bazel構建tensorflow與本地下載的tensorflow一起使用

Question

張量流服務構建依賴於大張量流; 但我已經成功建立了tensorflow。 所以我想用它。 我做這些事情：我更改了服務WORKSPACE的tensorflow（org： https : //github.com/tensorflow/serving/blob/master/WORKSPACE ）

workspace(name = "tf_serving")

# To update TensorFlow to a new revision.
# 1. Update the 'git_commit' args below to include the new git hash.
# 2. Get the sha256 hash of the archive with a command such as...
#    curl -L https://github.com/tensorflow/tensorflow/archive/<git hash>.tar.gz | sha256sum
#    and update the 'sha256' arg with the result.
# 3. Request the new archive to be mirrored on mirror.bazel.build for more
#    reliable downloads.
#load("//tensorflow_serving:repo.bzl", "tensorflow_http_archive")

#tensorflow_http_archive(
#    name = "org_tensorflow",
#    sha256 = "0f4b8375de30c54cc3233bc40e04742dab0ffe007acf8391651c6adb62be89f8",
#    git_commit = "2ea398b12ed18b6c51e09f363021c6aa306c5179",
#)

local_repository(
    name = "org_tensorflow",
    path = "/vagrant/tf/tensorflow/",
)


# TensorFlow depends on "io_bazel_rules_closure" so we need this here.
# Needs to be kept in sync with the same target in TensorFlow's WORKSPACE file.
http_archive(
    name = "io_bazel_rules_closure",
    sha256 = "a38539c5b5c358548e75b44141b4ab637bba7c4dc02b46b1f62a96d6433f56ae",
    strip_prefix = "rules_closure-dbb96841cc0a5fb2664c37822803b06dab20c7d1",
    urls = [
        "https://mirror.bazel.build/github.com/bazelbuild/rules_closure/archive/dbb96841cc0a5fb2664c37822803b06dab20c7d1.tar.gz",
        "https://github.com/bazelbuild/rules_closure/archive/dbb96841cc0a5fb2664c37822803b06dab20c7d1.tar.gz",  # 2018-04-13
    ],
)

# Please add all new TensorFlow Serving dependencies in workspace.bzl.
load("//tensorflow_serving:workspace.bzl", "tf_serving_workspace")

tf_serving_workspace()

# Specify the minimum required bazel version.
load("@org_tensorflow//tensorflow:version_check.bzl", "check_bazel_version_at_least")

check_bazel_version_at_least("0.15.0")

但是我用以下命令錯誤構建：

[root@localhost serving]# tools/bazel_in_docker.sh bazel build --config=nativeopt tensorflow_serving/...
== Pulling docker image: tensorflow/serving:nightly-devel
Trying to pull repository docker.io/tensorflow/serving ...
nightly-devel: Pulling from docker.io/tensorflow/serving
Digest: sha256:f500ae4ab367cbabfd474487175bb357d73c01466a80c699db90ba3f0ba7b5a8
Status: Image is up to date for docker.io/tensorflow/serving:nightly-devel
== Running cmd: sh -c 'cd /root/serving; TEST_TMPDIR=.cache bazel build --config=nativeopt tensorflow_serving/...'
usermod: no changes
$TEST_TMPDIR defined: output root default is '/root/serving/.cache' and max_idle_secs default is '15'.
Starting local Bazel server and connecting to it...
.............
ERROR: error loading package '': Encountered error while reading extension file 'tensorflow/workspace.bzl': no such package '@org_tensorflow//tensorflow': /root/serving/.cache/_bazel_root/01a289b7faaf5ec651fb0e4e35f862a1/external/org_tensorflow must be an existing directory
ERROR: error loading package '': Encountered error while reading extension file 'tensorflow/workspace.bzl': no such package '@org_tensorflow//tensorflow': /root/serving/.cache/_bazel_root/01a289b7faaf5ec651fb0e4e35f862a1/external/org_tensorflow must be an existing directory
INFO: Elapsed time: 0.460s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (0 packages loaded)

buil如何成功地與本地tensorflow一起服務？ 謝謝！

Answer 1

您應該提高docker build資源CPU和內存。 我在筆記本電腦上將4 vcpu和4 Gig ram升級到docker，但是在構建tensorflow服務映像時需要使用此選項將Bazzel C編譯器的最大內存限制為2048 Meg

https://www.tensorflow.org/serving/docker

 docker build --pull --build-arg TF_SERVING_BUILD_OPTIONS="--copt=-mavx \\ --cxxopt=-D_GLIBCXX_USE_CXX11_ABI=0 --local_resources 2048,.5,1.0" -t \\ $USER/tensorflow-serving-devel -f Dockerfile.devel . 

