Package org.platanios.tensorflow.proto
Interface GPUOptions.ExperimentalOrBuilder
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- All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder
,com.google.protobuf.MessageOrBuilder
- All Known Implementing Classes:
GPUOptions.Experimental
,GPUOptions.Experimental.Builder
- Enclosing class:
- GPUOptions
public static interface GPUOptions.ExperimentalOrBuilder extends com.google.protobuf.MessageOrBuilder
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Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description java.lang.String
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.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.int
getKernelTrackerMaxInterval()
Parameters for GPUKernelTracker.int
getKernelTrackerMaxPending()
If kernel_tracker_max_pending > 0 then no more than this many tracking events can be outstanding at a time.int
getNumDevToDevCopyStreams()
If > 1, the number of device-to-device copy streams to create for each GPUDevice.boolean
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.boolean
getUseUnifiedMemory()
If true, uses CUDA unified memory for memory allocations.GPUOptions.Experimental.VirtualDevices
getVirtualDevices(int index)
The multi virtual device settings.int
getVirtualDevicesCount()
The multi virtual device settings.java.util.List<GPUOptions.Experimental.VirtualDevices>
getVirtualDevicesList()
The multi virtual device settings.GPUOptions.Experimental.VirtualDevicesOrBuilder
getVirtualDevicesOrBuilder(int index)
The multi virtual device settings.java.util.List<? extends GPUOptions.Experimental.VirtualDevicesOrBuilder>
getVirtualDevicesOrBuilderList()
The multi virtual device settings.-
Methods inherited from interface com.google.protobuf.MessageOrBuilder
findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof
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Method Detail
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getVirtualDevicesList
java.util.List<GPUOptions.Experimental.VirtualDevices> getVirtualDevicesList()
The multi virtual device settings. If empty (not set), it will create single virtual device on each visible GPU, according to the settings in "visible_device_list" above. Otherwise, the number of elements in the list must be the same as the number of visible GPUs (after "visible_device_list" filtering if it is set), and the string represented device names (e.g. /device:GPU:<id>) will refer to the virtual devices and have the <id> field assigned sequentially starting from 0, according to the order they appear in this list and the "memory_limit" list inside each element. For example, visible_device_list = "1,0" virtual_devices { memory_limit: 1GB memory_limit: 2GB } virtual_devices {} will create three virtual devices as: /device:GPU:0 -> visible GPU 1 with 1GB memory /device:GPU:1 -> visible GPU 1 with 2GB memory /device:GPU:2 -> visible GPU 0 with all available memory NOTE: 1. It's invalid to set both this and "per_process_gpu_memory_fraction" at the same time. 2. Currently this setting is per-process, not per-session. Using different settings in different sessions within same process will result in undefined behavior.
repeated .org.platanios.tensorflow.proto.GPUOptions.Experimental.VirtualDevices virtual_devices = 1;
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getVirtualDevices
GPUOptions.Experimental.VirtualDevices getVirtualDevices(int index)
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 .org.platanios.tensorflow.proto.GPUOptions.Experimental.VirtualDevices virtual_devices = 1;
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getVirtualDevicesCount
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 .org.platanios.tensorflow.proto.GPUOptions.Experimental.VirtualDevices virtual_devices = 1;
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getVirtualDevicesOrBuilderList
java.util.List<? extends GPUOptions.Experimental.VirtualDevicesOrBuilder> getVirtualDevicesOrBuilderList()
The multi virtual device settings. If empty (not set), it will create single virtual device on each visible GPU, according to the settings in "visible_device_list" above. Otherwise, the number of elements in the list must be the same as the number of visible GPUs (after "visible_device_list" filtering if it is set), and the string represented device names (e.g. /device:GPU:<id>) will refer to the virtual devices and have the <id> field assigned sequentially starting from 0, according to the order they appear in this list and the "memory_limit" list inside each element. For example, visible_device_list = "1,0" virtual_devices { memory_limit: 1GB memory_limit: 2GB } virtual_devices {} will create three virtual devices as: /device:GPU:0 -> visible GPU 1 with 1GB memory /device:GPU:1 -> visible GPU 1 with 2GB memory /device:GPU:2 -> visible GPU 0 with all available memory NOTE: 1. It's invalid to set both this and "per_process_gpu_memory_fraction" at the same time. 2. Currently this setting is per-process, not per-session. Using different settings in different sessions within same process will result in undefined behavior.
repeated .org.platanios.tensorflow.proto.GPUOptions.Experimental.VirtualDevices virtual_devices = 1;
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getVirtualDevicesOrBuilder
GPUOptions.Experimental.VirtualDevicesOrBuilder getVirtualDevicesOrBuilder(int index)
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 .org.platanios.tensorflow.proto.GPUOptions.Experimental.VirtualDevices virtual_devices = 1;
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getUseUnifiedMemory
boolean 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;
- Returns:
- The useUnifiedMemory.
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getNumDevToDevCopyStreams
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;
- Returns:
- The numDevToDevCopyStreams.
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getCollectiveRingOrder
java.lang.String getCollectiveRingOrder()
If non-empty, defines a good GPU ring order on a single worker based on device interconnect. This assumes that all workers have the same GPU topology. Specify as a comma-separated string, e.g. "3,2,1,0,7,6,5,4". This ring order is used by the RingReducer implementation of CollectiveReduce, and serves as an override to automatic ring order generation in OrderTaskDeviceMap() during CollectiveParam resolution.
string collective_ring_order = 4;
- Returns:
- The collectiveRingOrder.
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getCollectiveRingOrderBytes
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;
- Returns:
- The bytes for collectiveRingOrder.
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getTimestampedAllocator
boolean 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;
- Returns:
- The timestampedAllocator.
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getKernelTrackerMaxInterval
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;
- Returns:
- The kernelTrackerMaxInterval.
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getKernelTrackerMaxBytes
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;
- Returns:
- The kernelTrackerMaxBytes.
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getKernelTrackerMaxPending
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;
- Returns:
- The kernelTrackerMaxPending.
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