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RELEASE.md
advanced use cases can also write their own custom `tf.train.experimental.ShardingCallback`s. * `tf.train.CheckpointOptions` * Added `experimental_skip_slot_variables` (a boolean option) to skip restoring of optimizer slot variables in a checkpoint. * `tf.saved_model.SaveOptions` * `SaveOptions` now takes a new argument called
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configure.py
def set_windows_build_flags(environ_cp): """Set Windows specific build options.""" # First available in VS 16.4. Speeds up Windows compile times by a lot. See # https://groups.google.com/a/tensorflow.org/d/topic/build/SsW98Eo7l3o/discussion # pylint: disable=line-too-long write_to_bazelrc(
Python - Registered: Tue Apr 23 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 18:25:36 GMT 2024 - 53.8K bytes - Viewed (0) -
.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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ci/official/README.md
# Recommended: use a local+remote cache. # # Bazel will cache your builds in tensorflow/build_output/cache, # and will also try using public build cache results to speed up # 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
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ci/official/utilities/setup.sh
if [[ -z "${TFCI:-}" ]]; then echo '==TFCI==: The $TFCI variable is not set. This is fine as long as you' echo 'already sourced a TFCI env file with "set -a; source <path>; set +a".' echo 'If you have not, you will see a lot of undefined variable errors.' else FROM_ENV=$(mktemp) # "export -p" prints a list of environment values in a safe-to-source format, # e.g. `declare -x TFCI_BAZEL_COMMON_ARGS="list of args"` for bash.
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