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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1 # Prevent double-compilation of some TF code, ref. b/183279666 (internal) # > TF's gen_api_init_files has a genrule to run the core TensorFlow code # > on the host machine. If we don't have --distinct_host_configuration=false, # > the core TensorFlow code will be built once for the host and once for the # > target platform.
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.github/ISSUE_TEMPLATE/tflite-other.md
Request - Documentation Bug - Others validations: required: true - type: markdown attributes: value: | Please make sure that this is a bug. As per our [GitHub Policy](https://github.com/tensorflow/tensorflow/blob/master/ISSUES.md),we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. - type: markdown attributes: value: |
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CONTRIBUTING.md
using a job called "copybara". **5. Copy to Google Internal codebase and run internal CI** - Once the PR is in the Google codebase, we make sure it integrates well with its dependencies and the rest of the system. - Rarely, If the tests fail at this stage, we cannot merge the code. - If needed, we may come to you to make some changes. At times, it may not be
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tensorflow/c/eager/parallel_device/BUILD
licenses = ["notice"], ) # Currently pybind extension shared objects must use only C API headers since # the C API has static initializers duplicated in the Python bindings. So we # need a second rule that omits .cc files, in # tensorflow/python:_pywrap_parallel_device. filegroup( name = "lib_headers", srcs = ["parallel_device_lib.h"], ) filegroup( name = "lib_sources",
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SECURITY.md
whether these uses were recommended or considered safe, or where we recommend some form of isolation when dealing with untrusted data. As a result, this document also outlines what issues we consider as TensorFlow security vulnerabilities. We recognize issues as vulnerabilities only when they occur in scenarios that we outline as safe; issues that have a security impact only when TensorFlow is used
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WORKSPACE
# buildifier: disable=load-on-top workspace(name = "org_tensorflow") # buildifier: disable=load-on-top # We must initialize hermetic python first. load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive") http_archive( name = "bazel_skylib", sha256 = "74d544d96f4a5bb630d465ca8bbcfe231e3594e5aae57e1edbf17a6eb3ca2506", urls = [
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ci/official/requirements_updater/requirements.in
typing_extensions == 4.8.0 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
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tensorflow/c/experimental/filesystem/BUILD
"modular_filesystem_registration.cc", ], hdrs = [ "modular_filesystem.h", "modular_filesystem_registration.h", ], # TODO(b/139060984): Visibility should be more restrictive once we # convert to modular filesystems everywhere visibility = ["//visibility:public"], deps = [ ":filesystem_interface", "//tensorflow/c:tf_status_helper", "//tensorflow/c:tf_status_internal",
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tensorflow/c/BUILD
"//conditions:default": [], }), tags = [ "no_cuda_asan", # TODO(b/181771536) "no_windows", # TODO(b/155444728) ], # We must ensure that the dependencies can be dynamically linked since # the shared library must be able to use core:framework. deps = [ ":c_api", ":c_api_internal", ":c_test_util",
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ci/official/containers/linux_arm64/cuda.packages.txt
libcudnn8=8.9.6.50-1+cuda12.2 libcudnn8-dev=8.9.6.50-1+cuda12.2 # This can be removed once NVIDIA publishes a cuda-12.3.2 Docker image. # For now it ensures that we install at least version 12.3.107 of PTXAS, # since 12.3.103 has a bug.
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