ConfigProto

public final class ConfigProto 

 Session configuration parameters.
 The system picks appropriate values for fields that are not set.
Protobuf type tensorflow.ConfigProto

Nested Classes

class ConfigProto.Builder 
 Session configuration parameters. 
class ConfigProto.Experimental 
 Everything inside Experimental is subject to change and is not subject
 to API stability guarantees in
 https://www.tensorflow.org/guide/version_compat. 
interface ConfigProto.ExperimentalOrBuilder  

Constants

int ALLOW_SOFT_PLACEMENT_FIELD_NUMBER
int CLUSTER_DEF_FIELD_NUMBER
int DEVICE_COUNT_FIELD_NUMBER
int DEVICE_FILTERS_FIELD_NUMBER
int EXPERIMENTAL_FIELD_NUMBER
int GPU_OPTIONS_FIELD_NUMBER
int GRAPH_OPTIONS_FIELD_NUMBER
int INTER_OP_PARALLELISM_THREADS_FIELD_NUMBER
int INTRA_OP_PARALLELISM_THREADS_FIELD_NUMBER
int ISOLATE_SESSION_STATE_FIELD_NUMBER
int LOG_DEVICE_PLACEMENT_FIELD_NUMBER
int OPERATION_TIMEOUT_IN_MS_FIELD_NUMBER
int PLACEMENT_PERIOD_FIELD_NUMBER
int RPC_OPTIONS_FIELD_NUMBER
int SESSION_INTER_OP_THREAD_POOL_FIELD_NUMBER
int SHARE_CLUSTER_DEVICES_IN_SESSION_FIELD_NUMBER
int USE_PER_SESSION_THREADS_FIELD_NUMBER

