GPUOptions.Experimental

genel statik son sınıf GPUOptions.Experimental

Protobuf tipi tensorflow.GPUOptions.Experimental

İç İçe Sınıflar

sınıf GPUOptions.Experimental.Builder Protobuf tipi tensorflow.GPUOptions.Experimental
sınıf GPUOptions.Experimental.VirtualDevices
 Configuration for breaking down a visible GPU into multiple "virtual"
 devices. 
arayüz GPUOptions.Experimental.VirtualDevicesOrBuilder

Sabitler

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

Genel Yöntemler

boolean
eşittir (Nesne nesnesi)
Sicim
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.
Statik GPUSeçenekleri.Deneysel
getDefaultInstance ()
GPUOptions.Deneysel
getDefaultInstanceForType ()
final statik com.google.protobuf.Descriptors.Descriptor
getDescriptor ()
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.
int
getKernelTrackerMaxInterval ()
 Parameters for GPUKernelTracker.
int
getKernelTrackerMaxPending ()
 If kernel_tracker_max_pending > 0 then no more than this many
 tracking events can be outstanding at a time.
int
getNumDevToDevCopyStreams ()
 If > 1, the number of device-to-device copy streams to create
 for each GPUDevice.
getParserForType ()
int
getSerializedSize ()
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.
final com.google.protobuf.UnknownFieldSet
getUnknownFields ()
boolean
getUseUnifiedMemory ()
 If true, uses CUDA unified memory for memory allocations.
GPUOptions.Experimental.VirtualDevices
getVirtualDevices (int dizini)
 The multi virtual device settings.
int
getVirtualDevicesCount ()
 The multi virtual device settings.
Liste< GPUOptions.Experimental.VirtualDevices >
getVirtualDevicesList ()
 The multi virtual device settings.
GPUOptions.Experimental.VirtualDevicesOrBuilder
getVirtualDevicesOrBuilder (int dizini)
 The multi virtual device settings.
Liste<? GPUOptions.Experimental.VirtualDevicesOrBuilder'ı genişletir >
getVirtualDevicesOrBuilderList ()
 The multi virtual device settings.
int
hash kodu ()
son boole değeri
Başlatıldı ()
statik GPUOptions.Experimental.Builder
newBuilder ( GPUOptions.Deneysel prototip)
statik GPUOptions.Experimental.Builder
yeniİnşaatçı ()
GPUOptions.Experimental.Builder
yeniBuilderForType ()
Statik GPUSeçenekleri.Deneysel
parseDelimitedFrom (InputStream girişi)
Statik GPUSeçenekleri.Deneysel
parseDelimitedFrom (InputStream girişi, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
Statik GPUSeçenekleri.Deneysel
parseFrom (ByteBuffer verileri)
Statik GPUSeçenekleri.Deneysel
parseFrom (com.google.protobuf.CodedInputStream girişi, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
Statik GPUSeçenekleri.Deneysel
parseFrom (ByteBuffer verileri, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
Statik GPUSeçenekleri.Deneysel
ayrıştırmaFrom (com.google.protobuf.CodedInputStream girişi)
Statik GPUSeçenekleri.Deneysel
parseFrom (byte[] verileri, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
Statik GPUSeçenekleri.Deneysel
ayrıştırmaFrom (com.google.protobuf.ByteString verileri)
Statik GPUSeçenekleri.Deneysel
parseFrom (InputStream girişi, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
Statik GPUSeçenekleri.Deneysel
parseFrom (com.google.protobuf.ByteString verileri, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
statik
ayrıştırıcı ()
GPUOptions.Experimental.Builder
inşaatçıya ()
geçersiz
writeTo (com.google.protobuf.CodedOutputStream çıkışı)

Kalıtsal Yöntemler

Sabitler

genel statik final int COLLECTIVE_RING_ORDER_FIELD_NUMBER

Sabit Değer: 4

genel statik final int KERNEL_TRACKER_MAX_BYTES_FIELD_NUMBER

Sabit Değer: 8

genel statik final int KERNEL_TRACKER_MAX_INTERVAL_FIELD_NUMBER

Sabit Değer: 7

genel statik final int KERNEL_TRACKER_MAX_PENDING_FIELD_NUMBER

Sabit Değer: 9

genel statik son int NUM_DEV_TO_DEV_COPY_STREAMS_FIELD_NUMBER

Sabit Değer: 3

genel statik final int TIMESTAMPED_ALLOCATOR_FIELD_NUMBER

Sabit Değer: 5

genel statik final int USE_UNIFIED_MEMORY_FIELD_NUMBER

Sabit Değer: 2

genel statik final int VIRTUAL_DEVICES_FIELD_NUMBER

Sabit Değer: 1

Genel Yöntemler

genel boole eşittir (Object obj)

genel 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;

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;

genel statik GPUOptions.Experimental getDefaultInstance ()

genel GPUOptions.Experimental getDefaultInstanceForType ()

public static 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;

halk getParserForType ()

public int getSerializedSize ()

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

genel 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 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;

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;

genel Liste< 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 GPUOptions.Experimental.VirtualDevicesOrBuilder getVirtualDevicesOrBuilder (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 liste<? 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;

genel int hashCode ()

genel final boolean isInitialized ()

genel statik GPUOptions.Experimental.Builder newBuilder ( GPUOptions.Experimental prototip)

genel statik GPUOptions.Experimental.Builder newBuilder ()

genel GPUOptions.Experimental.Builder newBuilderForType ()

genel statik GPUOptions.Deneysel ayrıştırmaDelimitedFrom (InputStream girişi)

Atar
IOİstisnası

genel statik GPUOptions.Deneysel ayrıştırmaDelimitedFrom (InputStream girişi, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Atar
IOİstisnası

genel statik GPUOptions.Deneysel ayrıştırmaFrom (ByteBuffer verileri)

Atar
Geçersiz ProtokolBufferException

genel statik GPUOptions.Deneysel ayrıştırmaFrom (com.google.protobuf.CodedInputStream girişi, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Atar
IOİstisnası

genel statik GPUOptions.Deneysel ayrıştırmaFrom (ByteBuffer verileri, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Atar
Geçersiz ProtokolBufferException

genel statik GPUOptions.Deneysel ayrıştırmaFrom (com.google.protobuf.CodedInputStream girişi)

Atar
IOİstisnası

genel statik GPUOptions.Deneysel ayrıştırmaFrom (bayt[] verileri, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Atar
Geçersiz ProtokolBufferException

genel statik GPUOptions.Deneysel ayrıştırmaFrom (com.google.protobuf.ByteString verileri)

Atar
Geçersiz ProtokolBufferException

genel statik GPUOptions.Deneysel ayrıştırmaFrom (InputStream girişi, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Atar
IOİstisnası

genel statik GPUOptions.Deneysel ayrıştırmaFrom (com.google.protobuf.ByteString verileri, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Atar
Geçersiz ProtokolBufferException

genel statik ayrıştırıcı ()

genel GPUOptions.Experimental.Builder toBuilder ()

genel geçersiz writeTo (com.google.protobuf.CodedOutputStream çıkışı)

Atar
IOİstisnası