Abstraction for a locally visible physical device. TensorFlow can utilize various devices such as the CPU or multiple GPUs for computation. Before initializing a local device for use, the user can customize certain properties of the device such as it's visibility or memory configuration.
| 269 | |
| 270 | |
| 271 | class PhysicalDevice( |
| 272 | collections.namedtuple("PhysicalDevice", ["name", "device_type"])): |
| 273 | """Abstraction for a locally visible physical device. |
| 274 | |
| 275 | TensorFlow can utilize various devices such as the CPU or multiple GPUs |
| 276 | for computation. Before initializing a local device for use, the user can |
| 277 | customize certain properties of the device such as it's visibility or memory |
| 278 | configuration. |
| 279 | |
| 280 | Once a PhysicalDevice is initialized one or many LogicalDevice objects are |
| 281 | created. Use tf.config.set_virtual_device_configuration() to create multiple |
| 282 | LogicalDevice objects for a PhysicalDevice. This is useful when separation |
| 283 | between models is needed. |
| 284 | |
| 285 | Fields: |
| 286 | name: Unique identifier for device. |
| 287 | device_type: String declaring the type of device such as "CPU" or "GPU". |
| 288 | """ |
| 289 | pass |
| 290 | |
| 291 | |
| 292 | class _AtomicCounter(object): |
no outgoing calls
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