Public Methods

boolean
containsDeviceCount(String key) 
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
boolean
equals(Object obj)
boolean
getAllowSoftPlacement() 
 Whether soft placement is allowed.
ClusterDef
getClusterDef() 
 Optional list of all workers to use in this session.
ClusterDefOrBuilder
getClusterDefOrBuilder() 
 Optional list of all workers to use in this session.
static ConfigProto
getDefaultInstance()
ConfigProto
getDefaultInstanceForType()
final static com.google.protobuf.Descriptors.Descriptor
getDescriptor()
Map<String, Integer>
getDeviceCount()
Use getDeviceCountMap() instead.
int
getDeviceCountCount() 
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
Map<String, Integer>
getDeviceCountMap() 
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
int
getDeviceCountOrDefault(String key, int defaultValue) 
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
int
getDeviceCountOrThrow(String key) 
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
String
getDeviceFilters(int index) 
 When any filters are present sessions will ignore all devices which do not
 match the filters.
com.google.protobuf.ByteString
getDeviceFiltersBytes(int index) 
 When any filters are present sessions will ignore all devices which do not
 match the filters.
int
getDeviceFiltersCount() 
 When any filters are present sessions will ignore all devices which do not
 match the filters.
com.google.protobuf.ProtocolStringList
getDeviceFiltersList() 
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Experimental
getExperimental()
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.ExperimentalOrBuilder
getExperimentalOrBuilder()
.tensorflow.ConfigProto.Experimental experimental = 16;
GPUOptions
getGpuOptions() 
 Options that apply to all GPUs.
GPUOptionsOrBuilder
getGpuOptionsOrBuilder() 
 Options that apply to all GPUs.
GraphOptions
getGraphOptions() 
 Options that apply to all graphs.
GraphOptionsOrBuilder
getGraphOptionsOrBuilder() 
 Options that apply to all graphs.
int
getInterOpParallelismThreads() 
 Nodes that perform blocking operations are enqueued on a pool of
 inter_op_parallelism_threads available in each process.
int
getIntraOpParallelismThreads() 
 The execution of an individual op (for some op types) can be
 parallelized on a pool of intra_op_parallelism_threads.
boolean
getIsolateSessionState() 
 If true, any resources such as Variables used in the session will not be
 shared with other sessions.
boolean
getLogDevicePlacement() 
 Whether device placements should be logged.
long
getOperationTimeoutInMs() 
 Global timeout for all blocking operations in this session.
getParserForType()
int
getPlacementPeriod() 
 Assignment of Nodes to Devices is recomputed every placement_period
 steps until the system warms up (at which point the recomputation
 typically slows down automatically).
RPCOptions
getRpcOptions() 
 Options that apply when this session uses the distributed runtime.
RPCOptionsOrBuilder
getRpcOptionsOrBuilder() 
 Options that apply when this session uses the distributed runtime.
int
getSerializedSize()
ThreadPoolOptionProto
getSessionInterOpThreadPool(int index) 
 This option is experimental - it may be replaced with a different mechanism
 in the future.
int
getSessionInterOpThreadPoolCount() 
 This option is experimental - it may be replaced with a different mechanism
 in the future.
List<ThreadPoolOptionProto>
getSessionInterOpThreadPoolList() 
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ThreadPoolOptionProtoOrBuilder
getSessionInterOpThreadPoolOrBuilder(int index) 
 This option is experimental - it may be replaced with a different mechanism
 in the future.
List<? extends ThreadPoolOptionProtoOrBuilder>
getSessionInterOpThreadPoolOrBuilderList() 
 This option is experimental - it may be replaced with a different mechanism
 in the future.
boolean
getShareClusterDevicesInSession() 
 When true, WorkerSessions are created with device attributes from the
 full cluster.
final com.google.protobuf.UnknownFieldSet
getUnknownFields()
boolean
getUsePerSessionThreads() 
 If true, use a new set of threads for this session rather than the global
 pool of threads.
boolean
hasClusterDef() 
 Optional list of all workers to use in this session.
boolean
hasExperimental()
.tensorflow.ConfigProto.Experimental experimental = 16;
boolean
hasGpuOptions() 
 Options that apply to all GPUs.
boolean
hasGraphOptions() 
 Options that apply to all graphs.
boolean
hasRpcOptions() 
 Options that apply when this session uses the distributed runtime.
int
hashCode()
final boolean
isInitialized()
static ConfigProto.Builder
newBuilder()
static ConfigProto.Builder
newBuilder(ConfigProto prototype)
ConfigProto.Builder
newBuilderForType()
static ConfigProto
parseDelimitedFrom(InputStream input)
static ConfigProto
parseDelimitedFrom(InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
static ConfigProto
parseFrom(ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
static ConfigProto
parseFrom(com.google.protobuf.CodedInputStream input)
static ConfigProto
parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
static ConfigProto
parseFrom(ByteBuffer data)
static ConfigProto
parseFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
static ConfigProto
parseFrom(com.google.protobuf.ByteString data)
static ConfigProto
parseFrom(InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
static ConfigProto
parseFrom(com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
static
parser()
ConfigProto.Builder
toBuilder()
void
writeTo(com.google.protobuf.CodedOutputStream output)

Inherited Methods

Constants

public static final int ALLOW_SOFT_PLACEMENT_FIELD_NUMBER

Constant Value: 7

public static final int CLUSTER_DEF_FIELD_NUMBER

Constant Value: 14

public static final int DEVICE_COUNT_FIELD_NUMBER

Constant Value: 1

public static final int DEVICE_FILTERS_FIELD_NUMBER

Constant Value: 4

public static final int EXPERIMENTAL_FIELD_NUMBER

Constant Value: 16

public static final int GPU_OPTIONS_FIELD_NUMBER

Constant Value: 6

public static final int GRAPH_OPTIONS_FIELD_NUMBER

Constant Value: 10

public static final int INTER_OP_PARALLELISM_THREADS_FIELD_NUMBER

Constant Value: 5

public static final int INTRA_OP_PARALLELISM_THREADS_FIELD_NUMBER

Constant Value: 2

public static final int ISOLATE_SESSION_STATE_FIELD_NUMBER

Constant Value: 15

public static final int LOG_DEVICE_PLACEMENT_FIELD_NUMBER

Constant Value: 8

public static final int OPERATION_TIMEOUT_IN_MS_FIELD_NUMBER

Constant Value: 11

public static final int PLACEMENT_PERIOD_FIELD_NUMBER

Constant Value: 3

public static final int RPC_OPTIONS_FIELD_NUMBER

Constant Value: 13

public static final int SESSION_INTER_OP_THREAD_POOL_FIELD_NUMBER

Constant Value: 12

public static final int SHARE_CLUSTER_DEVICES_IN_SESSION_FIELD_NUMBER

Constant Value: 17

public static final int USE_PER_SESSION_THREADS_FIELD_NUMBER

Constant Value: 9

Public Methods

public boolean containsDeviceCount (String key) 

