GPUOptions.Experimental

GPUOptions de clase final estática pública .

Protobuf tipo tensorflow.GPUOptions.Experimental

Clases anidadas

clase	GPUOptions.Experimental.Builder		Protobuf tipo tensorflow.GPUOptions.Experimental
clase	GPUOptions.Experimental.VirtualDevices		Configuration for breaking down a visible GPU into multiple "virtual" devices.
interfaz	GPUOptions.Experimental.VirtualDevicesOrBuilder

Constantes

En t	COLLECTIVE_RING_ORDER_FIELD_NUMBER
En t	KERNEL_TRACKER_MAX_BYTES_FIELD_NUMBER
En t	KERNEL_TRACKER_MAX_INTERVAL_FIELD_NUMBER
En t	KERNEL_TRACKER_MAX_PENDING_FIELD_NUMBER
En t	NUM_DEV_TO_DEV_COPY_STREAMS_FIELD_NUMBER
En t	TIMESTAMPED_ALLOCATOR_FIELD_NUMBER
En t	USE_UNIFIED_MEMORY_FIELD_NUMBER
En t	VIRTUAL_DEVICES_FIELD_NUMBER

Métodos públicos

booleano	es igual a (Objeto obj)
Cuerda	getCollectiveRingOrder () If non-empty, defines a good GPU ring order on a single worker based on device interconnect.
com.google.protobuf.ByteString	getCollectiveRingOrderBytes () If non-empty, defines a good GPU ring order on a single worker based on device interconnect.
GPUOptions estáticas.Experimental	getDefaultInstance ()
GPUOptions.Experimental	getDefaultInstanceForType ()
com.google.protobuf.Descriptors.Descriptor estático final	getDescriptor ()
En t	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.
En t	getKernelTrackerMaxInterval () Parameters for GPUKernelTracker.
En t	getKernelTrackerMaxPending () If kernel_tracker_max_pending > 0 then no more than this many tracking events can be outstanding at a time.
En t	getNumDevToDevCopyStreams () If > 1, the number of device-to-device copy streams to create for each GPUDevice.
	getParserForType ()
En t	getSerializedSize ()
booleano	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.
final com.google.protobuf.UnknownFieldSet	getUnknownFields ()
booleano	getUseUnifiedMemory () If true, uses CUDA unified memory for memory allocations.
GPUOptions.Experimental.VirtualDevices	getVirtualDevices (índice int) The multi virtual device settings.
En t	getVirtualDevicesCount () The multi virtual device settings.
Lista < GPUOptions.Experimental.VirtualDevices >	getVirtualDevicesList () The multi virtual device settings.
GPUOptions.Experimental.VirtualDevicesOrBuilder	getVirtualDevicesOrBuilder (índice int) The multi virtual device settings.
Lista <? extiende GPUOptions.Experimental.VirtualDevicesOrBuilder >	getVirtualDevicesOrBuilderList () The multi virtual device settings.
En t	hashCode ()
booleano final	isInitialized ()
static GPUOptions.Experimental.Builder	newBuilder ( GPUOptions.Experimental prototype)
static GPUOptions.Experimental.Builder	newBuilder ()
GPUOptions.Experimental.Builder	newBuilderForType ()
GPUOptions estáticas.Experimental	parseDelimitedFrom (entrada InputStream)
GPUOptions estáticas.Experimental	parseDelimitedFrom (entrada InputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
GPUOptions estáticas.Experimental	parseFrom (datos ByteBuffer)
GPUOptions estáticas.Experimental	parseFrom (entrada com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
GPUOptions estáticas.Experimental	parseFrom (datos ByteBuffer, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
GPUOptions estáticas.Experimental	parseFrom (entrada com.google.protobuf.CodedInputStream)
GPUOptions estáticas.Experimental	parseFrom (byte [] datos, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
GPUOptions estáticas.Experimental	parseFrom (datos com.google.protobuf.ByteString)
GPUOptions estáticas.Experimental	parseFrom (entrada InputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
GPUOptions estáticas.Experimental	parseFrom (datos com.google.protobuf.ByteString, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
estático	analizador ()
GPUOptions.Experimental.Builder	toBuilder ()
vacío	writeTo (salida de com.google.protobuf.CodedOutputStream)

