GPUOptions.Experimental

सार्वजनिक स्थैतिक अंतिम वर्ग GPUOptions.Experimental

प्रोटोबफ़ प्रकार tensorflow.GPUOptions.Experimental

नेस्टेड क्लासेस

कक्षा GPUOptions.प्रायोगिक.बिल्डर प्रोटोबफ़ प्रकार tensorflow.GPUOptions.Experimental
कक्षा GPUOptions.Experimental.VirtualDevices 
 Configuration for breaking down a visible GPU into multiple "virtual"
 devices. 
इंटरफ़ेस GPUOptions.Experimental.VirtualDevicesOrBuilder

स्थिरांक

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

सार्वजनिक तरीके

बूलियन
बराबर (वस्तु वस्तु)
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.
स्थैतिक GPUOptions.प्रायोगिक
getDefaultInstance ()
GPUOptions.प्रायोगिक
getDefaultInstanceForType ()
अंतिम स्थिर com.google.protobuf.Descriptors.Descriptor
getDescriptor ()
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.
getParserForType ()
int यहाँ
getSerializedआकार ()
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.
अंतिम com.google.protobuf.UnknownFieldSet
अज्ञात फ़ील्ड प्राप्त करें ()
बूलियन
getUseUnifiedMemory () 
 If true, uses CUDA unified memory for memory allocations.
GPUOptions.Experimental.VirtualDevices
getVirtualDevices (int अनुक्रमणिका) 
 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.
int यहाँ
हैशकोड ()
अंतिम बूलियन
प्रारंभ किया गया है ()
स्थिर GPUOptions.Experimental.Builder
न्यूबिल्डर ( GPUOptions.प्रायोगिक प्रोटोटाइप)
स्थिर GPUOptions.Experimental.Builder
नयाबिल्डर ()
GPUOptions.प्रायोगिक.बिल्डर
न्यूबिल्डरफॉरटाइप ()
स्थैतिक GPUOptions.प्रायोगिक
parseDelimitedFrom (इनपुटस्ट्रीम इनपुट)
स्थैतिक GPUOptions.प्रायोगिक
parseDelimitedFrom (इनपुटस्ट्रीम इनपुट, com.google.protobuf.ExtensionRegistryLite एक्सटेंशनरजिस्ट्री)
स्थैतिक GPUOptions.प्रायोगिक
पार्सफ्रॉम (बाइटबफ़र डेटा)
स्थैतिक GPUOptions.प्रायोगिक
parseFrom (com.google.protobuf.CodedInputStream इनपुट, com.google.protobuf.ExtensionRegistryLite एक्सटेंशनरजिस्ट्री)
स्थैतिक GPUOptions.प्रायोगिक
पार्सफ्रॉम (बाइटबफ़र डेटा, com.google.protobuf.ExtensionRegistryLite एक्सटेंशनरजिस्ट्री)
स्थैतिक GPUOptions.प्रायोगिक
parseFrom (com.google.protobuf.CodedInputStream इनपुट)
स्थैतिक GPUOptions.प्रायोगिक
पार्सफ्रॉम (बाइट[] डेटा, com.google.protobuf.ExtensionRegistryLite एक्सटेंशनरजिस्ट्री)
स्थैतिक GPUOptions.प्रायोगिक
parseFrom (com.google.protobuf.ByteString डेटा)
स्थैतिक GPUOptions.प्रायोगिक
पार्सफ्रॉम (इनपुटस्ट्रीम इनपुट, com.google.protobuf.ExtensionRegistryLite एक्सटेंशनरजिस्ट्री)
स्थैतिक GPUOptions.प्रायोगिक
parseFrom (com.google.protobuf.ByteString डेटा, com.google.protobuf.ExtensionRegistryLite एक्सटेंशनरजिस्ट्री)
स्थिर
पार्सर ()
GPUOptions.प्रायोगिक.बिल्डर
टूबिल्डर ()
खालीपन
राइटटू (com.google.protobuf.CodedOutputStream आउटपुट)

