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ci/official/wheel_test/README.md
file for testing, such as: * Ensuring the entire API is importable * Testing downstream projects against the wheel Ensure you have Bazel installed and accessible from your command line. These tests use hermetic Python. They also require a built TensorFlow wheel file and a requirements_lock file. The requirements_lock file is generated by the
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ci/official/envs/disk_cache
# See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # Sourcing this enables local disk cache if [[ $(uname -s) == "Darwin" ]]; then echo "Please note that using disk cache on macOS is not recommended because the" echo "cache can end up being pretty big and make the build process inefficient." fi
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ci/official/utilities/code_check_changed_files.bats
# 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. # ============================================================================== setup_file() { bazel version # Start the bazel server
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ci/official/envs/rbe
# 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. # ============================================================================== # Enables RBE as long as RBE is available for the selected platform. # The "selected" platform is considered to be
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# > on the host machine. If we don't have --distinct_host_configuration=false, # > the core TensorFlow code will be built once for the host and once for the # > target platform. # See also https://docs.bazel.build/versions/master/guide.html#build-configurations-and-cross-compilation build --distinct_host_configuration=false # Store performance profiling log in the mounted artifact directory.
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ci/official/envs/linux_x86_cuda
# 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. # ============================================================================== source ci/official/envs/linux_x86
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ci/official/requirements_updater/BUILD.bazel
# 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. # ============================================================================== load("@python//3.10:defs.bzl", compile_pip_requirements_3_10 = "compile_pip_requirements")
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ci/official/README.md
# Official CI Directory Maintainer: TensorFlow and TensorFlow DevInfra Issue Reporting: File an issue against this repo and tag [@devinfra](https://github.com/orgs/tensorflow/teams/devinfra) ******************************************************************************** ## TensorFlow's Official CI and Build/Test Scripts TensorFlow's official CI jobs run the scripts in this folder. Our internal CI
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ci/official/requirements_updater/README.md
# Hermetic Python Hermetic Python allows not to rely on system-installed Python, and system-installed Python packages. \ Instead, an independent Python toolchain is registered, ensuring the right dependencies are always used. \ See https://github.com/bazelbuild/rules_python/ for more details. ### Specifying the Python version Note: Only a number of minor Python versions are supported at any given time.
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WORKSPACE
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.
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