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ci/official/envs/rbe
That setting must exist for this script to function correctly. EOF exit 1 fi TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" # These flags share the user's gcloud credentials with the container, so that bazel # inside the container can authenticate. Note: TF's CI does not have any credential # stored here.
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.github/ISSUE_TEMPLATE/tflite-other.md
reproduce the issue description: Provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to Colab/Jupyter/any notebook. placeholder: Tell us what you see! value: render: shell validations: required: true - type: textarea id: logs attributes: label: Relevant log output description:
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.github/ISSUE_TEMPLATE/tflite-in-play-services.md
(`Settings` > `Apps` > `Google Play Services` > `App details`): **Standalone code to reproduce the issue** Provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to or attach code demonstrating the problem. **Any other info / logs** Include any logs or source code that would be helpful to diagnose the problem.
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tensorflow/c/BUILD
], ) cc_library( name = "tf_shape", srcs = ["tf_shape.cc"], hdrs = ["tf_shape.h"], copts = tf_copts(), visibility = ["//visibility:public"], deps = [ ":c_api_macros", ":tf_shape_internal", "//tensorflow/core:framework", ], ) cc_library( name = "tf_shape_internal", hdrs = ["tf_shape_internal.h"], copts = tf_copts(),
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ci/official/README.md
# export TFCI=py311,linux_x86,no_docker # Advanced: Use Remote Build Execution (RBE) (internal developers only) # # RBE dramatically speeds up builds and testing. It also gives you a # public URL to share your build results with collaborators. However, # it is only available to a limited set of internal TensorFlow developers. # # RBE is incompatible with local caching, so you must remove
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LICENSE
1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all
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RELEASE.md
`max_batch_size` for building TensorRT engines. Add parameters `use_dynamic_shape` to enable dynamic shape support. The default is to disable dynamic shape support. Add `dynamic_shape_profile_strategy` for selecting a dynamic shape profile strategy. The default is profile strategy is `Range`. * Issue a warning when function get_tensorrt_rewriter_config is used.
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SECURITY.md
inspected and debugged and it is intended to be used during the development phase. As part of the differences that make Eager mode easier to debug, the [shape inference functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface) are skipped, and any checks implemented inside the shape inference code are not executed. The security impact of skipping those checks should be low, since the attack
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CITATION.cff
cff-version: 1.2.0 message: "If you use TensorFlow in your research, please cite it using these metadata. Software is available from tensorflow.org." title: TensorFlow, Large-scale machine learning on heterogeneous systems
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ci/official/containers/linux_arm64/devel.usertools/wheel_verification.bats
python3 -m pip install "$TF_WHEEL" } @test "TensorFlow is importable" { source /tf/venv/bin/activate python3 -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)' } # Is this still useful? @test "TensorFlow has Keras" { source /tf/venv/bin/activate python3 -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)'
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