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SECURITY.md
### Tenant isolation Since any tenants or users providing models, graphs or checkpoints can execute code in context of the TensorFlow service, it is important to design isolation mechanisms that prevent unwanted access to the data from other tenants. Network isolation between different models is also important not only to prevent unauthorized access to data or models, but also to prevent malicious users or
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ci/official/debug_tfci.sh
# ============================================================================== # This script dumps some information about the environment. It's most useful # for verifying changes to the TFCI scripts system, and most users won't need # to interact with it at all. source "${BASH_SOURCE%/*}/utilities/setup.sh" echo "==TFCI== env outside of tfrun:" env echo "==TFCI== env inside of tfrun:"
Shell Script - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Nov 01 19:54:25 GMT 2023 - 1022 bytes - Viewed (0) -
ci/devinfra/docker_windows/Dockerfile
RUN [Environment]::SetEnvironmentVariable('CYGWIN', 'winsymlinks:native', 'Machine'); \ [Environment]::SetEnvironmentVariable('HOME', 'C:\Users\ContainerAdministrator\', 'Machine'); \ [Environment]::SetEnvironmentVariable('HOMEDRIVE', 'C:', 'Machine'); \ [Environment]::SetEnvironmentVariable('HOMEPATH', '\Users\ContainerAdministrator\', 'Machine'); \ [Environment]::SetEnvironmentVariable('GOROOT', 'C:\Program Files\Go\', 'Machine'); \
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ci/official/utilities/setup_macos.sh
# The TFCI Mac VM image seems to have uncommitted local changes to the Pyenv # repository so we have to discard them and reset the working directory before # we can pull in the latest changes. cd /Users/kbuilder/.pyenv/ && git reset --hard HEAD && git pull && cd - fi # "TFCI_MACOS_PYENV_INSTALL_ENABLE" controls whether to use Pyenv to install # the Python version set in "TFCI_PYTHON_VERSION" and use it as default.
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RELEASE.md
## Bug Fixes and Other Changes * tf.data: * tf.data users can now represent, get, and set options of TensorFlow input pipelines using `tf.data.Options()`, `tf.data.Dataset.options()`, and `tf.data.Dataset.with_options()` respectively. * New `tf.data.Dataset.reduce()` API allows users to reduce a finite dataset to a single element using a user-provided reduce function.
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tensorflow/c/eager/c_api_experimental.h
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tensorflow/c/BUILD
":tf_buffer_hdrs", ":tf_datatype_hdrs", ":tf_status_headers", ":tf_tensor_hdrs", # TODO: Only include tf_tstring_hdrs. Don't expose the implementation of TF_TString to API # users. ":tf_tstring", "//tensorflow/core:protos_all_cc", ], ) tf_cuda_library( name = "c_api_internal", hdrs = [ "c_api.h", "c_api_internal.h",
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configure.py
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tensorflow/c/eager/tape.h
// If `persistent` is true, GradientTape will not eagerly delete backward // functions (and hence the tensors they keep alive). Instead, everything // is deleted in ~GradientTape. Persistent GradientTapes are useful when // users want to compute multiple gradients over the same tape. explicit GradientTape(bool persistent) : persistent_(persistent) {} ~GradientTape() { for (const auto& pair : op_tape_) {
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.bazelrc
# Load rc file written by ./configure. try-import %workspace%/.tf_configure.bazelrc try-import %workspace%/xla_configure.bazelrc # Load rc file with user-specific options. try-import %workspace%/.bazelrc.user # Here are bazelrc configs for release builds # Build TensorFlow v2. test:release_base --test_size_filters=small,medium # Ensure release_base is set on linux
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