GPUOptions.Experimental.Builder

공개 정적 최종 클래스 GPUOptions.Experimental.Builder

Protobuf 유형 tensorflow.GPUOptions.Experimental

공개 방법

GPUOptions.Experimental.Builder	addAllVirtualDevices (Iterable<? 확장 GPUOptions.Experimental.VirtualDevices > 값) The multi virtual device settings.
GPUOptions.Experimental.Builder	addRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor 필드, 개체 값)
GPUOptions.Experimental.Builder	addVirtualDevices ( GPUOptions.Experimental.VirtualDevices.Builder builderForValue) The multi virtual device settings.
GPUOptions.Experimental.Builder	addVirtualDevices (int 인덱스, GPUOptions.Experimental.VirtualDevices 값) The multi virtual device settings.
GPUOptions.Experimental.Builder	addVirtualDevices ( GPUOptions.Experimental.VirtualDevices 값) The multi virtual device settings.
GPUOptions.Experimental.Builder	addVirtualDevices (int 인덱스, 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 (정수 인덱스) The multi virtual device settings.
GPUOptions.Experimental	짓다 ()
GPUOptions.Experimental	빌드부분 ()
GPUOptions.Experimental.Builder	분명한 ()
GPUOptions.Experimental.Builder	ClearCollectiveRingOrder () If non-empty, defines a good GPU ring order on a single worker based on device interconnect.
GPUOptions.Experimental.Builder	ClearField (com.google.protobuf.Descriptors.FieldDescriptor 필드)
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	클리어타임스탬프 할당자 () 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	ClearUseUnifiedMemory () If true, uses CUDA unified memory for memory allocations.
GPUOptions.Experimental.Builder	클리어가상장치 () The multi virtual device settings.
GPUOptions.Experimental.Builder	클론 ()
끈	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.Experimental	getDefaultInstanceForType ()
최종 정적 com.google.protobuf.Descriptors.Descriptor	getDescriptor ()
com.google.protobuf.Descriptors.Descriptor	getDescriptorForType ()
정수	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.
정수	getKernelTrackerMaxInterval () Parameters for GPUKernelTracker.
정수	getKernelTrackerMaxPending () If kernel_tracker_max_pending > 0 then no more than this many tracking events can be outstanding at a time.
정수	getNumDevToDevCopyStreams () If > 1, the number of device-to-device copy streams to create for each GPUDevice.
부울	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.
부울	getUseUnifiedMemory () If true, uses CUDA unified memory for memory allocations.
GPUOptions.Experimental.VirtualDevices	getVirtualDevices (정수 인덱스) The multi virtual device settings.
GPUOptions.Experimental.VirtualDevices.Builder	getVirtualDevicesBuilder (정수 인덱스) The multi virtual device settings.
목록< GPUOptions.Experimental.VirtualDevices.Builder >	getVirtualDevicesBuilderList () The multi virtual device settings.
정수	getVirtualDevicesCount () The multi virtual device settings.
목록< GPUOptions.Experimental.VirtualDevices >	getVirtualDevicesList () The multi virtual device settings.
GPUOptions.Experimental.VirtualDevicesOrBuilder	getVirtualDevicesOrBuilder (정수 인덱스) The multi virtual device settings.
목록<? GPUOptions.Experimental.VirtualDevicesOrBuilder 확장 >	getVirtualDevicesOrBuilderList () The multi virtual device settings.
최종 부울	초기화됨 ()
GPUOptions.Experimental.Builder	mergeFrom (com.google.protobuf.다른 메시지 보내기)
GPUOptions.Experimental.Builder	mergeFrom (com.google.protobuf.CodedInputStream 입력, com.google.protobuf.ExtensionRegistryLite ExtensionRegistry)
최종 GPUOptions.Experimental.Builder	mergeUnknownFields (com.google.protobuf.UnknownFieldSet 알려지지 않은Fields)
GPUOptions.Experimental.Builder	RemoveVirtualDevices (정수 인덱스) The multi virtual device settings.
GPUOptions.Experimental.Builder	setCollectiveRingOrder (문자열 값) If non-empty, defines a good GPU ring order on a single worker based on device interconnect.
GPUOptions.Experimental.Builder	setCollectiveRingOrderBytes (com.google.protobuf.ByteString 값) If non-empty, defines a good GPU ring order on a single worker based on device interconnect.
GPUOptions.Experimental.Builder	setField (com.google.protobuf.Descriptors.FieldDescriptor 필드, 개체 값)
GPUOptions.Experimental.Builder	setKernelTrackerMaxBytes (정수 값) 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 (정수 값) Parameters for GPUKernelTracker.
GPUOptions.Experimental.Builder	setKernelTrackerMaxPending (정수 값) If kernel_tracker_max_pending > 0 then no more than this many tracking events can be outstanding at a time.
GPUOptions.Experimental.Builder	setNumDevToDevCopyStreams (int 값) If > 1, the number of device-to-device copy streams to create for each GPUDevice.
GPUOptions.Experimental.Builder	setRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor 필드, int 인덱스, 개체 값)
GPUOptions.Experimental.Builder	setTimestampedAllocator (부울 값) 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.
최종 GPUOptions.Experimental.Builder	setUnknownFields (com.google.protobuf.UnknownFieldSet UnknownFields)
GPUOptions.Experimental.Builder	setUseUnifiedMemory (부울 값) If true, uses CUDA unified memory for memory allocations.
GPUOptions.Experimental.Builder	setVirtualDevices (int 인덱스, GPUOptions.Experimental.VirtualDevices.Builder builderForValue) The multi virtual device settings.
GPUOptions.Experimental.Builder	setVirtualDevices (int 인덱스, GPUOptions.Experimental.VirtualDevices 값) The multi virtual device settings.

