info_line('nokafka', 'Disable Apache Kafka support.')

# Description:
#   contains parts of TensorFlow that are experimental or unstable and which are not supported.

licenses(["notice"])  # Apache 2.0

package(default_visibility = ["//tensorflow:__subpackages__"])

load("//third_party/mpi:mpi.bzl", "if_mpi")
load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda")
load("//tensorflow:tensorflow.bzl", "if_not_windows")
load("//tensorflow:tensorflow.bzl", "if_not_windows_cuda")

py_library(
    name = "contrib_py",
    srcs = glob(
        ["**/*.py"],
        exclude = [
            "**/*_test.py",
        ],
    ),
    srcs_version = "PY2AND3",
    visibility = ["//visibility:public"],
    deps = [
        "//tensorflow/contrib/all_reduce",
        "//tensorflow/contrib/batching:batch_py",
        "//tensorflow/contrib/bayesflow:bayesflow_py",
        "//tensorflow/contrib/boosted_trees:init_py",
        "//tensorflow/contrib/checkpoint/python:checkpoint",
        "//tensorflow/contrib/cluster_resolver:cluster_resolver_py",
        "//tensorflow/contrib/coder:coder_py",
        "//tensorflow/contrib/compiler:compiler_py",
        "//tensorflow/contrib/compiler:xla",
        "//tensorflow/contrib/autograph",
        "//tensorflow/contrib/constrained_optimization",
        "//tensorflow/contrib/copy_graph:copy_graph_py",
        "//tensorflow/contrib/crf:crf_py",
        "//tensorflow/contrib/cudnn_rnn:cudnn_rnn_py",
        "//tensorflow/contrib/data",
        "//tensorflow/contrib/deprecated:deprecated_py",
        "//tensorflow/contrib/distribute:distribute",
        "//tensorflow/contrib/distributions:distributions_py",
        "//tensorflow/contrib/eager/python:tfe",
        "//tensorflow/contrib/estimator:estimator_py",
        "//tensorflow/contrib/factorization:factorization_py",
        "//tensorflow/contrib/feature_column:feature_column_py",
        "//tensorflow/contrib/framework:framework_py",
        "//tensorflow/contrib/gan",
        "//tensorflow/contrib/graph_editor:graph_editor_py",
        "//tensorflow/contrib/grid_rnn:grid_rnn_py",
        "//tensorflow/contrib/hadoop",
        "//tensorflow/contrib/hooks",
        "//tensorflow/contrib/image:distort_image_py",
        "//tensorflow/contrib/image:image_py",
        "//tensorflow/contrib/image:single_image_random_dot_stereograms_py",
        "//tensorflow/contrib/input_pipeline:input_pipeline_py",
        "//tensorflow/contrib/integrate:integrate_py",
        "//tensorflow/contrib/keras",
        "//tensorflow/contrib/kernel_methods",
        "//tensorflow/contrib/labeled_tensor",
        "//tensorflow/contrib/layers:layers_py",
        "//tensorflow/contrib/learn",
        "//tensorflow/contrib/legacy_seq2seq:seq2seq_py",
        "//tensorflow/contrib/libsvm",
        "//tensorflow/contrib/linear_optimizer:sdca_estimator_py",
        "//tensorflow/contrib/linear_optimizer:sdca_ops_py",
        "//tensorflow/contrib/lite/python:lite",
        "//tensorflow/contrib/lookup:lookup_py",
        "//tensorflow/contrib/losses:losses_py",
        "//tensorflow/contrib/losses:metric_learning_py",
        "//tensorflow/contrib/memory_stats:memory_stats_py",
        "//tensorflow/contrib/meta_graph_transform",
        "//tensorflow/contrib/metrics:metrics_py",
        "//tensorflow/contrib/mixed_precision:mixed_precision",
        "//tensorflow/contrib/model_pruning",
        "//tensorflow/contrib/nccl:nccl_py",
        "//tensorflow/contrib/nearest_neighbor:nearest_neighbor_py",
        "//tensorflow/contrib/nn:nn_py",
        "//tensorflow/contrib/opt:opt_py",
        "//tensorflow/contrib/optimizer_v2:optimizer_v2_py",
        "//tensorflow/contrib/periodic_resample:init_py",
        "//tensorflow/contrib/predictor",
        "//tensorflow/contrib/proto",
        "//tensorflow/contrib/quantization:quantization_py",
        "//tensorflow/contrib/quantize:quantize_graph",
        "//tensorflow/contrib/receptive_field:receptive_field_py",
        "//tensorflow/contrib/recurrent:recurrent_py",
        "//tensorflow/contrib/reduce_slice_ops:reduce_slice_ops_py",
        "//tensorflow/contrib/remote_fused_graph/pylib:remote_fused_graph_ops_py",
        "//tensorflow/contrib/resampler:resampler_py",
        "//tensorflow/contrib/rnn:rnn_py",
        "//tensorflow/contrib/rpc",
        "//tensorflow/contrib/saved_model:saved_model_py",
        "//tensorflow/contrib/seq2seq:seq2seq_py",
        "//tensorflow/contrib/signal:signal_py",
        "//tensorflow/contrib/slim",
        "//tensorflow/contrib/slim:nets",
        "//tensorflow/contrib/solvers:solvers_py",
        "//tensorflow/contrib/sparsemax:sparsemax_py",
        "//tensorflow/contrib/specs",
        "//tensorflow/contrib/staging",
        "//tensorflow/contrib/stat_summarizer:stat_summarizer_py",
        "//tensorflow/contrib/stateless",
        "//tensorflow/contrib/summary:summary",
        "//tensorflow/contrib/tensor_forest:init_py",
        "//tensorflow/contrib/tensorboard",
        "//tensorflow/contrib/testing:testing_py",
        "//tenso

