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ci/official/containers/linux_arm64/devel.usertools/code_check_full.bats
# the dependencies of that target which include a license notice file are then # included when generating that license. Necessary because the license targets # in TensorFlow are manually enumerated rather than generated automatically. do_external_licenses_check(){ BUILD_TARGET="$1" LICENSES_TARGET="$2" # grep patterns for targets which are allowed to be missing from the licenses
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ci/official/envs/public_cache_push
# Sourcing this enables Bazel remote cache (read and write) # Note that "_push" cache configs write to GCS buckets and require # authentication. If you are not a Googler, source "public_cache" to enable the # public read-only cache. # The cache configs are different for MacOS and Linux if [[ $(uname -s) == "Darwin" ]]; then TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_macos_cache_push" else
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tensorflow/c/experimental/filesystem/BUILD
# Experimental filesystem C APIs for TensorFlow. # Will be moved in proper place once all filesystems are converted to the # modular framework. load("//tensorflow:tensorflow.bzl", "tf_cc_test") load("//tensorflow/core/platform:rules_cc.bzl", "cc_library") package( # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"], licenses = ["notice"], ) # This is only for plugins cc_library( name = "filesystem_interface",
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RELEASE.md
different computation approaches and orders. * To verify if oneDNN optimizations are on, look for a message with *"oneDNN custom operations are on"* in the log. If the exact phrase is not there, it means they are off. * Making the `tf.function` type system fully available:
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ci/official/requirements_updater/requirements.in
gast == 0.4.0 termcolor == 2.3.0 wrapt == 1.16.0 tblib == 2.0.0 # Install tensorboard, and keras # Note that here we want the latest version that matches TF major.minor version # Note that we must use nightly here as these are used in nightly jobs # For release jobs, we will pin these on the release branch keras-nightly ~= 3.0.0.dev tb-nightly ~= 2.17.0.a # Test dependencies grpcio >= 1.24.3, < 2.0 portpicker == 1.6.0 scipy == 1.11.3
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ci/official/containers/linux_arm64/Dockerfile
RUN mkdir -p /tf/venv RUN chown -R buildslave:buildslave /tf RUN dpkg -i /patchelf/patchelf_0.14.3-1_arm64.deb # All lines past this point are reset when $CACHEBUSTER is set. We need this # for Python specifically because we install some nightly packages which are # likely to change daily. ARG CACHEBUSTER=0 RUN echo $CACHEBUSTER # Setup build and environment COPY devel.usertools /usertools
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ci/official/envs/linux_arm64
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_arm64 # Note: this is not set to "--cpu", because that changes the package name # to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow" # despite lacking Nvidia CUDA support. TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow" TFCI_DOCKER_ENABLE=1
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ci/official/envs/versions_upload
# See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # Release jobs are very basic. They don't use any caching or RBE, # but they do upload logs to resultstore. # IMPORTANT: trailing slash is required on GCS URIs, as it tells gcloud to # pretend the path is a directory. TFCI_ARTIFACT_FINAL_GCS_ENABLE=1
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tensorflow/BUILD
":linux_armhf", ], ) config_setting( name = "freebsd", values = {"cpu": "freebsd"}, visibility = ["//visibility:public"], ) # Features that are default ON are handled differently below. # config_setting( name = "no_gcp_support", define_values = {"no_gcp_support": "true"}, visibility = ["//visibility:public"], ) config_setting(
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
test --build_tests_only --keep_going --test_output=errors --verbose_failures=true test --test_timeout=500,900,-1,-1 # Give only the list of failed tests at the end of the log test --test_summary=short # "nonpip" tests are regular py_test tests. # Pass --config=nonpip to run the same suite of tests. If you want to run just # one test for investigation, you don't need --config=nonpip; just run the # bazel test invocation as normal.
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