GPUOptions.Experimental

classe final estática pública GPUOptions.Experimental

Tipo de protobuf tensorflow.GPUOptions.Experimental

Classes aninhadas

aula GPUOptions.Experimental.Builder Tipo de protobuf tensorflow.GPUOptions.Experimental
aula GPUOptions.Experimental.VirtualDevices
 Configuration for breaking down a visible GPU into multiple "virtual"
 devices. 
interface GPUOptions.Experimental.VirtualDevicesOrBuilder

Constantes

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

Métodos Públicos

boleano
é igual (objeto obj)
Corda
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 ()
final estático com.google.protobuf.Descriptors.Descriptor
getDescriptor ()
interno
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.
interno
getKernelTrackerMaxInterval ()
 Parameters for GPUKernelTracker.
interno
getKernelTrackerMaxPending ()
 If kernel_tracker_max_pending > 0 then no more than this many
 tracking events can be outstanding at a time.
interno
getNumDevToDevCopyStreams ()
 If > 1, the number of device-to-device copy streams to create
 for each GPUDevice.
getParserForType ()
interno
getSerializedSize ()
boleano
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 ()
boleano
getUseUnifiedMemory ()
 If true, uses CUDA unified memory for memory allocations.
GPUOptions.Experimental.VirtualDevices
getVirtualDevices (índice interno)
 The multi virtual device settings.
interno
getVirtualDevicesCount ()
 The multi virtual device settings.
Lista< GPUOptions.Experimental.VirtualDevices >
getVirtualDevicesList ()
 The multi virtual device settings.
GPUOptions.Experimental.VirtualDevicesOrBuilder
getVirtualDevicesOrBuilder (índice interno)
 The multi virtual device settings.
Lista<? estende GPUOptions.Experimental.VirtualDevicesOrBuilder >
getVirtualDevicesOrBuilderList ()
 The multi virtual device settings.
interno
código hash ()
booleano final
éinicializado ()
GPUOptions.Experimental.Builder estático
newBuilder ( GPUOptions.Protótipo experimental)
GPUOptions.Experimental.Builder estático
novoConstrutor ()
GPUOptions.Experimental.Builder
novoBuilderForType ()
GPUOptions estáticas.Experimental
parseDelimitedFrom (entrada InputStream)
GPUOptions estáticas.Experimental
parseDelimitedFrom (entrada InputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
GPUOptions estáticas.Experimental
parseFrom (dados de ByteBuffer)
GPUOptions estáticas.Experimental
parseFrom (entrada com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
GPUOptions estáticas.Experimental
parseFrom (dados de ByteBuffer, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
GPUOptions estáticas.Experimental
parseFrom (entrada com.google.protobuf.CodedInputStream)
GPUOptions estáticas.Experimental
parseFrom (byte[] dados, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
GPUOptions estáticas.Experimental
parseFrom (dados com.google.protobuf.ByteString)
GPUOptions estáticas.Experimental
parseFrom (entrada InputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
GPUOptions estáticas.Experimental
parseFrom (dados com.google.protobuf.ByteString, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
estático
analisador ()
GPUOptions.Experimental.Builder
paraConstrutor ()
vazio
writeTo (saída com.google.protobuf.CodedOutputStream)

Métodos herdados

Constantes

público estático 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

público estático final int NUM_DEV_TO_DEV_COPY_STREAMS_FIELD_NUMBER

Valor Constante: 3

público estático final int TIMESTAMPED_ALLOCATOR_FIELD_NUMBER

Valor Constante: 5

público estático final int USE_UNIFIED_MEMORY_FIELD_NUMBER

Valor Constante: 2

público estático final int VIRTUAL_DEVICES_FIELD_NUMBER

Valor Constante: 1

Métodos Públicos

booleano público é igual (Object obj)

String 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 estática pública.Experimental getDefaultInstance ()

público GPUOptions.Experimental getDefaultInstanceForType ()

final estático público 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;

final público 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;

público GPUOptions.Experimental.VirtualDevices 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;

público GPUOptions.Experimental.VirtualDevicesOrBuilder 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<? estende 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;

hashCode int público ()

público final booleano isInitialized ()

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

público estático GPUOptions.Experimental.Builder newBuilder ()

público GPUOptions.Experimental.Builder newBuilderForType ()

GPUOptions.Experimental estático públicoDelimitedFrom ( entrada InputStream)

Lança
IOException

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

Lança
IOException

GPUOptions estática pública.Experimental parseFrom (dados ByteBuffer)

Lança
InvalidProtocolBufferException

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

Lança
IOException

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

Lança
InvalidProtocolBufferException

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

Lança
IOException

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

Lança
InvalidProtocolBufferException

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

Lança
InvalidProtocolBufferException

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

Lança
IOException

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

Lança
InvalidProtocolBufferException

estática pública analisador ()

GPUOptions.Experimental.Builder público paraBuilder ()

public void writeTo (saída com.google.protobuf.CodedOutputStream)

Lança
IOException