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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/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/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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tensorflow/c/eager/parallel_device/parallel_device_lib.cc
// Outputs: std::vector<TensorHandlePtr> op_outputs_ TF_GUARDED_BY(execution_mutex_); // TF_Status is an incomplete type and so can't be stack allocated. To avoid // unnecessary allocations each Execute call, we keep one heap-allocated // version for the thread. StatusPtr status_ TF_GUARDED_BY(execution_mutex_); const std::string device_; ExecutorPtr executor_ TF_GUARDED_BY(execution_mutex_);
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tensorflow/c/eager/c_api_distributed_test.cc
const char task1_name[] = "/job:localhost/replica:0/task:1/device:CPU:0"; const char task2_name[] = "/job:localhost/replica:0/task:2/device:CPU:0"; // Create one variable per task. TFE_TensorHandle* h0 = TestVariable(ctx, 1.0, task1_name); TFE_TensorHandle* h1 = TestVariable(ctx, 2.0, task2_name); TFE_TensorHandle* h2 = TestVariable(ctx, 3.0, task0_name);
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