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ci/official/requirements_updater/BUILD.bazel
compile_pip_requirements_3_9( name = "requirements_3_9", extra_args = ["--allow-unsafe"], requirements_in = REQUIREMENTS_FILE_NAME, requirements_txt = "requirements_lock_3_9.txt", ) compile_pip_requirements_3_10( name = "requirements_3_10", extra_args = ["--allow-unsafe"], requirements_in = REQUIREMENTS_FILE_NAME, requirements_txt = "requirements_lock_3_10.txt", )
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ci/official/utilities/code_check_changed_files.bats
setup_file() { bazel version # Start the bazel server # Fixes "fatal: detected dubious ownership in repository" for Docker. git config --system --add safe.directory '*' git config --system protocol.file.allow always # Note that you could generate a list of all the affected targets with e.g.: # bazel query $(paste -sd "+" $BATS_FILE_TMPDIR/changed_files) --keep_going
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ci/official/requirements_updater/README.md
name = "requirements_3_11", extra_args = ["--allow-unsafe"], requirements_in = "requirements.in", requirements_txt = "requirements_lock_3_11.txt", ) ``` ``` compile_pip_requirements_3_11( name = "requirements_3_11_release", extra_args = [ "--allow-unsafe", "-P keras-nightly", "-P tb-nightly",
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SECURITY.md
For example, if an attacker were to upload a malicious video file, they could potentially exploit a vulnerability in the TensorFlow code that handles videos, which could allow them to execute arbitrary code on the system running TensorFlow. It is important to keep TensorFlow up to date with the latest security patches and follow the sandboxing guideline above to protect against these types of
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