TensorFlow 服务构建 denpend on large tensorflow; 但我已经成功地构建了 TensorFlow。 所以我想使用它。 我做这些事情： 我更改了张量流服务工作区(组织：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")
但是我使用此命令错误进行构建：
[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)
BIIL 成功使用本地张量流服务了什么 ID？ 谢谢！
你应该提高docker build ressource CPU和内存。我在笔记本电脑上对 docker 进行了 4 vcpu 和 4 Gig ram 升级，但在构建张量流服务图像时，您需要使用此选项将 Bazzel C 编译器限制为 2048 Meg 内存
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
$USER/tensorflow-serving-devel -f Dockerfile.devel .还需要将 Bazel 的版本升级到 20 才能构建工作。 在您的 docker 文件中
设置 Bazel augmenter 版本 20 倒编译器张量流
Set up Bazel augmenter version 20 pour compiler tensorflow
# Need >= 0.15.0 so bazel compiles work with docker bind mounts.
ENV BAZEL_VERSION 0.20.0
WORKDIR /
RUN mkdir /bazel &&
cd /bazel &&
curl -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36" -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$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-$BAZEL_VERSION-installer-linux-x86_64.sh这里是名为dockerbuild的整个docker文件.txt和docker build命令
docker build --pull --build-arg TF_SERVING_BUILD_OPTIONS="--copt=-mavx --cxxopt=-D_GLIBCXX_USE_CXX11_ABI=0 --local_resources 2048，.5,1.0" -f dockerbuild.txt .
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
FROM ubuntu:18.04 as base_build
ARG TF_SERVING_VERSION_GIT_BRANCH=master
ARG TF_SERVING_VERSION_GIT_COMMIT=head
LABEL maintainer=gvasudevan@google.com
LABEL tensorflow_serving_github_branchtag=${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"]