GPUOptions.Experimental

GPUOptions.Experimental 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

entero COLLECTIVE_RING_ORDER_FIELD_NUMBER
entero KERNEL_TRACKER_MAX_BYTES_FIELD_NUMBER
entero KERNEL_TRACKER_MAX_INTERVAL_FIELD_NUMBER
entero KERNEL_TRACKER_MAX_PENDING_FIELD_NUMBER
entero NUM_DEV_TO_DEV_COPY_STREAMS_FIELD_NUMBER
entero TIMESTAMPED_ALLOCATOR_FIELD_NUMBER
entero USE_UNIFIED_MEMORY_FIELD_NUMBER
entero VIRTUAL_DEVICES_FIELD_NUMBER

Métodos públicos

booleano
es igual (Objeto obj)
Cadena
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.
Opciones de GPU estáticas.Experimental
getDefaultInstancia ()
GPUOptions.Experimental
getDefaultInstanceForType ()
com.google.protobuf.Descriptors.Descriptor estático final
obtenerDescriptor ()
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.
entero
getKernelTrackerMaxInterval () 
 Parameters for GPUKernelTracker.
entero
getKernelTrackerMaxPending () 
 If kernel_tracker_max_pending > 0 then no more than this many
 tracking events can be outstanding at a time.
entero
getNumDevToDevCopyStreams () 
 If > 1, the number of device-to-device copy streams to create
 for each GPUDevice.
getParserForType ()
entero
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.
com.google.protobuf.UnknownFieldSet final
obtener campos desconocidos ()
booleano
getUseUnifiedMemory () 
 If true, uses CUDA unified memory for memory allocations.
GPUOptions.Experimental.VirtualDevices
getVirtualDevices (índice int) 
 The multi virtual device settings.
entero
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.
entero
código hash ()
booleano final
está inicializado ()
GPUOptions.Experimental.Builder estático
newBuilder ( GPUOptions.Prototipo experimental)
GPUOptions.Experimental.Builder estático
nuevoConstructor ()
GPUOptions.Experimental.Builder
nuevoBuilderForType ()
Opciones de GPU estáticas.Experimental
parseDelimitedFrom (entrada de InputStream)
Opciones de GPU estáticas.Experimental
parseDelimitedFrom (entrada de InputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
Opciones de GPU estáticas.Experimental
parseFrom (datos de ByteBuffer)
Opciones de GPU estáticas.Experimental
parseFrom (entrada com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensiónRegistry)
Opciones de GPU estáticas.Experimental
parseFrom (datos de ByteBuffer, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
Opciones de GPU estáticas.Experimental
parseFrom (entrada com.google.protobuf.CodedInputStream)
Opciones de GPU estáticas.Experimental
parseFrom (byte[] datos, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
Opciones de GPU estáticas.Experimental
parseFrom (datos com.google.protobuf.ByteString)
Opciones de GPU estáticas.Experimental
parseFrom (entrada de InputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
Opciones de GPU estáticas.Experimental
parseFrom (com.google.protobuf.ByteString datos, com.google.protobuf.ExtensionRegistryLite extensiónRegistry)
estático
analizador ()
GPUOptions.Experimental.Builder
al constructor ()
vacío
writeTo (salida de com.google.protobuf.CodedOutputStream)

Métodos heredados

Constantes

int final estático público COLLECTIVE_RING_ORDER_FIELD_NUMBER

Valor constante: 4

int final estático público 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

int final estático público NUM_DEV_TO_DEV_COPY_STREAMS_FIELD_NUMBER

Valor constante: 3

int final estático público TIMESTAMPED_ALLOCATOR_FIELD_NUMBER

Valor constante: 5

int final estático público USE_UNIFIED_MEMORY_FIELD_NUMBER

Valor constante: 2

público estático final int VIRTUAL_DEVICES_FIELD_NUMBER

Valor constante: 1

Métodos públicos

público booleano es igual (Objeto obj)

cadena 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;

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;

GPUOptions estáticas públicas.Experimental getDefaultInstance ()

GPUOptions públicas.Experimental getDefaultInstanceForType ()

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

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;

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;

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;

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;

público getParserForType ()

público int getSerializedSize ()

getTimestampedAllocator booleano 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;

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

getUseUnifiedMemory booleano 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 públicas 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;

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 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 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 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;

código hash int público ()

público final booleano isInitialized ()

GPUOptions.Experimental.Builder estático público newBuilder (prototipo GPUOptions.Experimental )

GPUOptions.Experimental.Builder estático público newBuilder ()

GPUOptions.Experimental.Builder público nuevoBuilderForType ()

GPUOptions estáticas públicas. Análisis experimentalDelimitedFrom (entrada de InputStream)

Lanza
IOExcepción

GPUOptions estáticas públicas.Experimental parseDelimitedFrom (entrada de InputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza
IOExcepción

GPUOptions estáticas públicas. Análisis experimental de (datos ByteBuffer)

Lanza
Excepción de buffer de protocolo no válido

GPUOptions.Experimental estático público parseFrom (entrada com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza
IOExcepción

GPUOptions.Experimental estático público parseFrom (datos ByteBuffer, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza
Excepción de buffer de protocolo no válido

GPUOptions.Experimental estático público parseFrom (entrada com.google.protobuf.CodedInputStream)

Lanza
IOExcepción

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

Lanza
Excepción de buffer de protocolo no válido

GPUOptions.Experimental estático público parseFrom (datos com.google.protobuf.ByteString)

Lanza
Excepción de buffer de protocolo no válido

GPUOptions.Experimental estático público parseFrom (entrada de InputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza
IOExcepción

GPUOptions.Experimental estático público parseFrom (datos com.google.protobuf.ByteString, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza
Excepción de buffer de protocolo no válido

estática pública analizador ()

GPUOptions.Experimental.Builder público toBuilder ()

escritura vacía pública (salida de com.google.protobuf.CodedOutputStream)

Lanza
IOExcepción