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RELEASE.md
* XLA reduction emitter is deterministic when the environment variable `TF_DETERMINISTIC_OPS` is set to "true" or "1". This extends deterministic `tf.nn.bias_add` back-prop functionality (and therefore also deterministic back-prop of bias-addition in Keras layers) to include when XLA JIT compilation is enabled. * Fix problem, when running on a CUDA GPU and when either environment
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ci/official/envs/rbe
# This env is only valid when RBE is available on the selected platform, since # it enables a derived --config setting. If RBE is not available (i.e. there # is no --config setting), bazel would fail and quit. This script does a quick # check This script checks for such errors early if ! grep "rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" .bazelrc; then cat <<EOF
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false # Change the value of CACHEBUSTER when upgrading the toolchain, or when testing # different compilation methods. E.g. for a PR to test a new CUDA version, set # the CACHEBUSTER to the PR number. build --action_env=CACHEBUSTER=20220325
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ci/official/utilities/code_check_changed_files.bats
if [[ "$(git rev-parse --abbrev-ref HEAD)" == "pull_branch" ]]; then # TF's CI runs 'git fetch origin "pull/PR#/merge:pull_branch"' # To get the as-merged branch during the CI tests git diff --diff-filter ACMRT --name-only pull_branch^ pull_branch > $BATS_FILE_TMPDIR/changed_files else # If the branch is not present, then diff against origin/master
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ci/official/envs/disk_cache
# 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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tensorflow/c/eager/parallel_device/BUILD
"//tensorflow/core:test_main", ], ) tf_cc_test( name = "parallel_device_remote_test", srcs = ["parallel_device_remote_test.cc"], # TODO(b/136478427): Enable global heap checking when servers shut down # cleanly. args = ["--heap_check="], deps = [ ":parallel_device", ":parallel_device_testlib", "//tensorflow/c:c_api",
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CONTRIBUTING.md
* If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an [individual CLA](https://code.google.com/legal/individual-cla-v1.0.html). * If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](https://code.google.com/legal/corporate-cla-v1.0.html).
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ci/official/README.md
# # Bazel will cache your builds in tensorflow/build_output/cache, # and will also try using public build cache results to speed up # your builds. This usually saves a lot of time, especially when # re-running tests. However, note that: # # - New environments like new CUDA versions, changes to manylinux, # compilers, etc. can cause undefined behavior such as build failures # or tests passing incorrectly.
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.github/ISSUE_TEMPLATE/tflite-converter-issue.md
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model. ``` ### 3. Failure after conversion If the conversion is successful, but the generated model is wrong, then state what is wrong: - Model produces wrong results and/or has lesser accuracy. - Model produces correct results, but it is slower than expected. ### 4. (optional) RNN conversion support
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WORKSPACE
# # 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()
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