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CONTRIBUTING.md
* [Google Objective-C Style Guide](https://google.github.io/styleguide/objcguide.html) #### Running sanity check If you have Docker installed on your system, you can perform a sanity check on your changes by running the command: ```bash tensorflow/tools/ci_build/ci_build.sh CPU tensorflow/tools/ci_build/ci_sanity.sh ```
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ci/official/README.md
You may check how your changes will affect TensorFlow by: 1. Creating a PR and observing the presubmit test results 2. Running the CI scripts locally, as explained below 3. **Google employees only**: Google employees can use an internal-only tool called "MLCI" that makes testing more convenient: it can execute any full CI job against a pending change. Search for "MLCI" internally to find it.
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tensorflow/c/experimental/ops/README.md
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ci/official/requirements_updater/README.md
unless indicated otherwise. 1) Add the new version to the `VERSIONS` variable inside `tensorflow/tools/toolchains/python/python_repo.bzl`. \ While this isn't necessary for running the updater, it is required for actually using the new version with Tensorflow. 2) In the `WORKSPACE` file, add the new version to the `python_versions` parameter of the `python_register_multi_toolchains` function.
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RELEASE.md
* Fixes an undefined behavior arising from reference binding to nullptr in `RaggedTensorToSparse` ([CVE-2021-37656](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37656)) * Fixes an undefined behavior arising from reference binding to nullptr in `MatrixDiagV*` ops ([CVE-2021-37657](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37657))
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SECURITY.md
research](https://scholar.google.com/scholar?q=gpu+side+channel) shows that it might be possible to use side channel attacks on the GPU to leak data from other running models or processes in the same system. GPUs can also have implementation bugs that might allow attackers to leave malicious code running and leak or tamper with applications from other users. Please report vulnerabilities to the vendor of the affected hardware accelerator.
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ci/devinfra/docker_windows/Dockerfile
$env:PATH = [Environment]::GetEnvironmentVariable('PATH', 'Machine'); \ gcloud components install docker-credential-gcr kubectl gsutil; # Install cygwin and packages # Running a seperate ps1 file since when running inside a Dockerfile, it does # not work. COPY install/install_cygwin.ps1 c:/ RUN c:/install_cygwin.ps1; \ $old_path = [Environment]::GetEnvironmentVariable(\"PATH\", \"Machine\"); \
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ci/official/utilities/code_check_full.bats
EOF find . | tr '[A-Z]' '[a-z]' | sort | uniq -d | tee $BATS_FILE_TMPDIR/repeats [[ ! -s $BATS_FILE_TMPDIR/repeats ]] } # It's unclear why, but running this on //tensorflow/... is faster than running # only on affected targets, usually. There are targets in //tensorflow/lite that # don't pass --nobuild, so they're on their own. #
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.bazelrc
build:rocm --config=no_tfrt
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README.md
[![Fuzzing Status](https://oss-fuzz-build-logs.storage.googleapis.com/badges/tensorflow.svg)](https://bugs.chromium.org/p/oss-fuzz/issues/list?sort=-opened&can=1&q=proj:tensorflow)
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