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 obj) |
| 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 | |
| GPUOptions.प्रायोगिक | |
| अंतिम स्थिर com.google.protobuf.Descriptors.Descriptor | |
| 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. |
| int यहाँ | |
| 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 | |
| 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 | |
| स्थैतिक GPUOptions.Experimental.Builder | न्यूबिल्डर ( GPUOptions.प्रायोगिक प्रोटोटाइप) |
| स्थैतिक GPUOptions.Experimental.Builder | नयाबिल्डर () |
| GPUOptions.प्रायोगिक.बिल्डर | |
| 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 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
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. 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
सार्वजनिक शून्य राइटटू (com.google.protobuf.CodedOutputStream आउटपुट)
फेंकता
| आईओएक्सेप्शन |
|---|