提示：代码过长已自动截断

• tensorflow/core/platform/default/build_config.bzl

# Platform-specific build configurations.

load("@protobuf_archive//:protobuf.bzl", "proto_gen")
load("//tensorflow:tensorflow.bzl", "if_not_mobile")
load("//tensorflow:tensorflow.bzl", "if_windows")
load("//tensorflow:tensorflow.bzl", "if_not_windows")
load("//tensorflow/core:platform/default/build_config_root.bzl", "if_static")
load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda")
load(
    "//third_party/mkl:build_defs.bzl",
    "if_mkl_ml",
)

# Appends a suffix to a list of deps.
def tf_deps(deps, suffix):
    tf_deps = []

    # If the package name is in shorthand form (ie: does not contain a ':'),
    # expand it to the full name.
    for dep in deps:
        tf_dep = dep

        if not ":" in dep:
            dep_pieces = dep.split("/")
            tf_dep += ":" + dep_pieces[len(dep_pieces) - 1]

        tf_deps += [tf_dep + suffix]

    return tf_deps

# Modified from @cython//:Tools/rules.bzl
def pyx_library(
        name,
        deps = [],
        py_deps = [],
        srcs = [],
        **kwargs):
    """Compiles a group of .pyx / .pxd / .py files.

    First runs Cython to create .cpp files for each input .pyx or .py + .pxd
    pair. Then builds a shared object for each, passing "deps" to each cc_binary
    rule (includes Python headers by default). Finally, creates a py_library rule
    with the shared objects and any pure Python "srcs", with py_deps as its
    dependencies; the shared objects can be imported like normal Python files.

    Args:
      name: Name for the rule.
      deps: C/C++ dependencies of the Cython (e.g. Numpy headers).
      py_deps: Pure Python dependencies of the final library.
      srcs: .py, .pyx, or .pxd files to either compile or pass through.
      **kwargs: Extra keyword arguments passed to the py_library.
    """

    # First filter out files that should be run compiled vs. passed through.
    py_srcs = []
    pyx_srcs = []
    pxd_srcs = []
    for src in srcs:
        if src.endswith(".pyx") or (src.endswith(".py") and
                                    src[:-3] + ".pxd" in srcs):
            pyx_srcs.append(src)
        elif src.endswith(".py"):
            py_srcs.append(src)
        else:
            pxd_srcs.append(src)
        if src.endswith("__init__.py"):
            pxd_srcs.append(src)