 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.  If a particular device
 type is not found in the map, the system picks an appropriate
 number.
map<string, int32> device_count = 1;

public boolean equals (Object obj)

public boolean getAllowSoftPlacement () 

 Whether soft placement is allowed. If allow_soft_placement is true,
 an op will be placed on CPU if
   1. there's no GPU implementation for the OP
 or
   2. no GPU devices are known or registered
 or
   3. need to co-locate with reftype input(s) which are from CPU.
bool allow_soft_placement = 7;

public ClusterDef getClusterDef () 

 Optional list of all workers to use in this session.
.tensorflow.ClusterDef cluster_def = 14;

public ClusterDefOrBuilder getClusterDefOrBuilder () 

 Optional list of all workers to use in this session.
.tensorflow.ClusterDef cluster_def = 14;

public static ConfigProto getDefaultInstance ()

public ConfigProto getDefaultInstanceForType ()

public static final com.google.protobuf.Descriptors.Descriptor getDescriptor ()

public Map<String, Integer> getDeviceCount ()

Use getDeviceCountMap() instead.

public int getDeviceCountCount () 

 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.  If a particular device
 type is not found in the map, the system picks an appropriate
 number.
map<string, int32> device_count = 1;

public Map<String, Integer> getDeviceCountMap () 

 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.  If a particular device
 type is not found in the map, the system picks an appropriate
 number.
map<string, int32> device_count = 1;

public int getDeviceCountOrDefault (String key, int defaultValue) 

 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.  If a particular device
 type is not found in the map, the system picks an appropriate
 number.
map<string, int32> device_count = 1;

public int getDeviceCountOrThrow (String key) 

 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.  If a particular device
 type is not found in the map, the system picks an appropriate
 number.
map<string, int32> device_count = 1;

public String getDeviceFilters (int index) 

 When any filters are present sessions will ignore all devices which do not
 match the filters. Each filter can be partially specified, e.g. "/job:ps"
 "/job:worker/replica:3", etc.
repeated string device_filters = 4;

public com.google.protobuf.ByteString getDeviceFiltersBytes (int index) 

 When any filters are present sessions will ignore all devices which do not
 match the filters. Each filter can be partially specified, e.g. "/job:ps"
 "/job:worker/replica:3", etc.
repeated string device_filters = 4;

public int getDeviceFiltersCount () 

 When any filters are present sessions will ignore all devices which do not
 match the filters. Each filter can be partially specified, e.g. "/job:ps"
 "/job:worker/replica:3", etc.
repeated string device_filters = 4;

public com.google.protobuf.ProtocolStringList getDeviceFiltersList () 

 When any filters are present sessions will ignore all devices which do not
 match the filters. Each filter can be partially specified, e.g. "/job:ps"
 "/job:worker/replica:3", etc.
repeated string device_filters = 4;

public ConfigProto.Experimental getExperimental ()

.tensorflow.ConfigProto.Experimental experimental = 16;

public ConfigProto.ExperimentalOrBuilder getExperimentalOrBuilder ()

.tensorflow.ConfigProto.Experimental experimental = 16;

public GPUOptions getGpuOptions () 

 Options that apply to all GPUs.
.tensorflow.GPUOptions gpu_options = 6;

public GPUOptionsOrBuilder getGpuOptionsOrBuilder () 

 Options that apply to all GPUs.
.tensorflow.GPUOptions gpu_options = 6;

public GraphOptions getGraphOptions () 

 Options that apply to all graphs.
.tensorflow.GraphOptions graph_options = 10;

public GraphOptionsOrBuilder getGraphOptionsOrBuilder () 

 Options that apply to all graphs.
.tensorflow.GraphOptions graph_options = 10;

public int getInterOpParallelismThreads () 

