2

正如标题所暗示的，我遇到了一个问题，即 proto import 语句似乎与正确的路径无关。具体来说，考虑一个 dir 中的目录结构（我们称之为 ~/base）：

`>> tree -L 1
├── models
├── my-lib
|     ├── nlp
|        ├── BUILD
|        └── nlp_parser.cc
|     └── WORKSPACE
├── serving
└── tensorflow

对于那些不熟悉的人，models（如在https://github.com/tensorflow/models/中）将 tensorflow（https://github.com/tensorflow/tensorflow）作为 git 子模块，就像serving. 由于这一点，再加上 tensorflow 的 git 子模块在不同的提交上并且有时不兼容，我已经从项目中删除了 gitsubmodule 并将它们符号链接到最顶层目录上的 tensorflow 存储库，以便我只能管理一个张量流回购而不是3。那是我做了以下事情： 

`cd models/syntaxnet; rm -rf tensorflow; ln -s ../../tensorflow/ .; cd -`

`cd serving; rm -rf tensorflow tf_models; ln -s ../tensorflow/ .; ln -s ../models .`

现在我想在其中构建一个my-lib依赖于serving、tensorflow和的目标models。我将这些作为本地存储库添加到我的 WORKSPACE 中，如下所示（cat my-lib/WORKSPACE）：

workspace(name = "myworkspace")

local_repository(
  name = "org_tensorflow",
  path = __workspace_dir__ + "/../tensorflow",
)

local_repository(
  name = "syntaxnet",
  path = __workspace_dir__ + "/../models/syntaxnet",
)

local_repository(
  name = "tf_serving",
  path = __workspace_dir__ + "/../serving",
)

load('@org_tensorflow//tensorflow:workspace.bzl', 'tf_workspace')
tf_workspace("~/base/tensorflow", "@org_tensorflow")

# ===== gRPC dependencies =====

bind(
    name = "libssl",
    actual = "@boringssl_git//:ssl",
)
bind(
    name = "zlib",
    actual = "@zlib_archive//:zlib",
)

这是我的构建文件（cat my-lib/nlp/BUILD）：

load("@tf_serving//tensorflow_serving:serving.bzl", "serving_proto_library")

cc_binary(
  name = "nlp_parser",
  srcs = [ "nlp_parser.cc" ],
  linkopts = ["-lm"],
  deps = [
        "@org_tensorflow//tensorflow/core:core_cpu",
        "@org_tensorflow//tensorflow/core:framework",
        "@org_tensorflow//tensorflow/core:lib",
        "@org_tensorflow//tensorflow/core:protos_all_cc",
        "@org_tensorflow//tensorflow/core:tensorflow",
        "@syntaxnet//syntaxnet:parser_ops_cc",
        "@syntaxnet//syntaxnet:sentence_proto",
        "@tf_serving//tensorflow_serving/servables/tensorflow:session_bundle_config_proto",
        "@tf_serving//tensorflow_serving/servables/tensorflow:session_bundle_factory",
        "@org_tensorflow//tensorflow/contrib/session_bundle",
        "@org_tensorflow//tensorflow/contrib/session_bundle:signature",
  ],
)

最后，这是构建 ( cd my-lib; bazel build nlp/nlp_parser --verbose_failures) 的输出：

INFO: Found 1 target...
ERROR: /home/blah/blah/external/org_tensorflow/tensorflow/core/debug/BUILD:33:1: null failed: linux-sandbox failed: error executing command 
  (cd /home/blah/blah/execroot/my-lib && \
  exec env - \
  /home/blah/blah/execroot/my-lib/_bin/linux-sandbox @/home/blah/blah/execroot/my-lib/bazel-sandbox/c65fa6b6-9b7d-4710-b19c-4d42a3e6a667-31.params -- bazel-out/host/bin/external/protobuf/protoc '--cpp_out=bazel-out/local-fastbuild/genfiles/external/org_tensorflow' '--plugin=protoc-gen-grpc=bazel-out/host/bin/external/grpc/grpc_cpp_plugin' '--grpc_out=bazel-out/local-fastbuild/genfiles/external/org_tensorflow' -Iexternal/org_tensorflow -Ibazel-out/local-fastbuild/genfiles/external/org_tensorflow -Iexternal/protobuf/src -Ibazel-out/local-fastbuild/genfiles/external/protobuf/src external/org_tensorflow/tensorflow/core/debug/debug_service.proto).
bazel-out/local-fastbuild/genfiles/external/protobuf/src: warning: directory does not exist.
tensorflow/core/util/event.proto: File not found.
tensorflow/core/debug/debug_service.proto: Import "tensorflow/core/util/event.proto" was not found or had errors.
tensorflow/core/debug/debug_service.proto:38:25: "Event" is not defined.
Target //nlp:nlp_parser failed to build
INFO: Elapsed time: 0.776s, Critical Path: 0.42s

在 WORKSPACE 中将模块添加为 local_repository 以便原型导入工作的正确方法是什么？

4

1 回答 1

2

在尝试在 Ubuntu 上构建一个依赖于 tensorflow 的项目之后，我遇到了类似的问题。在 OS X 上构建后，我遇到了类似的问题。最终对我有用的是禁用沙盒--spawn_strategy=standalone

于 2016-10-06T01:47:47.317 回答