    # Invoke cython to produce the shared object libraries.
    for filename in pyx_srcs:
        native.genrule(
            name = filename + "_cython_translation",
            srcs = [filename],
            outs = [filename.split(".")[0] + ".cpp"],
            # Optionally use PYTHON_BIN_PATH on Linux platforms so that python 3
            # works. Windows has issues with cython_binary so skip PYTHON_BIN_PATH.
            cmd = "PYTHONHASHSEED=0 $(location @cython//:cython_binary) --cplus $(SRCS) --output-file $(OUTS)",
            tools = ["@cython//:cython_binary"] + pxd_srcs,
        )

    shared_objects = []
    for src in pyx_srcs:
        stem = src.split(".")[0]
        shared_object_name = stem + ".so"
        native.cc_binary(
            name = shared_object_name,
            srcs = [stem + ".cpp"],
            deps = deps + ["//third_party/python_runtime:headers"],
            linkshared = 1,
        )
        shared_objects.append(shared_object_name)

    # Now create a py_library with these shared objects as data.
    native.py_library(
        name = name,
        srcs = py_srcs,
        deps = py_deps,
        srcs_version = "PY2AND3",
        data = shared_objects,
        **kwargs
    )

def _proto_cc_hdrs(srcs, use_grpc_plugin = False):
    ret = [s[:-len(".proto")] + ".pb.h" for s in srcs]
    if use_grpc_plugin:
        ret += [s[:-len(".proto")] + ".grpc.pb.h" for s in srcs]
    return ret

def _proto_cc_srcs(srcs, use_grpc_plugin = False):
    ret = [s[:-len(".proto")] + ".pb.cc" for s in srcs]
    if use_grpc_plugin:
        ret += [s[:-len(".proto")] + ".grpc.pb.cc" for s in srcs]
    return ret

def _proto_py_outs(srcs, use_grpc_plugin = False):
    ret = [s[:-len(".proto")] + "_pb2.py" for s in srcs]
    if use_grpc_plugin:
        ret += [s[:-len(".proto")] + "_pb2_grpc.py" for s in srcs]
    return ret

# Re-defined protocol buffer rule to allow building "header only" protocol
# buffers, to avoid duplicate registrations. Also allows non-iterable cc_libs
# containing select() statements.
def cc_proto_library(
        name,
        srcs = [],
        deps = [],
        cc_libs = [],
        include = None,
        protoc = "@protobuf_archive//:protoc",
        internal_bootstrap_hack = False,
        use_grpc_plugin = False,
        use_grpc_namespace = False,
        default_header = False,
        **kargs):
    """Bazel rule to create a C++ protobuf library from proto source files.

    Args:
      name: the name of the cc_proto_library.
      srcs: the .proto files of the cc_proto_library.
      deps: a list of dependency labels; must be cc_proto_library.
      cc_libs: a list of other cc_library targets depended by the generated
          cc_library.
      include: a string indicating the include path of the .proto files.
      protoc: the label of the protocol compiler to generate the sources.
      internal_bootstrap_hack: a flag indicate the cc_proto_library is used only
          for bootstraping. When it is set to True, no files will be generated.
          The rule will simply be a provider for .proto files, so that other
          cc_proto_library can depend on it.
      use_grpc_plugin: a flag to indicate whether to call the grpc C++ plugin
          when processing the proto files.
      default_header: Controls the naming of generated rules. If True, the `name`
          rule will be header-only, and an _impl rule will contain the
          implementation. Otherwise the header-only rule (name + "_headers_only")
          must be referred to explicitly.
      **kargs: other keyword arguments that are passed to cc_library.
    """

    includes = []
    if include != None:
        includes = [include]

    if internal_bootstrap_hack:
        # For pre-checked-in generated files, we add the internal_bootstrap_hack
        # which will skip the codegen action.
        proto_gen(
            name = name + "_genproto",
            srcs = srcs,
            includes = includes,
            protoc = protoc,
            visibility = ["//visibility:public"],
            deps = [s + "_genproto" for s in deps],
        )