 Nodes that perform blocking operations are enqueued on a pool of
 inter_op_parallelism_threads available in each process.
 0 means the system picks an appropriate number.
 Negative means all operations are performed in caller's thread.
 Note that the first Session created in the process sets the
 number of threads for all future sessions unless use_per_session_threads is
 true or session_inter_op_thread_pool is configured.
int32 inter_op_parallelism_threads = 5;

public int getIntraOpParallelismThreads () 

 The execution of an individual op (for some op types) can be
 parallelized on a pool of intra_op_parallelism_threads.
 0 means the system picks an appropriate number.
 If you create an ordinary session, e.g., from Python or C++,
 then there is exactly one intra op thread pool per process.
 The first session created determines the number of threads in this pool.
 All subsequent sessions reuse/share this one global pool.
 There are notable exceptions to the default behavior describe above:
 1. There is an environment variable  for overriding this thread pool,
    named TF_OVERRIDE_GLOBAL_THREADPOOL.
 2. When connecting to a server, such as a remote `tf.train.Server`
    instance, then this option will be ignored altogether.
int32 intra_op_parallelism_threads = 2;

public boolean getIsolateSessionState () 

 If true, any resources such as Variables used in the session will not be
 shared with other sessions. However, when clusterspec propagation is
 enabled, this field is ignored and sessions are always isolated.
bool isolate_session_state = 15;

public boolean getLogDevicePlacement () 

 Whether device placements should be logged.
bool log_device_placement = 8;

public long getOperationTimeoutInMs () 

 Global timeout for all blocking operations in this session.  If non-zero,
 and not overridden on a per-operation basis, this value will be used as the
 deadline for all blocking operations.
int64 operation_timeout_in_ms = 11;

public getParserForType ()

public int getPlacementPeriod () 

 Assignment of Nodes to Devices is recomputed every placement_period
 steps until the system warms up (at which point the recomputation
 typically slows down automatically).
int32 placement_period = 3;

public RPCOptions getRpcOptions () 

 Options that apply when this session uses the distributed runtime.
.tensorflow.RPCOptions rpc_options = 13;

public RPCOptionsOrBuilder getRpcOptionsOrBuilder () 

 Options that apply when this session uses the distributed runtime.
.tensorflow.RPCOptions rpc_options = 13;

public int getSerializedSize ()

public ThreadPoolOptionProto getSessionInterOpThreadPool (int index) 

 This option is experimental - it may be replaced with a different mechanism
 in the future.
 Configures session thread pools. If this is configured, then RunOptions for
 a Run call can select the thread pool to use.
 The intended use is for when some session invocations need to run in a
 background pool limited to a small number of threads:
 - For example, a session may be configured to have one large pool (for
 regular compute) and one small pool (for periodic, low priority work);
 using the small pool is currently the mechanism for limiting the inter-op
 parallelism of the low priority work.  Note that it does not limit the
 parallelism of work spawned by a single op kernel implementation.
 - Using this setting is normally not needed in training, but may help some
 serving use cases.
 - It is also generally recommended to set the global_name field of this
 proto, to avoid creating multiple large pools. It is typically better to
 run the non-low-priority work, even across sessions, in a single large
 pool.
repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12;

public int getSessionInterOpThreadPoolCount () 

 This option is experimental - it may be replaced with a different mechanism
 in the future.
 Configures session thread pools. If this is configured, then RunOptions for
 a Run call can select the thread pool to use.
 The intended use is for when some session invocations need to run in a
 background pool limited to a small number of threads:
 - For example, a session may be configured to have one large pool (for
 regular compute) and one small pool (for periodic, low priority work);
 using the small pool is currently the mechanism for limiting the inter-op
 parallelism of the low priority work.  Note that it does not limit the
 parallelism of work spawned by a single op kernel implementation.
 - Using this setting is normally not needed in training, but may help some
 serving use cases.
 - It is also generally recommended to set the global_name field of this
 proto, to avoid creating multiple large pools. It is typically better to
 run the non-low-priority work, even across sessions, in a single large
 pool.
repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12;

public List<ThreadPoolOptionProto> getSessionInterOpThreadPoolList () 

