GPUOptions.Experimental.Builder

सार्वजनिक स्थैतिक अंतिम वर्ग GPUOptions.Experimental.Builder

प्रोटोबफ़ प्रकार tensorflow.GPUOptions.Experimental

सार्वजनिक तरीके

GPUOptions.प्रायोगिक.बिल्डर
addAllVirtualDevices (Iterable<? GPUOptions.Experimental.VirtualDevices > मान बढ़ाता है) 
 The multi virtual device settings.
GPUOptions.प्रायोगिक.बिल्डर
addRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor फ़ील्ड, ऑब्जेक्ट मान)
GPUOptions.प्रायोगिक.बिल्डर
addVirtualDevices ( GPUOptions.Experimental.VirtualDevices.Builder BuilderForValue) 
 The multi virtual device settings.
GPUOptions.प्रायोगिक.बिल्डर
addVirtualDevices (int अनुक्रमणिका, GPUOptions.Experimental.VirtualDevices मान) 
 The multi virtual device settings.
GPUOptions.प्रायोगिक.बिल्डर
addVirtualDevices ( GPUOptions.Experimental.VirtualDevices मान) 
 The multi virtual device settings.
GPUOptions.प्रायोगिक.बिल्डर
addVirtualDevices (int इंडेक्स, 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 (int अनुक्रमणिका) 
 The multi virtual device settings.
GPUOptions.प्रायोगिक
निर्माण ()
GPUOptions.प्रायोगिक
बिल्डआंशिक ()
GPUOptions.प्रायोगिक.बिल्डर
स्पष्ट ()
GPUOptions.प्रायोगिक.बिल्डर
क्लियरकलेक्टिवरिंगऑर्डर () 
 If non-empty, defines a good GPU ring order on a single worker based on
 device interconnect.
GPUOptions.प्रायोगिक.बिल्डर
क्लियरफ़ील्ड (com.google.protobuf.Descriptors.FieldDescriptor फ़ील्ड)
GPUOptions.प्रायोगिक.बिल्डर
क्लियरकर्नेलट्रैकरमैक्सबाइट्स () 
 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.प्रायोगिक.बिल्डर
क्लियरकर्नेलट्रैकरमैक्सइंटरवल () 
 Parameters for GPUKernelTracker.
GPUOptions.प्रायोगिक.बिल्डर
क्लियरकर्नेलट्रैकरमैक्सपेंडिंग () 
 If kernel_tracker_max_pending > 0 then no more than this many
 tracking events can be outstanding at a time.
GPUOptions.प्रायोगिक.बिल्डर
ClearNumDevToDevCopyStreams () 
 If > 1, the number of device-to-device copy streams to create
 for each GPUDevice.
GPUOptions.प्रायोगिक.बिल्डर
ClearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof)
GPUOptions.प्रायोगिक.बिल्डर
क्लियरटाइमस्टैम्प्डआलोकेटर () 
 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.प्रायोगिक.बिल्डर
ClearUseUnifiedMemory () 
 If true, uses CUDA unified memory for memory allocations.
GPUOptions.प्रायोगिक.बिल्डर
क्लियरवर्चुअलडिवाइसेज () 
 The multi virtual device settings.
GPUOptions.प्रायोगिक.बिल्डर
क्लोन ()
डोरी
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.प्रायोगिक
getDefaultInstanceForType ()
अंतिम स्थिर com.google.protobuf.Descriptors.Descriptor
getDescriptor ()
com.google.protobuf.Descriptors.Descriptor
getDescriptorForType ()
int यहाँ
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 यहाँ
getKernelTrackerMaxInterval () 
 Parameters for GPUKernelTracker.
int यहाँ
getKernelTrackerMaxPending () 
 If kernel_tracker_max_pending > 0 then no more than this many
 tracking events can be outstanding at a time.
int यहाँ
getNumDevToDevCopyStreams () 
 If > 1, the number of device-to-device copy streams to create
 for each GPUDevice.
बूलियन
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.
बूलियन
getUseUnifiedMemory () 
 If true, uses CUDA unified memory for memory allocations.