        # An empty cc_library to make rule dependency consistent.
        native.cc_library(
            name = name,
            **kargs
        )
        return

    grpc_cpp_plugin = None
    plugin_options = []
    if use_grpc_plugin:
        grpc_cpp_plugin = "//external:grpc_cpp_plugin"
        if use_grpc_namespace:
            plugin_options = ["services_namespace=grpc"]

    gen_srcs = _proto_cc_srcs(srcs, use_grpc_plugin)
    gen_hdrs = _proto_cc_hdrs(srcs, use_grpc_plugin)
    outs = gen_srcs + gen_hdrs

    proto_gen(
        name = name + "_genproto",
        srcs = srcs,
        outs = outs,
        gen_cc = 1,
        includes = includes,
        plugin = grpc_cpp_plugin,
        plugin_language = "grpc",
        plugin_options = plugin_options,
        protoc = protoc,
        visibility = ["//visibility:public"],
        deps = [s + "_genproto" for s in deps],
    )

    if use_grpc_plugin:
        cc_libs += select({
            "//tensorflow:linux_s390x": ["//external:grpc_lib_unsecure"],
            "//conditions:default": ["//external:grpc_lib"],
        })

    if default_header:
        header_only_name = name
        impl_name = name + "_impl"
    else:
        header_only_name = name + "_headers_only"
        impl_name = name

    native.cc_library(
        name = impl_name,
        srcs = gen_srcs,
        hdrs = gen_hdrs,
        deps = cc_libs + deps,
        includes = includes,
        **kargs
    )
    native.cc_library(
        name = header_only_name,
        deps = ["@protobuf_archive//:protobuf_headers"] + if_static([impl_name]),
        hdrs = gen_hdrs,
        **kargs
    )

# Re-defined protocol buffer rule to bring in the change introduced in commit
# https://github.com/google/protobuf/commit/294b5758c373cbab4b72f35f4cb62dc1d8332b68
# which was not part of a stable protobuf release in 04/2018.
# TODO(jsimsa): Remove this once the protobuf dependency version is updated
# to include the above commit.
def py_proto_library(
        name,
        srcs = [],
        deps = [],
        py_libs = [],
        py_extra_srcs = [],
        include = None,
        default_runtime = "@protobuf_archive//:protobuf_python",
        protoc = "@protobuf_archive//:protoc",
        use_grpc_plugin = False,
        **kargs):
    """Bazel rule to create a Python protobuf library from proto source files

    NOTE: the rule is only an internal workaround to generate protos. The
    interface may change and the rule may be removed when bazel has introduced
    the native rule.

    Args:
      name: the name of the py_proto_library.
      srcs: the .proto files of the py_proto_library.
      deps: a list of dependency labels; must be py_proto_library.
      py_libs: a list of other py_library targets depended by the generated
          py_library.
      py_extra_srcs: extra source files that will be added to the output
          py_library. This attribute is used for internal bootstrapping.
      include: a string indicating the include path of the .proto files.
      default_runtime: the implicitly default runtime which will be depended on by
          the generated py_library target.
      protoc: the label of the protocol compiler to generate the sources.
      use_grpc_plugin: a flag to indicate whether to call the Python C++ plugin
          when processing the proto files.
      **kargs: other keyword arguments that are passed to cc_library.
    """
    outs = _proto_py_outs(srcs, use_grpc_plugin)

    includes = []
    if include != None:
        includes = [include]

    grpc_python_plugin = None
    if use_grpc_plugin:
        grpc_python_plugin = "//external:grpc_python_plugin"
        # Note: Generated grpc code depends on Python grpc module. This dependency
        # is not explicitly listed in py_libs. Instead, host system is assumed to
        # have grpc installed.

    proto_gen(
        name = name + "_genproto",
        srcs = srcs,
        outs = outs,
        gen_py = 1,
        includes = includes,
        plugin = grpc_python_plugin,
        plugin_language = "grpc",
        protoc = protoc,
        visibility = ["//visibility:public"],
        deps = [s + "_genproto" for s in deps],
    )

    if default_runtime and not default_runtime in py_libs + deps:
        py_libs = py_libs + [default_runtime]

    native.py_library(
        name = name,
        srcs = outs + py_extra_srcs,
        deps = py_libs + deps,
        imports = includes,
        **kargs
    )

