GPUOptions.ExperimentalOrBuilder

genel statik arayüz GPUOptions.ExperimentalOrBuilder
Bilinen Dolaylı Alt Sınıflar

Genel Yöntemler

soyut Dize
getCollectiveRingOrder ()
 If non-empty, defines a good GPU ring order on a single worker based on
 device interconnect.
abstract com.google.protobuf.ByteString
getCollectiveRingOrderBytes ()
 If non-empty, defines a good GPU ring order on a single worker based on
 device interconnect.
soyut 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.
soyut int
getKernelTrackerMaxInterval ()
 Parameters for GPUKernelTracker.
soyut int
getKernelTrackerMaxPending ()
 If kernel_tracker_max_pending > 0 then no more than this many
 tracking events can be outstanding at a time.
soyut int
getNumDevToDevCopyStreams ()
 If > 1, the number of device-to-device copy streams to create
 for each GPUDevice.
soyut boole
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.
soyut boole
getUseUnifiedMemory ()
 If true, uses CUDA unified memory for memory allocations.
soyut GPUOptions.Experimental.VirtualDevices
getVirtualDevices (int dizini)
 The multi virtual device settings.
soyut int
getVirtualDevicesCount ()
 The multi virtual device settings.
özet Listesi< GPUOptions.Experimental.VirtualDevices >
getVirtualDevicesList ()
 The multi virtual device settings.
soyut GPUOptions.Experimental.VirtualDevicesOrBuilder
getVirtualDevicesOrBuilder (int dizini)
 The multi virtual device settings.
özet Liste<? GPUOptions.Experimental.VirtualDevicesOrBuilder'ı genişletir >
getVirtualDevicesOrBuilderList ()
 The multi virtual device settings.

Genel Yöntemler

genel özet Dize 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;

genel özet 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;

genel özet 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;

genel özet 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;

genel özet 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;

genel özet 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;

genel soyut boolean 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.
 
bool timestamped_allocator = 5;

genel soyut boolean getUseUnifiedMemory ()

 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;

genel özet GPUOptions.Experimental.VirtualDevices getVirtualDevices (int dizini)

 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;

genel özet 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;

genel özet Listesi< 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;

genel özet GPUOptions.Experimental.VirtualDevicesOrBuilder getVirtualDevicesOrBuilder (int indeksi)

 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;

genel özet listesi<? GPUOptions.Experimental.VirtualDevicesOrBuilder > getVirtualDevicesOrBuilderList () öğesini genişletir

 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;