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CONTRIBUTING.md
``` TensorFlow kernels and TensorFlow's dependencies are still not built with debugging information with `--config=dbg`, as issues occur on Linux if there is too much debug info (see [this GitHub issue](https://github.com/tensorflow/tensorflow/issues/48919) for context). If you want to debug a kernel, you can compile specific files with `-g` using the
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ci/devinfra/docker_windows/Dockerfile
FROM mcr.microsoft.com/dotnet/framework/sdk:4.8-windowsservercore-ltsc2019 # Set default powershell policy for this script (ProgressPreference='SilentlyContinue' makes # downloads with Invoke-WebRequest not show the progress bar and is MUCH faster). SHELL ["powershell.exe", "-ExecutionPolicy", "Bypass", "-Command", "$ErrorActionPreference='Stop'; $ProgressPreference='SilentlyContinue'; $VerbosePreference = 'Continue';"]
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ci/official/wheel_test/README.md
## Wheel Test This directory is dedicated to tests that require a built TensorFlow wheel file for testing, such as: * Ensuring the entire API is importable * Testing downstream projects against the wheel Ensure you have Bazel installed and accessible from your command line. These tests use hermetic Python. They also require a built TensorFlow wheel file and a requirements_lock file. The requirements_lock file is generated by the
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ci/official/envs/rbe
# it enables a derived --config setting. If RBE is not available (i.e. there # is no --config setting), bazel would fail and quit. This script does a quick # check This script checks for such errors early if ! grep "rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" .bazelrc; then cat <<EOF ERROR: RBE was enabled via the 'rbe' env in the 'TFCI' variable. TFCI: $TFCI
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ci/official/README.md
# 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. # - Automatic LLVM updates are known to extend build time even with # the cache; this is unavoidable. export TFCI=py311,linux_x86,public_cache,disk_cache
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RELEASE.md
on Ampere based GPUs.TensorFloat-32, or TF32 for short, is a math mode for NVIDIA Ampere based GPUs which causes certain float32 ops, such as matrix multiplications and convolutions, to run much faster on Ampere GPUs but with reduced precision. This reduced precision has not been found to effect convergence quality of deep learning models in practice.
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SECURITY.md
targets of those operations, which could result in arbitrary read/write/executions. ### Running a TensorFlow server TensorFlow is a platform for distributed computing, and as such there is a TensorFlow server (`tf.train.Server`). The TensorFlow server is intended for internal communication only. It is not built for use in untrusted environments or networks.
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tensorflow/BUILD
# New rules should be added above this target. # ------------------------------------------- # TensorFlow uses several libraries that may also be used by applications # linking against the C and C++ APIs (such as libjpeg). When we create # the shared library, only export the core TF API functions to avoid # causing library conflicts (e.g., those reported in github issue 1924).
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.bazelrc
# END TF TEST SUITE OPTIONS # START CROSS-COMPILE CONFIGS # Set execution platform to Linux x86 # Note: Lot of the "host_" flags such as "host_cpu" and "host_crosstool_top" # flags seem to be actually used to specify the execution platform details. It # seems it is this way because these flags are old and predate the distinction
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