विरासत में मिली विधियाँ

स्थिरांक

सार्वजनिक स्थैतिक अंतिम पूर्णांक COLLECTIVE_RING_ORDER_FIELD_NUMBER

स्थिर मान: 4

सार्वजनिक स्थैतिक अंतिम int KERNEL_TRACKER_MAX_BYTES_FIELD_NUMBER

स्थिर मान: 8

सार्वजनिक स्थैतिक अंतिम int KERNEL_TRACKER_MAX_INTERVAL_FIELD_NUMBER

स्थिर मान: 7

सार्वजनिक स्थैतिक अंतिम पूर्णांक KERNEL_TRACKER_MAX_PENDING_FIELD_NUMBER

स्थिर मान: 9

सार्वजनिक स्थैतिक अंतिम पूर्णांक NUM_DEV_TO_DEV_COPY_STREAMS_FIELD_NUMBER

स्थिर मान: 3

सार्वजनिक स्थैतिक अंतिम पूर्णांक TIMESTAMPED_ALLOCATOR_FIELD_NUMBER

स्थिर मान: 5

सार्वजनिक स्थैतिक अंतिम पूर्णांक USE_UNIFIED_MEMORY_FIELD_NUMBER

स्थिर मान: 2

सार्वजनिक स्थैतिक अंतिम int VIRTUAL_DEVICES_FIELD_NUMBER

स्थिर मान: 1

सार्वजनिक तरीके

सार्वजनिक बूलियन बराबर (ऑब्जेक्ट obj)

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 स्थैतिक GPUOptions. प्रायोगिक getDefaultInstance ()

सार्वजनिक GPUOptions. प्रायोगिक getDefaultInstanceForType ()

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

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;

जनता getParserForType ()

सार्वजनिक int getSerializedSize ()

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 अंतिम com.google.protobuf.UnknownFieldSet getUnknownFields ()

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 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 पूर्णांक हैशकोड ()

सार्वजनिक अंतिम बूलियन आरंभीकृत है ()

सार्वजनिक स्थैतिक GPUOptions.Experimental.Builder newBuilder ( GPUOptions.Experimental प्रोटोटाइप)

सार्वजनिक स्थैतिक GPUOptions.Experimental.Builder newBuilder ()

सार्वजनिक GPUOptions.Experimental.Builder newBuilderForType ()

सार्वजनिक स्थैतिक GPUOptions. प्रायोगिक पार्सDelimitedFrom (इनपुटस्ट्रीम इनपुट)

फेंकता
आईओएक्सेप्शन

सार्वजनिक स्थैतिक GPUOptions. प्रायोगिक पार्सडिलीमिटेडफ्रॉम (इनपुटस्ट्रीम इनपुट, com.google.protobuf.ExtensionRegistryLite एक्सटेंशनरजिस्ट्री)

फेंकता
आईओएक्सेप्शन

सार्वजनिक स्थैतिक GPUOptions. प्रायोगिक पार्सफ्रॉम (बाइटबफ़र डेटा)

फेंकता
अमान्यप्रोटोकॉलबफ़रएक्सेप्शन

सार्वजनिक स्थैतिक GPUOptions. प्रायोगिक पार्सफ्रॉम (com.google.protobuf.CodedInputStream इनपुट, com.google.protobuf.ExtensionRegistryLite एक्सटेंशनरजिस्ट्री)

फेंकता
आईओएक्सेप्शन

सार्वजनिक स्थैतिक GPUOptions. प्रायोगिक पार्सफ्रॉम (ByteBuffer डेटा, com.google.protobuf.ExtensionRegistryLite एक्सटेंशनरजिस्ट्री)

फेंकता
अमान्यप्रोटोकॉलबफ़रएक्सेप्शन

सार्वजनिक स्थैतिक GPUOptions. प्रायोगिक पार्सफ्रॉम (com.google.protobuf.CodedInputStream इनपुट)

फेंकता
आईओएक्सेप्शन

सार्वजनिक स्थैतिक GPUOptions. प्रायोगिक पार्सफ्रॉम (बाइट[] डेटा, com.google.protobuf.ExtensionRegistryLite एक्सटेंशनरजिस्ट्री)

फेंकता
अमान्यप्रोटोकॉलबफ़रएक्सेप्शन

सार्वजनिक स्थैतिक GPUOptions. प्रायोगिक पार्सFrom (com.google.protobuf.ByteString डेटा)

फेंकता
अमान्यप्रोटोकॉलबफ़रएक्सेप्शन

सार्वजनिक स्थैतिक GPUOptions. प्रायोगिक पार्सफ्रॉम (इनपुटस्ट्रीम इनपुट, com.google.protobuf.ExtensionRegistryLite एक्सटेंशनरजिस्ट्री)

फेंकता
आईओएक्सेप्शन

सार्वजनिक स्थैतिक GPUOptions. प्रायोगिक पार्सFrom (com.google.protobuf.ByteString डेटा, com.google.protobuf.ExtensionRegistryLite एक्सटेंशनरजिस्ट्री)

फेंकता
अमान्यप्रोटोकॉलबफ़रएक्सेप्शन

सार्वजनिक स्थैतिक पार्सर ()

सार्वजनिक GPUOptions.Experimental.Builder toBuilder ()

सार्वजनिक शून्य राइटटू (com.google.protobuf.CodedOutputStream आउटपुट)

फेंकता
आईओएक्सेप्शन