GPUOptions.Experimental

classe finale statique publique GPUOptions.Experimental

tensorflow.GPUOptions.Experimental de type Protobuf.GPUOptions.Experimental

Classes imbriquées

classe GPUOptions.Experimental.Builder tensorflow.GPUOptions.Experimental de type Protobuf.GPUOptions.Experimental
classe GPUOptions.Experimental.VirtualDevices 
 Configuration for breaking down a visible GPU into multiple "virtual"
 devices. 
interface GPUOptions.Experimental.VirtualDevicesOrBuilder

Constantes

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

Méthodes publiques

booléen
est égal (Obj objet)
Chaîne
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 statiques.Expérimental
getDefaultInstance ()
GPUOptions.Expérimental
getDefaultInstanceForType ()
final statique com.google.protobuf.Descriptors.Descriptor
getDescripteur ()
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 ()
booléen
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 ()
booléen
getUseUnifiedMemory () 
 If true, uses CUDA unified memory for memory allocations.
GPUOptions.Experimental.VirtualDevices
getVirtualDevices (index int) 
 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 (index int) 
 The multi virtual device settings.
Liste<? étend GPUOptions.Experimental.VirtualDevicesOrBuilder >
getVirtualDevicesOrBuilderList () 
 The multi virtual device settings.
int
Code de hachage ()
booléen final
estInitialisé ()
GPUOptions.Experimental.Builder statique
newBuilder ( GPUOptions.Prototype expérimental)
GPUOptions.Experimental.Builder statique
nouveauBuilder ()
GPUOptions.Experimental.Builder
nouveauBuilderForType ()
GPUOptions statiques.Expérimental
parseDelimitedFrom (entrée InputStream)
GPUOptions statiques.Expérimental
parseDelimitedFrom (entrée InputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
GPUOptions statiques.Expérimental
parseFrom (données ByteBuffer)
GPUOptions statiques.Expérimental
parseFrom (entrée com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
GPUOptions statiques.Expérimental
parseFrom (données ByteBuffer, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
GPUOptions statiques.Expérimental
parseFrom (entrée com.google.protobuf.CodedInputStream)
GPUOptions statiques.Expérimental
parseFrom (données octet[], com.google.protobuf.ExtensionRegistryLite extensionRegistry)
GPUOptions statiques.Expérimental
parseFrom (données com.google.protobuf.ByteString)
GPUOptions statiques.Expérimental
parseFrom (entrée InputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
GPUOptions statiques.Expérimental
parseFrom (données com.google.protobuf.ByteString, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
statique
analyseur ()
GPUOptions.Experimental.Builder
toBuilder ()
vide
writeTo (sortie com.google.protobuf.CodedOutputStream)

Méthodes héritées

Constantes

public statique final int COLLECTIVE_RING_ORDER_FIELD_NUMBER

Valeur constante : 4

public statique final int KERNEL_TRACKER_MAX_BYTES_FIELD_NUMBER

Valeur constante : 8

public statique final int KERNEL_TRACKER_MAX_INTERVAL_FIELD_NUMBER

Valeur constante : 7

public statique final int KERNEL_TRACKER_MAX_PENDING_FIELD_NUMBER

Valeur constante : 9

public statique final int NUM_DEV_TO_DEV_COPY_STREAMS_FIELD_NUMBER

Valeur constante : 3

public statique final int TIMESTAMPED_ALLOCATOR_FIELD_NUMBER

Valeur constante : 5

public statique final int USE_UNIFIED_MEMORY_FIELD_NUMBER

Valeur constante : 2

public statique final int VIRTUAL_DEVICES_FIELD_NUMBER

Valeur constante : 1

Méthodes publiques

public booléen égal (Objet obj)

chaîne publique 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;

GPUOptions statiques publiques.Experimental getDefaultInstance ()

public GPUOptions.Experimental getDefaultInstanceForType ()

public statique 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;

publique getParserForType ()

public int getSerializedSize ()

public booléen 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 booléen 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 (index 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;

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;

Liste publique < 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 (index 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;

Liste publique <? étend 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;

code de hachage int public ()

public final booléen isInitialized ()

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

public statique GPUOptions.Experimental.Builder newBuilder ()

public GPUOptions.Experimental.Builder newBuilderForType ()

public statique GPUOptions.Experimental parseDelimitedFrom (entrée InputStream)

Jetés
IOException

public statique GPUOptions.Experimental parseDelimitedFrom (entrée InputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

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IOException

public statique GPUOptions.Experimental parseFrom (données ByteBuffer)

Jetés
InvalidProtocolBufferException

public statique GPUOptions.Experimental parseFrom (entrée com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Jetés
IOException

public statique GPUOptions.Experimental parseFrom (données ByteBuffer, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Jetés
InvalidProtocolBufferException

public statique GPUOptions.Experimental parseFrom (entrée com.google.protobuf.CodedInputStream)

Jetés
IOException

public statique GPUOptions.Experimental parseFrom (données octet[], com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Jetés
InvalidProtocolBufferException

public statique GPUOptions.Experimental parseFrom (données com.google.protobuf.ByteString)

Jetés
InvalidProtocolBufferException

public statique GPUOptions.Experimental parseFrom (entrée InputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Jetés
IOException

public statique GPUOptions.Experimental parseFrom (données com.google.protobuf.ByteString, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Jetés
InvalidProtocolBufferException

public statique analyseur ()

public GPUOptions.Experimental.Builder toBuilder ()

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

Jetés
IOException