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tensorflow/c/eager/parallel_device/BUILD
# tensorflow/python:_pywrap_parallel_device. filegroup( name = "lib_headers", srcs = ["parallel_device_lib.h"], ) filegroup( name = "lib_sources", srcs = ["parallel_device_lib.cc"], ) filegroup( name = "device_headers", srcs = ["parallel_device.h"], ) filegroup( name = "device_sources", srcs = ["parallel_device.cc"], ) filegroup( name = "headers",
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tensorflow/c/eager/immediate_execution_tensor_handle.h
// Returns the device which created the handle. virtual const char* DeviceName(Status* status) const = 0; // Returns the device where the tensor was placed. virtual const char* BackingDeviceName(Status* status) const = 0; // Returns the device type which created the handle. virtual const char* DeviceType(Status* status) const = 0; // Returns the device ID which created the handle.
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tensorflow/c/c_api_internal.h
// Set whether to propagate assigned device information when constructing a new // Graph from a GraphDef. By default assigned device information is not copied // and is re-computed by the runtime. inline void TF_ImportGraphDefOptionsSetPropagateDeviceSpec( TF_ImportGraphDefOptions* opts, unsigned char propagate_device_spec) { opts->opts.propagate_device_spec = propagate_device_spec; } } // end namespace tensorflow
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Sat May 13 00:49:12 GMT 2023 - 7.6K bytes - Viewed (0) -
.github/ISSUE_TEMPLATE/tflite-other.md
- type: input id: OS attributes: label: OS Platform and Distribution description: placeholder: e.g., Linux Ubuntu 16.04 validations: required: false - type: input id: Mobile attributes: label: Mobile device description: placeholder: e.g., Linux Ubuntu 16.04 validations: required: false - type: input id: Python attributes: label: Python version description: placeholder: e.g., 3.9 validations: required: false
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tensorflow/c/eager/parallel_device/parallel_device_remote_test.cc
const char* first_device = "/job:worker/replica:0/task:1/device:CPU:0"; const char* second_device = "/job:worker/replica:0/task:2/device:CPU:0"; const char* device_name = "/job:localhost/replica:0/task:0/device:CUSTOM:0"; std::array<const char*, 2> underlying_devices{first_device, second_device}; RegisterParallelDevice(context.get(), device_name, underlying_devices, status.get());
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tensorflow/c/eager/abstract_operation.h
// returned by DeviceName should be "/device:GPU:*" until a particular GPU is // chosen for the operation by the device placement logic in the // executor. After that, the value returned by DeviceName will be a full // device name such as "/job:localhost/replica:0/task:0/device:GPU:1". virtual const string& DeviceName() const = 0; // Sets the operation device name. //
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tensorflow/c/eager/dlpack_test.cc
ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); const DLTensor* dltensor_out = &dlm_out->dl_tensor; EXPECT_EQ(dltensor_out->device.device_type, dltensor_in->device.device_type); EXPECT_EQ(dltensor_out->device.device_id, dltensor_in->device.device_id); EXPECT_EQ(dltensor_out->ndim, dltensor_in->ndim); EXPECT_EQ(dltensor_out->dtype.code, dltensor_in->dtype.code);
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri Jun 30 03:04:46 GMT 2023 - 4.4K bytes - Viewed (0) -
tensorflow/api_template.__init__.py
if _os.path.exists(_plugin_dir): _ll.load_library(_plugin_dir) # Load Pluggable Device Library _ll.load_pluggable_device_library(_plugin_dir) if _os.getenv("TF_PLUGGABLE_DEVICE_LIBRARY_PATH", ""): _ll.load_pluggable_device_library( _os.getenv("TF_PLUGGABLE_DEVICE_LIBRARY_PATH") ) # Add Keras module aliases
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tensorflow/c/eager/immediate_execution_tensor_handle.cc
} Status s; const char* device_name = DeviceName(&s); if (!s.ok()) { device_name = "<error fetching device name>"; } return absl::StrCat("TensorHandle(", value_string, ", shape=", shape_string, ", dtype=", DataType_Name(DataType()), ", device=\"", device_name, "\")"); } Status ImmediateExecutionTensorHandle::SummarizeValue(
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tensorflow/c/eager/immediate_execution_operation.h
virtual ImmediateExecutionContext* GetContext() const = 0; // Following two methods are used to support custom device. // Return true if the inputs contain custom device tensor handle. It means // that the argument need to be handled by a custom device. virtual bool HasCustomDeviceInput() const = 0; virtual const tensorflow::OpDef* OpDef() const = 0;
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