interfaz estática pública GPUOptions.ExperimentalOrBuilder
Subclases indirectas conocidas |
Métodos públicos
cadena abstracta | getCollectiveRingOrder () If non-empty, defines a good GPU ring order on a single worker based on device interconnect. |
resumen com.google.protobuf.ByteString | getCollectiveRingOrderBytes () If non-empty, defines a good GPU ring order on a single worker based on device interconnect. |
resumen entero | getKernelTrackerMaxBytes () If kernel_tracker_max_bytes = n > 0, then a tracking event is inserted after every series of kernels allocating a sum of memory >= n. |
resumen entero | getKernelTrackerMaxInterval () Parameters for GPUKernelTracker. |
resumen entero | getKernelTrackerMaxPending () If kernel_tracker_max_pending > 0 then no more than this many tracking events can be outstanding at a time. |
resumen entero | getNumDevToDevCopyStreams () If > 1, the number of device-to-device copy streams to create for each GPUDevice. |
booleano abstracto | getTimestampedAllocator () If true then extra work is done by GPUDevice and GPUBFCAllocator to keep track of when GPU memory is freed and when kernels actually complete so that we can know when a nominally free memory chunk is really not subject to pending use. |
booleano abstracto | getUseUnifiedMemory () If true, uses CUDA unified memory for memory allocations. |
Resumen GPUOptions.Experimental.VirtualDevices | getVirtualDevices (índice int) The multi virtual device settings. |
resumen entero | getVirtualDevicesCount () The multi virtual device settings. |
Lista abstracta < GPUOptions.Experimental.VirtualDevices > | getVirtualDevicesList () The multi virtual device settings. |
GPUOptions.Experimental.VirtualDevicesOrBuilder abstracto | getVirtualDevicesOrBuilder (índice int) The multi virtual device settings. |
Lista abstracta<? extiende GPUOptions.Experimental.VirtualDevicesOrBuilder > | getVirtualDevicesOrBuilderList () The multi virtual device settings. |
Métodos públicos
Cadena abstracta pública getCollectiveRingOrder ()
If non-empty, defines a good GPU ring order on a single worker based on device interconnect. This assumes that all workers have the same GPU topology. Specify as a comma-separated string, e.g. "3,2,1,0,7,6,5,4". This ring order is used by the RingReducer implementation of CollectiveReduce, and serves as an override to automatic ring order generation in OrderTaskDeviceMap() during CollectiveParam resolution.
string collective_ring_order = 4;
resumen público com.google.protobuf.ByteString getCollectiveRingOrderBytes ()
If non-empty, defines a good GPU ring order on a single worker based on device interconnect. This assumes that all workers have the same GPU topology. Specify as a comma-separated string, e.g. "3,2,1,0,7,6,5,4". This ring order is used by the RingReducer implementation of CollectiveReduce, and serves as an override to automatic ring order generation in OrderTaskDeviceMap() during CollectiveParam resolution.
string collective_ring_order = 4;
resumen público int getKernelTrackerMaxBytes ()
If kernel_tracker_max_bytes = n > 0, then a tracking event is inserted after every series of kernels allocating a sum of memory >= n. If one kernel allocates b * n bytes, then one event will be inserted after it, but it will count as b against the pending limit.
int32 kernel_tracker_max_bytes = 8;
resumen público int getKernelTrackerMaxInterval ()
Parameters for GPUKernelTracker. By default no kernel tracking is done. Note that timestamped_allocator is only effective if some tracking is specified. If kernel_tracker_max_interval = n > 0, then a tracking event is inserted after every n kernels without an event.
int32 kernel_tracker_max_interval = 7;
resumen público int getKernelTrackerMaxPending ()
If kernel_tracker_max_pending > 0 then no more than this many tracking events can be outstanding at a time. An attempt to launch an additional kernel will stall until an event completes.
int32 kernel_tracker_max_pending = 9;
resumen público int getNumDevToDevCopyStreams ()
If > 1, the number of device-to-device copy streams to create for each GPUDevice. Default value is 0, which is automatically converted to 1.
int32 num_dev_to_dev_copy_streams = 3;
getTimestampedAllocator booleano abstracto público ()
If true then extra work is done by GPUDevice and GPUBFCAllocator to keep track of when GPU memory is freed and when kernels actually complete so that we can know when a nominally free memory chunk is really not subject to pending use.
bool timestamped_allocator = 5;
getUseUnifiedMemory booleano abstracto público ()
If true, uses CUDA unified memory for memory allocations. If per_process_gpu_memory_fraction option is greater than 1.0, then unified memory is used regardless of the value for this field. See comments for per_process_gpu_memory_fraction field for more details and requirements of the unified memory. This option is useful to oversubscribe memory if multiple processes are sharing a single GPU while individually using less than 1.0 per process memory fraction.
bool use_unified_memory = 2;
GPUOptions.Experimental.VirtualDevices abstracto público getVirtualDevices (índice int)
The multi virtual device settings. If empty (not set), it will create single virtual device on each visible GPU, according to the settings in "visible_device_list" above. Otherwise, the number of elements in the list must be the same as the number of visible GPUs (after "visible_device_list" filtering if it is set), and the string represented device names (e.g. /device:GPU:<id>) will refer to the virtual devices and have the <id> field assigned sequentially starting from 0, according to the order they appear in this list and the "memory_limit" list inside each element. For example, visible_device_list = "1,0" virtual_devices { memory_limit: 1GB memory_limit: 2GB } virtual_devices {} will create three virtual devices as: /device:GPU:0 -> visible GPU 1 with 1GB memory /device:GPU:1 -> visible GPU 1 with 2GB memory /device:GPU:2 -> visible GPU 0 with all available memory NOTE: 1. It's invalid to set both this and "per_process_gpu_memory_fraction" at the same time. 2. Currently this setting is per-process, not per-session. Using different settings in different sessions within same process will result in undefined behavior.