def tf_proto_library_cc(
        name,
        srcs = [],
        has_services = None,
        protodeps = [],
        visibility = [],
        testonly = 0,
        cc_libs = [],
        cc_stubby_versions = None,
        cc_grpc_version = None,
        j2objc_api_version = 1,
        cc_api_version = 2,
        dart_api_version = 2,
        java_api_version = 2,
        py_api_version = 2,
        js_api_version = 2,
        js_codegen = "jspb",
        default_header = False):
    js_codegen = js_codegen  # unused argument
    js_api_version = js_api_version  # unused argument
    native.filegroup(
        name = name + "_proto_srcs",
        srcs = srcs + tf_deps(protodeps, "_proto_srcs"),
        testonly = testonly,
        visibility = visibility,
    )

    use_grpc_plugin = None
    if cc_grpc_version:
        use_grpc_plugin = True

    cc_deps = tf_deps(protodeps, "_cc")
    cc_name = name + "_cc"
    if not srcs:
        # This is a collection of sub-libraries. Build header-only and impl
        # libraries containing all the sources.
        proto_gen(
            name = cc_name + "_genproto",
            protoc = "@protobuf_archive//:protoc",
            visibility = ["//visibility:public"],
            deps = [s + "_genproto" for s in cc_deps],
        )
        native.cc_library(
            name = cc_name,
            deps = cc_deps + ["@protobuf_archive//:protobuf_headers"] + if_static([name + "_cc_impl"]),
            testonly = testonly,
            visibility = visibility,
        )
        native.cc_library(
            name = cc_name + "_impl",
            deps = [s + "_impl" for s in cc_deps] + ["@protobuf_archive//:cc_wkt_protos"],
        )

        return

    cc_proto_library(
        name = cc_name,
        testonly = testonly,
        srcs = srcs,
        cc_libs = cc_libs + if_static(
            ["@protobuf_archive//:protobuf"],
            ["@protobuf_archive//:protobuf_headers"],
        ),
        copts = if_not_windows([
            "-Wno-unknown-warning-option",
            "-Wno-unused-but-set-variable",
            "-Wno-sign-compare",
        ]),
        default_header = default_header,
        protoc = "@protobuf_archive//:protoc",
        use_grpc_plugin = use_grpc_plugin,
        visibility = visibility,
        deps = cc_deps + ["@protobuf_archive//:cc_wkt_protos"],
    )

def tf_proto_library_py(
        name,
        srcs = [],
        protodeps = [],
        deps = [],
        visibility = [],
        testonly = 0,
        srcs_version = "PY2AND3",
        use_grpc_plugin = False):
    py_deps = tf_deps(protodeps, "_py")
    py_name = name + "_py"
    if not srcs:
        # This is a collection of sub-libraries. Build header-only and impl
        # libraries containing all the sources.
        proto_gen(
            name = py_name + "_genproto",
            protoc = "@protobuf_archive//:protoc",
            visibility = ["//visibility:public"],
            deps = [s + "_genproto" for s in py_deps],
        )
        native.py_library(
            name = py_name,
            deps = py_deps + ["@protobuf_archive//:protobuf_python"],
            testonly = testonly,
            visibility = visibility,
        )
        return

    py_proto_library(
        name = py_name,
        testonly = testonly,
        srcs = srcs,
        default_runtime = "@protobuf_archive//:protobuf_python",
        protoc = "@protobuf_archive//:protoc",
        srcs_version = srcs_version,
        use_grpc_plugin = use_grpc_plugin,
        visibility = visibility,
        deps = deps + py_deps + ["@protobuf_archive//:protobuf_python"],
    )

def tf_jspb_proto_library(**kwargs):
    pass

def tf_nano_proto_library(**kwargs):
    pass

def tf_proto_library(
        name,
        srcs = [],
        has_services = None,
        protodeps = [],
        visibility = [],
        testonly = 0,
        cc_libs = [],
        cc_api_version = 2,
        cc_grpc_version = None,
        dart_api_version = 2,
        j2objc_api_version = 1,
        java_api_version = 2,
        py_api_version = 2,
        js_api_version = 2,
        js_codegen = "jspb",
        provide_cc_alias = False,
        default_header = False):
    """Make a proto library, possibly depending on other proto libraries."""
    _ignore = (js_api_version, js_codegen, provide_cc_alias)

    tf_proto_library_cc(
        name = name,
        testonly = testonly,
        srcs = srcs,
        cc_grpc_version = cc_grpc_version,
        cc_libs = cc_libs,
        default_header = default_header,
        protodeps = protodeps,
        visibility = visibility,
    )

    tf_proto_library_py(
        name = name,
        testonly = testonly,
        srcs = srcs,
        protodeps = protodeps,
        srcs_version = "PY2AND3",
        use_grpc_plugin = has_services,
        visibility = visibility,
    )

