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GPUOptions.Experimental.Builder

publiczna statyczna klasa końcowa GPUOptions.Experimental.Builder

Protobuf typu tensorflow.GPUOptions.Experimental

Metody publiczne

Opcje GPU. Eksperymentalne. Konstruktor	addAllVirtualDevices (Iterable<? rozszerza wartości GPUOptions.Experimental.VirtualDevices >) The multi virtual device settings.
GPUOptions.Experimental.Builder	addRepeatedField (pole com.google.protobuf.Descriptors.FieldDescriptor, wartość obiektu)
GPUOptions.Experimental.Builder	addVirtualDevices ( GPUOptions.Experimental.VirtualDevices.Builder builderForValue) The multi virtual device settings.
GPUOptions.Experimental.Builder	addVirtualDevices (indeks int, wartość GPUOptions.Experimental.VirtualDevices ) The multi virtual device settings.
GPUOptions.Experimental.Builder	addVirtualDevices (wartość GPUOptions.Experimental.VirtualDevices ) The multi virtual device settings.
GPUOptions.Experimental.Builder	addVirtualDevices (indeks int, GPUOptions.Experimental.VirtualDevices.Builder builderForValue) The multi virtual device settings.
GPUOptions.Experimental.VirtualDevices.Builder	addVirtualDevicesBuilder () The multi virtual device settings.
GPUOptions.Experimental.VirtualDevices.Builder	addVirtualDevicesBuilder (indeks int) The multi virtual device settings.
Opcje GPU. Eksperymentalne	zbudować ()
Opcje GPU. Eksperymentalne	kompilacjaCzęściowa ()
GPUOptions.Experimental.Builder	jasne ()
GPUOptions.Experimental.Builder	clearCollectiveRingOrder () If non-empty, defines a good GPU ring order on a single worker based on device interconnect.
Opcje GPU. Eksperymentalne. Konstruktor	clearField (pole com.google.protobuf.Descriptors.FieldDescriptor)
GPUOptions.Experimental.Builder	clearKernelTrackerMaxBytes () If kernel_tracker_max_bytes = n > 0, then a tracking event is inserted after every series of kernels allocating a sum of memory >= n.
GPUOptions.Experimental.Builder	clearKernelTrackerMaxInterval () Parameters for GPUKernelTracker.
GPUOptions.Experimental.Builder	clearKernelTrackerMaxPending () If kernel_tracker_max_pending > 0 then no more than this many tracking events can be outstanding at a time.
GPUOptions.Experimental.Builder	clearNumDevToDevCopyStreams () If > 1, the number of device-to-device copy streams to create for each GPUDevice.
GPUOptions.Experimental.Builder	clearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof)
GPUOptions.Experimental.Builder	clearTimestampedAllocator () 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.
GPUOptions.Experimental.Builder	wyczyśćUżyj UnifiedMemory () If true, uses CUDA unified memory for memory allocations.
GPUOptions.Experimental.Builder	wyczyśćUrządzenia wirtualne () The multi virtual device settings.
GPUOptions.Experimental.Builder	klon ()
Smyczkowy	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.
Opcje GPU. Eksperymentalne	getDefaultInstanceForType ()
końcowy statyczny com.google.protobuf.Descriptors.Descriptor	pobierzDescriptor ()
com.google.protobuf.Descriptors.Descriptor	getDescriptorForType ()
wew	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.
wew	getKernelTrackerMaxInterval () Parameters for GPUKernelTracker.
wew	getKernelTrackerMaxPending () If kernel_tracker_max_pending > 0 then no more than this many tracking events can be outstanding at a time.
wew	getNumDevToDevCopyStreams () If > 1, the number of device-to-device copy streams to create for each GPUDevice.
wartość logiczna	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.
wartość logiczna	getUseUnifiedMemory () If true, uses CUDA unified memory for memory allocations.
Opcje GPU. Eksperymentalne. Urządzenia wirtualne	getVirtualDevices (indeks int) The multi virtual device settings.
GPUOptions.Experimental.VirtualDevices.Builder	getVirtualDevicesBuilder (indeks int) The multi virtual device settings.
Lista< GPUOptions.Experimental.VirtualDevices.Builder >	getVirtualDevicesBuilderList () The multi virtual device settings.
wew	getVirtualDevicesCount () The multi virtual device settings.
Lista< GPUOptions.Experimental.VirtualDevices >	pobierz listę urządzeń wirtualnych () The multi virtual device settings.
GPUOptions.Experimental.VirtualDevicesOrBuilder	getVirtualDevicesOrBuilder (indeks int) The multi virtual device settings.
Lista<? rozszerza GPUOptions.Experimental.VirtualDevicesOrBuilder >	getVirtualDevicesOrBuilderList () The multi virtual device settings.
końcowa wartość logiczna	jest zainicjowany ()
GPUOptions.Experimental.Builder	mergeFrom (com.google.protobuf.Wiadomość inna)
GPUOptions.Experimental.Builder	mergeFrom (wejście com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite ExtensionRegistry)
final GPUOptions.Experimental.Builder	mergeUnknownFields (com.google.protobuf.UnknownFieldUstaw nieznane pola)
GPUOptions.Experimental.Builder	usuńUrządzenia Wirtualne (indeks int) The multi virtual device settings.
GPUOptions.Experimental.Builder	setCollectiveRingOrder (wartość ciągu) If non-empty, defines a good GPU ring order on a single worker based on device interconnect.
GPUOptions.Experimental.Builder	setCollectiveRingOrderBytes (wartość com.google.protobuf.ByteString) If non-empty, defines a good GPU ring order on a single worker based on device interconnect.
GPUOptions.Experimental.Builder	setField (pole com.google.protobuf.Descriptors.FieldDescriptor, wartość obiektu)
GPUOptions.Experimental.Builder	setKernelTrackerMaxBytes (wartość int) If kernel_tracker_max_bytes = n > 0, then a tracking event is inserted after every series of kernels allocating a sum of memory >= n.
GPUOptions.Experimental.Builder	setKernelTrackerMaxInterval (wartość int) Parameters for GPUKernelTracker.
GPUOptions.Experimental.Builder	setKernelTrackerMaxPending (wartość int) If kernel_tracker_max_pending > 0 then no more than this many tracking events can be outstanding at a time.
GPUOptions.Experimental.Builder	setNumDevToDevCopyStreams (wartość int) If > 1, the number of device-to-device copy streams to create for each GPUDevice.
GPUOptions.Experimental.Builder	setRepeatedField (pole com.google.protobuf.Descriptors.FieldDescriptor, indeks int, wartość obiektu)
GPUOptions.Experimental.Builder	setTimestampedAllocator (wartość logiczna) 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 GPUOptions.Experimental.Builder	setUnknownFields (com.google.protobuf.UnknownFieldUstaw nieznane pola)
GPUOptions.Experimental.Builder	setUseUnifiedMemory (wartość logiczna) If true, uses CUDA unified memory for memory allocations.
GPUOptions.Experimental.Builder	setVirtualDevices (indeks int, GPUOptions.Experimental.VirtualDevices.Builder builderForValue) The multi virtual device settings.
GPUOptions.Experimental.Builder	setVirtualDevices (indeks int, wartość GPUOptions.Experimental.VirtualDevices ) The multi virtual device settings.

