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ci/official/README.md
******************************************************************************** ## TensorFlow's Official CI and Build/Test Scripts TensorFlow's official CI jobs run the scripts in this folder. Our internal CI system, Kokoro, schedules our CI jobs by combining a build script with a file from the `envs` directory that is filled with configuration options: - Nightly jobs (Run nightly on the `nightly` branch)
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ci/official/upload.sh
# can't be set inside the rest of the _upload envs. FINAL_URI="$TFCI_ARTIFACT_FINAL_GCS_URI/$TF_VER_FULL" gsutil -m rsync -d -r "$DOWNLOADS" "$FINAL_URI" # Also mirror the latest-uploaded folder to the "latest" directory. # GCS does not support symlinks. gsutil -m rsync -d -r "$FINAL_URI" "$TFCI_ARTIFACT_LATEST_GCS_URI" fi if [[ "$TFCI_ARTIFACT_FINAL_PYPI_ENABLE" == 1 ]]; then
Shell Script - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Jan 24 20:52:12 GMT 2024 - 2.8K bytes - Viewed (0) -
CONTRIBUTING.md
`tensorflow/python/saved_model/load_test.py`: (Requires `python>=3.7`) ```bash bazel test ${flags} //tensorflow/python/saved_model:load_test --test_filter=*LoadTest.test_capture_variables* ``` **Note:** You can add `--test_sharding_strategy=disabled` to the `flags` to disable the sharding so that all the test outputs are in one file. However,
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RELEASE.md
semantics to match NumPy. * `tf.split` now takes arguments in a reversed order and with different keywords. In particular, we now match NumPy order as `tf.split(value, num_or_size_splits, axis)`. * `tf.sparse_split` now takes arguments in reversed order and with different keywords. In particular we now match NumPy order as `tf.sparse_split(sp_input, num_split, axis)`. NOTE: we have
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ci/official/utilities/code_check_full.bats
"//tensorflow/tools/lib_package:jnilicenses_generate" } # This test ensures that all the targets built into the Python package include # their dependencies. It's a rewritten version of the "smoke test", an older # Python script that was very difficult to understand. See # https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/pip_smoke_test.py @test "Pip package includes all required //tensorflow dependencies" {
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configure.py
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.bazelrc
# https://github.com/bazelbuild/bazel/issues/3360 build:linux --experimental_guard_against_concurrent_changes # Configure short or long logs build:short_logs --output_filter=DONT_MATCH_ANYTHING build:verbose_logs --output_filter= # Instruction set optimizations # TODO(gunan): Create a feature in toolchains for avx/avx2 to # avoid having to define linux/win separately. build:avx_linux --copt=-mavx
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tensorflow/c/eager/tape.h
// - target: dy in dx/dy // - source: dx in dx/dy // - tensor: one of the many inputs or outputs of an operation // // Below here we do the gradient algorithm. It works as follows: // // First we filter the tape to just the subset of operations we want to // differentiate. In the process of doing so we count how many times each Tensor // is used as an input to an op (so we know when we're done computing gradients
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