# A list of all files under platform matching the pattern in 'files'. In
# contrast with 'tf_platform_srcs' below, which seletive collects files that
# must be compiled in the 'default' platform, this is a list of all headers
# mentioned in the platform/* files.
def tf_platform_hdrs(files):
    return native.glob(["platform/*/" + f for f in files])

def tf_platform_srcs(files):
    base_set = ["platform/default/" + f for f in files]
    windows_set = base_set + ["platform/windows/" + f for f in files]
    posix_set = base_set + ["platform/posix/" + f for f in files]

    # Handle cases where we must also bring the posix file in. Usually, the list
    # of files to build on windows builds is just all the stuff in the
    # windows_set. However, in some cases the implementations in 'posix/' are
    # just what is necessary and historically we choose to simply use the posix
    # file instead of making a copy in 'windows'.
    for f in files:
        if f == "error.cc":
            windows_set.append("platform/posix/" + f)

    return select({
        "//tensorflow:windows": native.glob(windows_set),
        "//conditions:default": native.glob(posix_set),
    })

def tf_additional_lib_hdrs(exclude = []):
    windows_hdrs = native.glob([
        "platform/default/*.h",
        "platform/windows/*.h",
        "platform/posix/error.h",
    ], exclude = exclude)
    return select({
        "//tensorflow:windows": windows_hdrs,
        "//conditions:default": native.glob([
            "platform/default/*.h",
            "platform/posix/*.h",
        ], exclude = exclude),
    })

def tf_additional_lib_srcs(exclude = []):
    windows_srcs = native.glob([
        "platform/default/*.cc",
        "platform/windows/*.cc",
        "platform/posix/error.cc",
    ], exclude = exclude)
    return select({
        "//tensorflow:windows": windows_srcs,
        "//conditions:default": native.glob([
            "platform/default/*.cc",
            "platform/posix/*.cc",
        ], exclude = exclude),
    })

def tf_additional_minimal_lib_srcs():
    return [
        "platform/default/integral_types.h",
        "platform/default/mutex.h",
    ]

def tf_additional_proto_hdrs():
    return [
        "platform/default/integral_types.h",
        "platform/default/logging.h",
        "platform/default/protobuf.h",
    ] + if_windows([
        "platform/windows/integral_types.h",
    ])

def tf_additional_proto_compiler_hdrs():
    return [
        "platform/default/protobuf_compiler.h",
    ]

def tf_additional_proto_srcs():
    return [
        "platform/default/protobuf.cc",
    ]

def tf_additional_human_readable_json_deps():
    return []

def tf_additional_all_protos():
    return ["//tensorflow/core:protos_all"]

def tf_protos_all_impl():
    return ["//tensorflow/core:protos_all_cc_impl"]

def tf_protos_all():
    return if_static(
        extra_deps = tf_protos_all_impl(),
        otherwise = ["//tensorflow/core:protos_all_cc"],
    )

def tf_protos_grappler_impl():
    return ["//tensorflow/core/grappler/costs:op_performance_data_cc_impl"]

def tf_protos_grappler():
    return if_static(
        extra_deps = tf_protos_grappler_impl(),
        otherwise = ["//tensorflow/core/grappler/costs:op_performance_data_cc"],
    )

def tf_additional_cupti_wrapper_deps():
    return ["//tensorflow/core/platform/default/gpu:cupti_wrapper"]

def tf_additional_device_tracer_srcs():
    return ["platform/default/device_tracer.cc"]

def tf_additional_device_tracer_cuda_deps():
    return []

def tf_additional_device_tracer_deps():
    return []

def tf_additional_libdevice_data():
    return []

def tf_additional_libdevice_deps():
    return ["@local_config_cuda//cuda:cuda_headers"]

def tf_additional_libdevice_srcs():
    return ["platform/default/cuda_libdevice_path.cc"]
</div></details>

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