GPUOptions.Experimental.VirtualDevices
getVirtualDevices (int अनुक्रमणिका) 
 The multi virtual device settings.
GPUOptions.Experimental.VirtualDevices.Builder
getVirtualDevicesBuilder (int अनुक्रमणिका) 
 The multi virtual device settings.
सूची < GPUOptions.Experimental.VirtualDevices.Builder >
getVirtualDevicesBuilderList () 
 The multi virtual device settings.
int यहाँ
getVirtualDevicesCount () 
 The multi virtual device settings.
सूची< GPUOptions.Experimental.VirtualDevices >
getVirtualDevicesList () 
 The multi virtual device settings.
GPUOptions.Experimental.VirtualDevicesOrBuilder
getVirtualDevicesOrBuilder (इंट इंडेक्स) 
 The multi virtual device settings.
सूची<? GPUOptions.Experimental.VirtualDevicesOrBuilder > का विस्तार करता है
getVirtualDevicesOrBuilderList () 
 The multi virtual device settings.
अंतिम बूलियन
प्रारंभ किया गया है ()
GPUOptions.प्रायोगिक.बिल्डर
मर्जफ्रॉम (com.google.protobuf.Message अन्य)
GPUOptions.प्रायोगिक.बिल्डर
मर्जफ्रॉम (com.google.protobuf.CodedInputStream इनपुट, com.google.protobuf.ExtensionRegistryLite एक्सटेंशनरजिस्ट्री)
अंतिम GPUOptions.Experimental.Builder
मर्जअज्ञातफ़ील्ड्स (com.google.protobuf.UnknownFieldSet अज्ञातफ़ील्ड्स)
GPUOptions.प्रायोगिक.बिल्डर
रिमूववर्चुअलडिवाइसेज (इंट इंडेक्स) 
 The multi virtual device settings.
GPUOptions.प्रायोगिक.बिल्डर
setCollectiveRingOrder (स्ट्रिंग मान) 
 If non-empty, defines a good GPU ring order on a single worker based on
 device interconnect.
GPUOptions.प्रायोगिक.बिल्डर
setCollectiveRingOrderBytes (com.google.protobuf.ByteString मान) 
 If non-empty, defines a good GPU ring order on a single worker based on
 device interconnect.
GPUOptions.प्रायोगिक.बिल्डर
सेटफ़ील्ड (com.google.protobuf.Descriptors.FieldDescriptor फ़ील्ड, ऑब्जेक्ट मान)
GPUOptions.प्रायोगिक.बिल्डर
setKernelTrackerMaxBytes (int मान) 
 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.प्रायोगिक.बिल्डर
setKernelTrackerMaxInterval (int मान) 
 Parameters for GPUKernelTracker.
GPUOptions.प्रायोगिक.बिल्डर
setKernelTrackerMaxPending (int मान) 
 If kernel_tracker_max_pending > 0 then no more than this many
 tracking events can be outstanding at a time.
GPUOptions.प्रायोगिक.बिल्डर
setNumDevToDevCopyStreams (int मान) 
 If > 1, the number of device-to-device copy streams to create
 for each GPUDevice.
GPUOptions.प्रायोगिक.बिल्डर
setRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor फ़ील्ड, इंट इंडेक्स, ऑब्जेक्ट वैल्यू)
GPUOptions.प्रायोगिक.बिल्डर
setTimestampedAllocator (बूलियन मान) 
 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.
अंतिम GPUOptions.Experimental.Builder
अज्ञात फ़ील्ड सेट करें (com.google.protobuf. अज्ञात फ़ील्ड सेट अज्ञात फ़ील्ड)
GPUOptions.प्रायोगिक.बिल्डर
setUseUnifiedMemory (बूलियन मान) 
 If true, uses CUDA unified memory for memory allocations.
GPUOptions.प्रायोगिक.बिल्डर
setVirtualDevices (int इंडेक्स, GPUOptions.Experimental.VirtualDevices.Builder BuilderForValue) 
 The multi virtual device settings.
GPUOptions.प्रायोगिक.बिल्डर
setVirtualDevices (int अनुक्रमणिका, GPUOptions.Experimental.VirtualDevices मान) 
 The multi virtual device settings.