상속된 메서드

java.lang.Object 클래스에서

부울	같음 (개체 arg0)
마지막 수업<?>	getClass ()
정수	해시 코드 ()
최종 무효	알림 ()
최종 무효	통지모두 ()
끈	toString ()
최종 무효	대기 (long arg0, int arg1)
최종 무효	기다리세요 (긴 arg0)
최종 무효	기다리다 ()

인터페이스 org.tensorflow.proto.framework.GPUOptions.ExperimentalOrBuilder 에서

추상 문자열	getCollectiveRingOrder () If non-empty, defines a good GPU ring order on a single worker based on device interconnect.
추상 com.google.protobuf.ByteString	getCollectiveRingOrderBytes () If non-empty, defines a good GPU ring order on a single worker based on device interconnect.
추상 정수	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.
추상 정수	getKernelTrackerMaxInterval () Parameters for GPUKernelTracker.
추상 정수	getKernelTrackerMaxPending () If kernel_tracker_max_pending > 0 then no more than this many tracking events can be outstanding at a time.
추상 정수	getNumDevToDevCopyStreams () If > 1, the number of device-to-device copy streams to create for each GPUDevice.
추상 부울	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.
추상 부울	getUseUnifiedMemory () If true, uses CUDA unified memory for memory allocations.
추상 GPUOptions.Experimental.VirtualDevices	getVirtualDevices (정수 인덱스) The multi virtual device settings.
추상 정수	getVirtualDevicesCount () The multi virtual device settings.
추상 목록< GPUOptions.Experimental.VirtualDevices >	getVirtualDevicesList () The multi virtual device settings.
추상 GPUOptions.Experimental.VirtualDevicesOrBuilder	getVirtualDevicesOrBuilder (정수 인덱스) The multi virtual device settings.
추상 목록<? GPUOptions.Experimental.VirtualDevicesOrBuilder 확장 >	getVirtualDevicesOrBuilderList () The multi virtual device settings.