repeated .tensorflow.GPUOptions.Experimental.VirtualDevices virtual_devices = 1;
resumen público int getVirtualDevicesCount ()
The multi virtual device settings. If empty (not set), it will create single virtual device on each visible GPU, according to the settings in "visible_device_list" above. Otherwise, the number of elements in the list must be the same as the number of visible GPUs (after "visible_device_list" filtering if it is set), and the string represented device names (e.g. /device:GPU:<id>) will refer to the virtual devices and have the <id> field assigned sequentially starting from 0, according to the order they appear in this list and the "memory_limit" list inside each element. For example, visible_device_list = "1,0" virtual_devices { memory_limit: 1GB memory_limit: 2GB } virtual_devices {} will create three virtual devices as: /device:GPU:0 -> visible GPU 1 with 1GB memory /device:GPU:1 -> visible GPU 1 with 2GB memory /device:GPU:2 -> visible GPU 0 with all available memory NOTE: 1. It's invalid to set both this and "per_process_gpu_memory_fraction" at the same time. 2. Currently this setting is per-process, not per-session. Using different settings in different sessions within same process will result in undefined behavior.
repeated .tensorflow.GPUOptions.Experimental.VirtualDevices virtual_devices = 1;
Lista abstracta pública < GPUOptions.Experimental.VirtualDevices > getVirtualDevicesList ()
The multi virtual device settings. If empty (not set), it will create single virtual device on each visible GPU, according to the settings in "visible_device_list" above. Otherwise, the number of elements in the list must be the same as the number of visible GPUs (after "visible_device_list" filtering if it is set), and the string represented device names (e.g. /device:GPU:<id>) will refer to the virtual devices and have the <id> field assigned sequentially starting from 0, according to the order they appear in this list and the "memory_limit" list inside each element. For example, visible_device_list = "1,0" virtual_devices { memory_limit: 1GB memory_limit: 2GB } virtual_devices {} will create three virtual devices as: /device:GPU:0 -> visible GPU 1 with 1GB memory /device:GPU:1 -> visible GPU 1 with 2GB memory /device:GPU:2 -> visible GPU 0 with all available memory NOTE: 1. It's invalid to set both this and "per_process_gpu_memory_fraction" at the same time. 2. Currently this setting is per-process, not per-session. Using different settings in different sessions within same process will result in undefined behavior.
repeated .tensorflow.GPUOptions.Experimental.VirtualDevices virtual_devices = 1;
GPUOptions.Experimental.VirtualDevicesOrBuilder abstracto público getVirtualDevicesOrBuilder (índice int)
The multi virtual device settings. If empty (not set), it will create single virtual device on each visible GPU, according to the settings in "visible_device_list" above. Otherwise, the number of elements in the list must be the same as the number of visible GPUs (after "visible_device_list" filtering if it is set), and the string represented device names (e.g. /device:GPU:<id>) will refer to the virtual devices and have the <id> field assigned sequentially starting from 0, according to the order they appear in this list and the "memory_limit" list inside each element. For example, visible_device_list = "1,0" virtual_devices { memory_limit: 1GB memory_limit: 2GB } virtual_devices {} will create three virtual devices as: /device:GPU:0 -> visible GPU 1 with 1GB memory /device:GPU:1 -> visible GPU 1 with 2GB memory /device:GPU:2 -> visible GPU 0 with all available memory NOTE: 1. It's invalid to set both this and "per_process_gpu_memory_fraction" at the same time. 2. Currently this setting is per-process, not per-session. Using different settings in different sessions within same process will result in undefined behavior.
repeated .tensorflow.GPUOptions.Experimental.VirtualDevices virtual_devices = 1;
Lista de resúmenes públicos <? extiende GPUOptions.Experimental.VirtualDevicesOrBuilder > getVirtualDevicesOrBuilderList ()
The multi virtual device settings. If empty (not set), it will create single virtual device on each visible GPU, according to the settings in "visible_device_list" above. Otherwise, the number of elements in the list must be the same as the number of visible GPUs (after "visible_device_list" filtering if it is set), and the string represented device names (e.g. /device:GPU:<id>) will refer to the virtual devices and have the <id> field assigned sequentially starting from 0, according to the order they appear in this list and the "memory_limit" list inside each element. For example, visible_device_list = "1,0" virtual_devices { memory_limit: 1GB memory_limit: 2GB } virtual_devices {} will create three virtual devices as: /device:GPU:0 -> visible GPU 1 with 1GB memory /device:GPU:1 -> visible GPU 1 with 2GB memory /device:GPU:2 -> visible GPU 0 with all available memory NOTE: 1. It's invalid to set both this and "per_process_gpu_memory_fraction" at the same time. 2. Currently this setting is per-process, not per-session. Using different settings in different sessions within same process will result in undefined behavior.
repeated .tensorflow.GPUOptions.Experimental.VirtualDevices virtual_devices = 1;