विरासत में मिले तरीके

सार्वजनिक तरीके

सार्वजनिक GPUOptions.Experimental.Builder addAllVirtualDevices (Iterable<? GPUOptions.Experimental.VirtualDevices > मान बढ़ाता है) 

 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;

सार्वजनिक GPUOptions.Experimental.Builder addRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor फ़ील्ड, ऑब्जेक्ट मान)

सार्वजनिक 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;

सार्वजनिक GPUOptions.Experimental.Builder addVirtualDevices (int अनुक्रमणिका, GPUOptions.Experimental.VirtualDevices मान) 

 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;

सार्वजनिक GPUOptions.Experimental.Builder addVirtualDevices ( GPUOptions.Experimental.VirtualDevices मान) 

 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;

सार्वजनिक GPUOptions.Experimental.Builder addVirtualDevices (int इंडेक्स, 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;

सार्वजनिक 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;

सार्वजनिक GPUOptions.Experimental.VirtualDevices.Builder addVirtualDevicesBuilder (int अनुक्रमणिका) 

 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;

सार्वजनिक GPUOptions. प्रायोगिक निर्माण ()

सार्वजनिक GPUOptions. प्रायोगिक बिल्डआंशिक ()

सार्वजनिक GPUOptions.Experimental.Builder स्पष्ट ()

सार्वजनिक 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;

सार्वजनिक GPUOptions.Experimental.Builder ClearField (com.google.protobuf.Descriptors.FieldDescriptor फ़ील्ड)

सार्वजनिक 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;

सार्वजनिक 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;

सार्वजनिक 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;

सार्वजनिक 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;

सार्वजनिक GPUOptions.Experimental.Builder ClearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof)

सार्वजनिक 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;

सार्वजनिक 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;

सार्वजनिक 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;

सार्वजनिक GPUOptions.Experimental.Builder क्लोन ()

सार्वजनिक स्ट्रिंग 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;

सार्वजनिक 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;

सार्वजनिक GPUOptions. प्रायोगिक getDefaultInstanceForType ()

सार्वजनिक स्थैतिक अंतिम com.google.protobuf.Descriptors.Descriptor getDescriptor ()

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

सार्वजनिक 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;

सार्वजनिक 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;

सार्वजनिक 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;

सार्वजनिक 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;

सार्वजनिक बूलियन 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;

सार्वजनिक बूलियन 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;

सार्वजनिक GPUOptions.Experimental.VirtualDevices getVirtualDevices (int अनुक्रमणिका) 

 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;

सार्वजनिक GPUOptions.Experimental.VirtualDevices.Builder getVirtualDevicesBuilder (int अनुक्रमणिका) 

 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;

सार्वजनिक सूची < 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;

सार्वजनिक 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;

सार्वजनिक सूची < 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;

सार्वजनिक GPUOptions.Experimental.VirtualDevicesOrBuilder getVirtualDevicesOrBuilder (int अनुक्रमणिका) 

 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;

सार्वजनिक सूची<? GPUOptions.Experimental.VirtualDevicesOrBuilder > getVirtualDevicesOrBuilderList () का विस्तार करता है 

 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;

सार्वजनिक अंतिम बूलियन आरंभीकृत है ()

सार्वजनिक GPUOptions.Experimental.Builder mergeFrom (com.google.protobuf.Message अन्य)

सार्वजनिक GPUOptions.Experimental.Builder mergeFrom (com.google.protobuf.CodedInputStream इनपुट, com.google.protobuf.ExtensionRegistryLite एक्सटेंशनरजिस्ट्री)

फेंकता
आईओएक्सेप्शन

सार्वजनिक अंतिम GPUOptions.Experimental.Builder mergeUnknownFields (com.google.protobuf.UnknownFieldSet अज्ञातFields)

सार्वजनिक GPUOptions.Experimental.Builder रिमूववर्चुअलडिवाइसेज (int इंडेक्स) 

 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;

सार्वजनिक GPUOptions.Experimental.Builder setCollectiveRingOrder (स्ट्रिंग मान) 

 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;

सार्वजनिक GPUOptions.Experimental.Builder setCollectiveRingOrderBytes (com.google.protobuf.ByteString मान) 

 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;

सार्वजनिक GPUOptions.Experimental.Builder setField (com.google.protobuf.Descriptors.FieldDescriptor फ़ील्ड, ऑब्जेक्ट मान)

सार्वजनिक GPUOptions.Experimental.Builder setKernelTrackerMaxBytes (int मान) 

 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;

सार्वजनिक GPUOptions.Experimental.Builder setKernelTrackerMaxInterval (int मान) 

 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;

सार्वजनिक GPUOptions.Experimental.Builder setKernelTrackerMaxPending (int मान) 

 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;

सार्वजनिक GPUOptions.Experimental.Builder setNumDevToDevCopyStreams (int मान) 

 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;

सार्वजनिक GPUOptions.Experimental.Builder setRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor फ़ील्ड, int अनुक्रमणिका, ऑब्जेक्ट मान)

सार्वजनिक GPUOptions.Experimental.Builder setTimestampedAllocator (बूलियन मान) 

 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;

सार्वजनिक अंतिम GPUOptions.Experimental.Builder setUnknownFields (com.google.protobuf.UnknownFieldSet अज्ञातFields)

सार्वजनिक GPUOptions.Experimental.Builder setUseUnifiedMemory (बूलियन मान) 

 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;

सार्वजनिक GPUOptions.Experimental.Builder setVirtualDevices (int इंडेक्स, 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;

सार्वजनिक GPUOptions.Experimental.Builder setVirtualDevices (int अनुक्रमणिका, GPUOptions.Experimental.VirtualDevices मान) 

 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;