Métodos heredados

De la clase java.lang.Object

booleano	es igual a (Objeto arg0)
Clase final <?>	getClass ()
En t	hashCode ()
vacío final	notificar ()
vacío final	notificar a todos ()
Cuerda	toString ()
vacío final	esperar (largo arg0, int arg1)
vacío final	espera (largo arg0)
vacío final	esperar ()

Desde la interfaz org.tensorflow.proto.framework.GPUOptions.ExperimentalOrBuilder

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.
int abstracto	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.
int abstracto	getKernelTrackerMaxInterval () Parameters for GPUKernelTracker.
int abstracto	getKernelTrackerMaxPending () If kernel_tracker_max_pending > 0 then no more than this many tracking events can be outstanding at a time.
int abstracto	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.
GPUOptions.Experimental.VirtualDevices abstractos	getVirtualDevices (índice int) The multi virtual device settings.
int abstracto	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.
Resumen Lista <? extiende GPUOptions.Experimental.VirtualDevicesOrBuilder >	getVirtualDevicesOrBuilderList () The multi virtual device settings.

Constantes

public static final int COLLECTIVE_RING_ORDER_FIELD_NUMBER

Valor constante: 4

público estático final int KERNEL_TRACKER_MAX_BYTES_FIELD_NUMBER

Valor constante: 8

público estático final int KERNEL_TRACKER_MAX_INTERVAL_FIELD_NUMBER

Valor constante: 7

público estático final int KERNEL_TRACKER_MAX_PENDING_FIELD_NUMBER

Valor constante: 9

public static final int NUM_DEV_TO_DEV_COPY_STREAMS_FIELD_NUMBER

Valor constante: 3

public static final int TIMESTAMPED_ALLOCATOR_FIELD_NUMBER

Valor constante: 5

público estático final int USE_UNIFIED_MEMORY_FIELD_NUMBER

Valor constante: 2

public static final int VIRTUAL_DEVICES_FIELD_NUMBER

Valor constante: 1

Métodos públicos

public boolean es igual a (Object obj)

public String 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; Collective_ring_order string collective_ring_order = 4;

public 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; Collective_ring_order string collective_ring_order = 4;

GPUOptions.Experimental estática pública getDefaultInstance ()

public GPUOptions.Experimental getDefaultInstanceForType ()

público estático final com.google.protobuf.Descriptors.Descriptor getDescriptor ()

public 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;

public 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;

public 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;

public 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;

público getParserForType ()

public int getSerializedSize ()

public 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;

public final com.google.protobuf.UnknownFieldSet getUnknownFields ()

public 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;

public GPUOptions.Experimental.VirtualDevices getVirtualDevices (int index)

 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;

public 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 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;

public GPUOptions.Experimental.VirtualDevicesOrBuilder getVirtualDevicesOrBuilder (int index)

 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 pública <? 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;

public int hashCode ()

public final boolean isInitialized ()

public static GPUOptions.Experimental.Builder newBuilder ( GPUOptions.Experimental prototype)

public static GPUOptions.Experimental.Builder newBuilder ()

public GPUOptions.Experimental.Builder newBuilderForType ()

public static GPUOptions.Experimental parseDelimitedFrom (InputStream input)

Lanza

IOException

public static GPUOptions.Experimental parseDelimitedFrom (InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza

IOException

GPUOptions.Experimental estática pública GPUOptions.Experimental parseFrom (ByteBuffer datos)

Lanza

InvalidProtocolBufferException

public static GPUOptions.Experimental parseFrom (com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza

IOException

public static GPUOptions.Experimental parseFrom (ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza

InvalidProtocolBufferException

public static GPUOptions.Experimental parseFrom (entrada com.google.protobuf.CodedInputStream)

Lanza

IOException

public static GPUOptions.Experimental parseFrom (byte [] datos, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza

InvalidProtocolBufferException

public static GPUOptions.Experimental parseFrom (com.google.protobuf.ByteString data)

Lanza

InvalidProtocolBufferException

public static GPUOptions.Experimental parseFrom (InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza

IOException

public static GPUOptions.Experimental parseFrom (com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza

InvalidProtocolBufferException

público estático analizador ()

public GPUOptions.Experimental.Builder toBuilder ()

public void writeTo (salida de com.google.protobuf.CodedOutputStream)

Lanza

IOException