공개 방법

공개 GPUOptions.Experimental.Builder addAllVirtualDevices (Iterable<? 확장 GPUOptions.Experimental.VirtualDevices > 값)

 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;

공개 GPUOptions.Experimental.Builder addRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor 필드, 객체 값)

공개 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;

공개 GPUOptions.Experimental.Builder addVirtualDevices (int 인덱스, GPUOptions.Experimental.VirtualDevices 값)

 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;

공개 GPUOptions.Experimental.Builder addVirtualDevices ( GPUOptions.Experimental.VirtualDevices 값)

 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;

공개 GPUOptions.Experimental.Builder addVirtualDevices (int 인덱스, 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;

공개 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;

공개 GPUOptions.Experimental.VirtualDevices.Builder addVirtualDevicesBuilder (int 인덱스)

 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;

공개 GPUOptions.Experimental 빌드 ()

공개 GPUOptions.Experimental buildPartial ()

공개 GPUOptions.Experimental.Builder 지우기 ()

공개 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;

공개 GPUOptions.Experimental.BuilderclearField ( com.google.protobuf.Descriptors.FieldDescriptor 필드)

공개 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;

공개 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;

공개 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;

공개 GPUOptions.Experimental.BuilderclearNumDevToDevCopyStreams ( )

 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;

공개 GPUOptions.Experimental.BuilderclearOneof (com.google.protobuf.Descriptors.OneofDescriptoroneof )

공개 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;

공개 GPUOptions.Experimental.Builder ClearUseUnifiedMemory ()

 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;

공개 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;

공개 GPUOptions.Experimental.Builder 클론 ()

공개 문자열 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;

공개 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;

공개 GPUOptions.Experimental getDefaultInstanceForType ()

공개 정적 최종 com.google.protobuf.Descriptors.Descriptor getDescriptor ()

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

공개 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;

공개 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;

공개 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;

공개 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;

공개 부울 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;

공개 부울 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;

공개 GPUOptions.Experimental.VirtualDevices getVirtualDevices (int 인덱스)

 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;

공개 GPUOptions.Experimental.VirtualDevices.Builder getVirtualDevicesBuilder (int 인덱스)

 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;

공개 목록< 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;

공개 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;

공개 목록< 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;

공개 GPUOptions.Experimental.VirtualDevicesOrBuilder getVirtualDevicesOrBuilder (int 인덱스)

 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;

공개 목록<? GPUOptions.Experimental.VirtualDevicesOrBuilder > getVirtualDevicesOrBuilderList () 를 확장합니다.

 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;

공개 최종 부울 isInitialized ()

공개 GPUOptions.Experimental.Builder mergeFrom (com.google.protobuf.기타 메시지)

공개 GPUOptions.Experimental.Builder mergeFrom (com.google.protobuf.CodedInputStream 입력, com.google.protobuf.ExtensionRegistryLite ExtensionRegistry)

던지기

IO예외

공개 최종 GPUOptions.Experimental.Builder mergeUnknownFields (com.google.protobuf.UnknownFieldSetknownFields)

공개 GPUOptions.Experimental.Builder RemoveVirtualDevices (int 인덱스)

 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;

공개 GPUOptions.Experimental.Builder setCollectiveRingOrder (문자열 값)

 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;

공개 GPUOptions.Experimental.Builder setCollectiveRingOrderBytes (com.google.protobuf.ByteString 값)

 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;

공개 GPUOptions.Experimental.Builder setField (com.google.protobuf.Descriptors.FieldDescriptor 필드, 개체 값)

공개 GPUOptions.Experimental.Builder setKernelTrackerMaxBytes (int 값)

 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;

공개 GPUOptions.Experimental.Builder setKernelTrackerMaxInterval (int 값)

 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;

공개 GPUOptions.Experimental.Builder setKernelTrackerMaxPending (int 값)

 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;

공개 GPUOptions.Experimental.Builder setNumDevToDevCopyStreams (int 값)

 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;

공개 GPUOptions.Experimental.Builder setRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor 필드, int 인덱스, 개체 값)

공개 GPUOptions.Experimental.Builder setTimestampedAllocator (부울 값)

 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;

공개 최종 GPUOptions.Experimental.Builder setUnknownFields (com.google.protobuf.UnknownFieldSetknownFields)

공개 GPUOptions.Experimental.Builder setUseUnifiedMemory (부울 값)

 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;

공개 GPUOptions.Experimental.Builder setVirtualDevices (int 인덱스, 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;

공개 GPUOptions.Experimental.Builder setVirtualDevices (int 인덱스, GPUOptions.Experimental.VirtualDevices 값)

 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;