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WORKSPACE
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WORKSPACE
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workspace(name = "org_tensorflow")
# We must initialize hermetic python first.
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
http_archive(
name = "bazel_skylib",
sha256 = "74d544d96f4a5bb630d465ca8bbcfe231e3594e5aae57e1edbf17a6eb3ca2506",
urls = [
"https://storage.googleapis.com/mirror.tensorflow.org/github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz",
"https://github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz",
],
)
http_archive(
name = "rules_python",
sha256 = "9d04041ac92a0985e344235f5d946f71ac543f1b1565f2cdbc9a2aaee8adf55b",
strip_prefix = "rules_python-0.26.0",
url = "https://github.com/bazelbuild/rules_python/releases/download/0.26.0/rules_python-0.26.0.tar.gz",
)
load("@rules_python//python:repositories.bzl", "py_repositories")
py_repositories()
load("@rules_python//python:repositories.bzl", "python_register_toolchains")
load(
"//tensorflow/tools/toolchains/python:python_repo.bzl",
"python_repository",
)
python_repository(name = "python_version_repo")
load("@python_version_repo//:py_version.bzl", "TF_PYTHON_VERSION")
python_register_toolchains(
name = "python",
ignore_root_user_error = True,
python_version = TF_PYTHON_VERSION,
)
load("@python//:defs.bzl", "interpreter")
load("@rules_python//python:pip.bzl", "package_annotation", "pip_parse")
NUMPY_ANNOTATIONS = {
"numpy": package_annotation(
additive_build_content = """\
filegroup(
name = "includes",
srcs = glob(["site-packages/numpy/core/include/**/*.h"]),
)
cc_library(
name = "numpy_headers",
hdrs = [":includes"],
strip_include_prefix="site-packages/numpy/core/include/",
)
""",
),
}
pip_parse(
name = "pypi",
annotations = NUMPY_ANNOTATIONS,
python_interpreter_target = interpreter,
requirements = "//:requirements_lock_" + TF_PYTHON_VERSION.replace(".", "_") + ".txt",
)
load("@pypi//:requirements.bzl", "install_deps")
install_deps()
# Initialize the TensorFlow repository and all dependencies.
#
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")
tf_workspace3()
load("@//tensorflow:workspace2.bzl", "tf_workspace2")
tf_workspace2()
load("@//tensorflow:workspace1.bzl", "tf_workspace1")
tf_workspace1()
load("@//tensorflow:workspace0.bzl", "tf_workspace0")
tf_workspace0()