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tensorflow/c/eager/c_api_experimental.h
void* device_info); // Method to execute an operation. // // Arguments provide enough information to reconstruct the original `TFE_Op`, // or construct a transformed version, by inspecting the passed `op`. // // TFE_OpGetDevice(op) records the original placement of the operation. It may
C - Registered: Tue Apr 23 12:39:09 GMT 2024 - Last Modified: Wed Feb 21 22:37:46 GMT 2024 - 39.5K bytes - Viewed (0) -
RELEASE.md
* `tf.SavedModel` * Introduced class method `tf.saved_model.experimental.Fingerprint.from_proto(proto)`, which can be used to construct a `Fingerprint` object directly from a protobuf. * Introduced member method `tf.saved_model.experimental.Fingerprint.singleprint()`, which provides a convenient way to uniquely identify a SavedModel.
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tensorflow/c/c_api_test.cc
if (gpu_device.empty()) return; RunMinTest(gpu_device, /*use_XLA=*/true); } TEST(CAPI, SessionPRun) { TF_Status* s = TF_NewStatus(); TF_Graph* graph = TF_NewGraph(); // Construct the graph: A + 2 + B TF_Operation* a = Placeholder(graph, s, "A"); ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); TF_Operation* b = Placeholder(graph, s, "B");
C++ - Registered: Tue Apr 23 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 03:35:10 GMT 2024 - 96.9K bytes - Viewed (3) -
tensorflow/c/experimental/filesystem/modular_filesystem_test.cc
// parsed. // // Furthermore, as `INSTANTIATE_TEST_SUITE_P` needs to be at global level and we // don't want to have a `std::vector<std::string>` at global level, we use a // static pointer to such a vector: we construct it via `SchemeVector()` below // and when tests are instantiated we process it using `GetSchemes()`. static std::vector<std::string>* SchemeVector() { static std::vector<std::string>* schemes = new std::vector<std::string>;
C++ - Registered: Tue Apr 23 12:39:09 GMT 2024 - Last Modified: Fri May 27 20:25:58 GMT 2022 - 71K bytes - Viewed (0) -
tensorflow/c/eager/c_api.h
// Sets a thread-local device placement policy. After this call, other calls to // TFE_Execute in the same thread will use the device policy specified here // instead of the device policy used to construct the context. This has no // effect on the device policy used by other program threads. TF_CAPI_EXPORT extern void TFE_ContextSetThreadLocalDevicePlacementPolicy( TFE_Context* ctx, TFE_ContextDevicePlacementPolicy policy);
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tensorflow/c/eager/parallel_device/parallel_device_lib.h
TFE_TensorHandle* tensor, TF_Status* status) const; // Construct a parallel tensor consisting of the scalar values from `values`. template <typename DataType> std::unique_ptr<ParallelTensor> ScalarsFromSequence( absl::Span<const DataType> values, TFE_Context* context,
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tensorflow/c/eager/parallel_device/parallel_device_testlib.h
namespace tensorflow { namespace parallel_device { // A helper for performing common operations on variables. A much more // restricted stand-in for tf.Variable in Python. class Variable { public: // Construct a Variable from a resource-dtype TFE_TensorHandle and an // indication of the dtype of the variable's value. // // Note that creating this resource-dtype handle can fail, so `Create` is a
C - Registered: Tue Apr 23 12:39:09 GMT 2024 - Last Modified: Tue Feb 09 01:12:35 GMT 2021 - 6.9K bytes - Viewed (0) -
tensorflow/c/c_api.h
TF_CAPI_EXPORT extern void TF_FunctionToFunctionDef(TF_Function* func, TF_Buffer* output_func_def, TF_Status* status); // Construct and return the function whose FunctionDef representation is // serialized in `proto`. `proto_len` must equal the number of bytes // pointed to by `proto`. // Returns:
C - Registered: Tue Apr 23 12:39:09 GMT 2024 - Last Modified: Thu Oct 26 21:08:15 GMT 2023 - 82.3K bytes - Viewed (3)