Metody dziedziczone

Z klasy java.lang.Object

wartość logiczna	równa się (Obiekt arg0)
ostatnia klasa<?>	pobierzKlasę ()
wew	hashCode ()
ostateczna pustka	powiadomić ()
ostateczna pustka	powiadom wszystkich ()
Smyczkowy	doString ()
ostateczna pustka	czekaj (długi arg0, int arg1)
ostateczna pustka	czekaj (długi arg0)
ostateczna pustka	Czekać ()

Z interfejsu org.tensorflow.proto.framework.GPUOptions.ExperimentalOrBuilder

abstrakcyjny ciąg	getCollectiveRingOrder () If non-empty, defines a good GPU ring order on a single worker based on device interconnect.
streszczenie com.google.protobuf.ByteString	getCollectiveRingOrderBytes () If non-empty, defines a good GPU ring order on a single worker based on device interconnect.
streszczenie wew	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.
streszczenie wew	getKernelTrackerMaxInterval () Parameters for GPUKernelTracker.
streszczenie wew	getKernelTrackerMaxPending () If kernel_tracker_max_pending > 0 then no more than this many tracking events can be outstanding at a time.
streszczenie wew	getNumDevToDevCopyStreams () If > 1, the number of device-to-device copy streams to create for each GPUDevice.
abstrakcyjna wartość logiczna	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.
abstrakcyjna wartość logiczna	getUseUnifiedMemory () If true, uses CUDA unified memory for memory allocations.
streszczenie GPUOptions.Experimental.VirtualDevices	getVirtualDevices (indeks int) The multi virtual device settings.
streszczenie wew	getVirtualDevicesCount () The multi virtual device settings.
Lista abstrakcyjna< GPUOptions.Experimental.VirtualDevices >	pobierz listę urządzeń wirtualnych () The multi virtual device settings.
streszczenie GPUOptions.Experimental.VirtualDevicesOrBuilder	getVirtualDevicesOrBuilder (indeks int) The multi virtual device settings.
lista abstrakcyjna<? rozszerza GPUOptions.Experimental.VirtualDevicesOrBuilder >	getVirtualDevicesOrBuilderList () The multi virtual device settings.