還需要將Bazel的版本升級到20，才能進行構建。 在您的docker文件中

設置Bazel增強器版本20傾倒編譯器TensorFlow

 Set up Bazel augmenter version 20 pour compiler tensorflow # Need >= 0.15.0 so bazel compiles work with docker bind mounts. ENV BAZEL_VERSION 0.20.0 WORKDIR / RUN mkdir /bazel && \\ cd /bazel && \\ curl -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36" -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \\ curl -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36" -fSsL -o /bazel/LICENSE.txt https://raw.githubusercontent.com/bazelbuild/bazel/master/LICENSE && \\ chmod +x bazel-*.sh && \\ ./bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \\ cd / && \\ rm -f /bazel/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh 

這里是名為dockerbuild.txt的整個docker文件和docker build命令

docker build --pull --build-arg TF_SERVING_BUILD_OPTIONS =“-copt = -mavx --cxxopt = -D_GLIBCXX_USE_CXX11_ABI = 0 --local_resources 2048，.5,1.0” -f dockerbuild.txt。

 # Copyright 2018 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. FROM ubuntu:18.04 as base_build ARG TF_SERVING_VERSION_GIT_BRANCH=master ARG TF_SERVING_VERSION_GIT_COMMIT=head LABEL maintainer=gvasudevan@google.com LABEL tensorflow_serving_github_branchtag=${TF_SERVING_VERSION_GIT_BRANCH} LABEL tensorflow_serving_github_commit=${TF_SERVING_VERSION_GIT_COMMIT} RUN apt-get update && apt-get install -y --no-install-recommends \\ automake \\ build-essential \\ ca-certificates \\ curl \\ git \\ libcurl3-dev \\ libfreetype6-dev \\ libpng-dev \\ libtool \\ libzmq3-dev \\ mlocate \\ openjdk-8-jdk\\ openjdk-8-jre-headless \\ pkg-config \\ python-dev \\ software-properties-common \\ swig \\ unzip \\ wget \\ zip \\ zlib1g-dev \\ && \\ apt-get clean && \\ rm -rf /var/lib/apt/lists/* RUN curl -fSsL -O https://bootstrap.pypa.io/get-pip.py && \\ python get-pip.py && \\ rm get-pip.py RUN pip --no-cache-dir install \\ grpcio \\ h5py \\ keras_applications \\ keras_preprocessing \\ mock \\ numpy \\ six \\ Pillow \\ matplotlib \\ opencv-python \\ pandas \\ requests # Set up Bazel augmenter version 20 pour compiler tensorflow # Need >= 0.15.0 so bazel compiles work with docker bind mounts. ENV BAZEL_VERSION 0.20.0 WORKDIR / RUN mkdir /bazel && \\ cd /bazel && \\ curl -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36" -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \\ curl -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36" -fSsL -o /bazel/LICENSE.txt https://raw.githubusercontent.com/bazelbuild/bazel/master/LICENSE && \\ chmod +x bazel-*.sh && \\ ./bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \\ cd / && \\ rm -f /bazel/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh # Download TF Serving sources (optionally at specific commit). WORKDIR /tensorflow-serving RUN git clone --branch=${TF_SERVING_VERSION_GIT_BRANCH} https://github.com/tensorflow/serving . && \\ git remote add upstream https://github.com/tensorflow/serving.git && \\ if [ "${TF_SERVING_VERSION_GIT_COMMIT}" != "head" ]; then git checkout ${TF_SERVING_VERSION_GIT_COMMIT} ; fi FROM base_build as binary_build # Build, and install TensorFlow Serving ARG TF_SERVING_BUILD_OPTIONS="--config=nativeopt" RUN echo "Building with build options: ${TF_SERVING_BUILD_OPTIONS}" ARG TF_SERVING_BAZEL_OPTIONS="" RUN echo "Building with Bazel options: ${TF_SERVING_BAZEL_OPTIONS}" RUN bazel build --color=yes --curses=yes \\ ${TF_SERVING_BAZEL_OPTIONS} \\ --verbose_failures \\ --output_filter=DONT_MATCH_ANYTHING \\ ${TF_SERVING_BUILD_OPTIONS} \\ tensorflow_serving/model_servers:tensorflow_model_server && \\ cp bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server \\ /usr/local/bin/ # Build and install TensorFlow Serving API RUN bazel build --color=yes --curses=yes \\ ${TF_SERVING_BAZEL_OPTIONS} \\ --verbose_failures \\ --output_filter=DONT_MATCH_ANYTHING \\ ${TF_SERVING_BUILD_OPTIONS} \\ tensorflow_serving/tools/pip_package:build_pip_package && \\ bazel-bin/tensorflow_serving/tools/pip_package/build_pip_package \\ /tmp/pip && \\ pip --no-cache-dir install --upgrade \\ /tmp/pip/tensorflow_serving_api-*.whl && \\ rm -rf /tmp/pip FROM binary_build as clean_build # Clean up Bazel cache when done. RUN bazel clean --expunge --color=yes && \\ rm -rf /root/.cache CMD ["/bin/bash"]