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tensorflow/c/c_api_test.cc
TF_Graph* graph = TF_NewGraph(); // Make two scalar constants. TF_Operation* one = ScalarConst(1, graph, s, "one"); ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); TF_Operation* two = ScalarConst(2, graph, s, "two"); ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); // Add oper. TF_Operation* add = Add(one, two, graph, s, "add"); ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
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ci/official/utilities/rename_and_verify_wheels.sh
# Puts new wheel through auditwheel to rename and verify it, deletes the old # one, checks the filesize, and then ensures the new wheel is installable. set -euxo pipefail cd "$TFCI_OUTPUT_DIR" # Move extra wheel files somewhere out of the way. This script # expects just one wheel file to exist. if [[ "$(ls *.whl | wc -l | tr -d ' ')" != "1" ]]; then echo "More than one wheel file is present: moving the oldest to"
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CONTRIBUTING.md
airtime before a decision is made regarding whether they are to be migrated to the core. * As every PR requires several CPU/GPU hours of CI testing, we discourage submitting PRs to fix one typo, one warning,etc. We recommend fixing the same issue at the file level at least (e.g.: fix all typos in a file, fix all compiler warnings in a file, etc.) * Tests should follow the
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tensorflow/c/eager/c_api_experimental.h
TF_Status* status); // ----------------------------------------------------------------------------- // Eager Executor APIs. typedef struct TFE_Executor TFE_Executor; // Creates a new eager Executor. Nodes in one executor are guaranteed to be // executed in sequence. Assigning nodes to different executors allows executing // nodes in parallel. // in_flight_nodes_limit: when is_async is true, this value controls the
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ci/official/utilities/setup_macos.sh
# We enable this in the nightly and release builds because before uploading the # wheels, we install them in a virtual environment and run some smoke tests on # it. TFCI Mac VMs only have one Python version installed so we need to install # the other versions manually. if [[ "${TFCI_MACOS_PYENV_INSTALL_ENABLE}" == 1 ]]; then # Install the necessary Python, unless it's already present
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ci/official/utilities/code_check_full.bats
# Find all one-step dependencies of those tests which are from //tensorflow # (since external deps will come from Python-level pip dependencies), # excluding dependencies and files that are known to be unneccessary.
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tensorflow/c/c_api.cc
"important to you"); return EmptyWhileParams(); #else if (ninputs == 0) { status->status = InvalidArgument("TF_NewWhile() must be passed at least one input"); return EmptyWhileParams(); } TF_Graph* cond_graph = TF_NewGraph(); TF_Graph* body_graph = TF_NewGraph(); cond_graph->parent = g; cond_graph->parent_inputs = inputs;
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RELEASE.md
policy with `tf.data.experimental.service.ShardingPolicy` enum. It can be one of `OFF` (equivalent to today's `"parallel_epochs"` mode), `DYNAMIC` (equivalent to today's `"distributed_epoch"` mode), or one of the static sharding policies: `FILE`, `DATA`, `FILE_OR_DATA`, or `HINT` (corresponding to values of `tf.data.experimental.AutoShardPolicy`).
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tensorflow/c/eager/c_api_experimental.cc
tensorflow::ImmediateExecutionTensorHandle* unwrapped_handle = tensorflow::unwrap(handles[i]); if (tensorflow::CustomDeviceTensorHandle::classof(unwrapped_handle)) { // One of the inputs we're trying to pack is on a custom device. We'll let // the first custom device we see handle all of the packing. auto* custom_device_handle =
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tensorflow/c/eager/tape.h
// watched inputs. void Watch(int64_t tensor_id); // Records an operation with inputs `input_tensor_id` and outputs // `output_tensors` on the tape and marks all its outputs as watched if at // least one input of the op is watched and has trainable dtype. // // op_type is used to decide which of the incoming gradients can be left as // nullptr instead of building zeros when build_default_zeros_grads == true.
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