Metody publiczne

public GPUOptions.Experimental.Builder addAllVirtualDevices (Iterable<? rozszerza wartości GPUOptions.Experimental.VirtualDevices >)

 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.Builder addRepeatedField (pole com.google.protobuf.Descriptors.FieldDescriptor, wartość obiektu)

public GPUOptions.Experimental.Builder addVirtualDevices ( GPUOptions.Experimental.VirtualDevices.Builder builderForValue)

 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.Builder addVirtualDevices (indeks int, wartość GPUOptions.Experimental.VirtualDevices )

 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.Builder addVirtualDevices (wartość GPUOptions.Experimental.VirtualDevices )

 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.Builder addVirtualDevices (indeks int, GPUOptions.Experimental.VirtualDevices.Builder builderForValue)

 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.VirtualDevices.Builder addVirtualDevicesBuilder ()

 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.VirtualDevices.Builder addVirtualDevicesBuilder (indeks 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 GPUOptions.Kompilacja eksperymentalna ()

public GPUOptions.Kompilacja eksperymentalnaCzęściowa ()

public GPUOptions.Experimental.Builder wyczyść ()

public GPUOptions.Experimental.Builder clearCollectiveRingOrder ()

 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;

publiczne GPUOptions.Experimental.Builder clearField (pole com.google.protobuf.Descriptors.FieldDescriptor)

public GPUOptions.Experimental.Builder clearKernelTrackerMaxBytes ()

 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 GPUOptions.Experimental.Builder clearKernelTrackerMaxInterval ()

 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 GPUOptions.Experimental.Builder clearKernelTrackerMaxPending ()

 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 GPUOptions.Experimental.Builder clearNumDevToDevCopyStreams ()

 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;

public GPUOptions.Experimental.Builder clearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof)

public GPUOptions.Experimental.Builder clearTimestampedAllocator ()

 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 GPUOptions.Experimental.Builder wyczyśćUseUnifiedMemory ()

 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.Builder clearVirtualDevices ()

 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.Builder clone ()

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;

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;

publiczne GPUOptions.Eksperymentalne getDefaultInstanceForType ()

public static final com.google.protobuf.Descriptors.Descriptor getDescriptor ()

publiczny com.google.protobuf.Descriptors.Descriptor getDescriptorForType ()

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;

publiczna wartość logiczna 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;

publiczna wartość logiczna 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 (indeks 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 GPUOptions.Experimental.VirtualDevices.Builder getVirtualDevicesBuilder (indeks 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 List< GPUOptions.Experimental.VirtualDevices.Builder > getVirtualDevicesBuilderList ()

 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;

public List< 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 (indeks 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 publiczna<? rozszerza 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;

publiczna końcowa wartość logiczna isInitialized ()

public GPUOptions.Experimental.Builder mergeFrom (com.google.protobuf.Message other)

publiczne GPUOptions.Experimental.Builder mergeFrom (wejście com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite ExtensionRegistry)

Rzuca

Wyjątek IO

publiczna wersja końcowa GPUOptions.Experimental.Builder mergeUnknownFields (com.google.protobuf.UnknownFieldSetknownFields)

public GPUOptions.Experimental.Builder usuńVirtualDevices (indeks 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 GPUOptions.Experimental.Builder setCollectiveRingOrder (wartość ciągu)

 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 GPUOptions.Experimental.Builder setCollectiveRingOrderBytes (wartość com.google.protobuf.ByteString)

 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 GPUOptions.Experimental.Builder setField (pole com.google.protobuf.Descriptors.FieldDescriptor, wartość obiektu)

public GPUOptions.Experimental.Builder setKernelTrackerMaxBytes (wartość int)

 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 GPUOptions.Experimental.Builder setKernelTrackerMaxInterval (wartość int)

 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 GPUOptions.Experimental.Builder setKernelTrackerMaxPending (wartość int)

 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 GPUOptions.Experimental.Builder setNumDevToDevCopyStreams (wartość int)

 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;

public GPUOptions.Experimental.Builder setRepeatedField (pole com.google.protobuf.Descriptors.FieldDescriptor, indeks int, wartość obiektu)

public GPUOptions.Experimental.Builder setTimestampedAllocator (wartość logiczna)

 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;

publiczny końcowy GPUOptions.Experimental.Builder setUnknownFields (com.google.protobuf.UnknownFieldSetknownFields)

public GPUOptions.Experimental.Builder setUseUnifiedMemory (wartość logiczna)

 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.Builder setVirtualDevices (indeks int, GPUOptions.Experimental.VirtualDevices.Builder builderForValue)

 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.Builder setVirtualDevices (indeks int, wartość GPUOptions.Experimental.VirtualDevices )

 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;