 This option is experimental - it may be replaced with a different mechanism
 in the future.
 Configures session thread pools. If this is configured, then RunOptions for
 a Run call can select the thread pool to use.
 The intended use is for when some session invocations need to run in a
 background pool limited to a small number of threads:
 - For example, a session may be configured to have one large pool (for
 regular compute) and one small pool (for periodic, low priority work);
 using the small pool is currently the mechanism for limiting the inter-op
 parallelism of the low priority work.  Note that it does not limit the
 parallelism of work spawned by a single op kernel implementation.
 - Using this setting is normally not needed in training, but may help some
 serving use cases.
 - It is also generally recommended to set the global_name field of this
 proto, to avoid creating multiple large pools. It is typically better to
 run the non-low-priority work, even across sessions, in a single large
 pool.
repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12;

public ThreadPoolOptionProtoOrBuilder getSessionInterOpThreadPoolOrBuilder (int index) 

 This option is experimental - it may be replaced with a different mechanism
 in the future.
 Configures session thread pools. If this is configured, then RunOptions for
 a Run call can select the thread pool to use.
 The intended use is for when some session invocations need to run in a
 background pool limited to a small number of threads:
 - For example, a session may be configured to have one large pool (for
 regular compute) and one small pool (for periodic, low priority work);
 using the small pool is currently the mechanism for limiting the inter-op
 parallelism of the low priority work.  Note that it does not limit the
 parallelism of work spawned by a single op kernel implementation.
 - Using this setting is normally not needed in training, but may help some
 serving use cases.
 - It is also generally recommended to set the global_name field of this
 proto, to avoid creating multiple large pools. It is typically better to
 run the non-low-priority work, even across sessions, in a single large
 pool.
repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12;

public List<? extends ThreadPoolOptionProtoOrBuilder> getSessionInterOpThreadPoolOrBuilderList () 

 This option is experimental - it may be replaced with a different mechanism
 in the future.
 Configures session thread pools. If this is configured, then RunOptions for
 a Run call can select the thread pool to use.
 The intended use is for when some session invocations need to run in a
 background pool limited to a small number of threads:
 - For example, a session may be configured to have one large pool (for
 regular compute) and one small pool (for periodic, low priority work);
 using the small pool is currently the mechanism for limiting the inter-op
 parallelism of the low priority work.  Note that it does not limit the
 parallelism of work spawned by a single op kernel implementation.
 - Using this setting is normally not needed in training, but may help some
 serving use cases.
 - It is also generally recommended to set the global_name field of this
 proto, to avoid creating multiple large pools. It is typically better to
 run the non-low-priority work, even across sessions, in a single large
 pool.
repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12;

public boolean getShareClusterDevicesInSession () 

 When true, WorkerSessions are created with device attributes from the
 full cluster.
 This is helpful when a worker wants to partition a graph
 (for example during a PartitionedCallOp).
bool share_cluster_devices_in_session = 17;

public final com.google.protobuf.UnknownFieldSet getUnknownFields ()

public boolean getUsePerSessionThreads () 

 If true, use a new set of threads for this session rather than the global
 pool of threads. Only supported by direct sessions.
 If false, use the global threads created by the first session, or the
 per-session thread pools configured by session_inter_op_thread_pool.
 This option is deprecated. The same effect can be achieved by setting
 session_inter_op_thread_pool to have one element, whose num_threads equals
 inter_op_parallelism_threads.
bool use_per_session_threads = 9;

public boolean hasClusterDef () 

 Optional list of all workers to use in this session.
.tensorflow.ClusterDef cluster_def = 14;

public boolean hasExperimental ()

.tensorflow.ConfigProto.Experimental experimental = 16;

public boolean hasGpuOptions () 

 Options that apply to all GPUs.
.tensorflow.GPUOptions gpu_options = 6;

public boolean hasGraphOptions () 

 Options that apply to all graphs.
.tensorflow.GraphOptions graph_options = 10;

public boolean hasRpcOptions () 

 Options that apply when this session uses the distributed runtime.
.tensorflow.RPCOptions rpc_options = 13;

public int hashCode ()

public final boolean isInitialized ()

public static ConfigProto.Builder newBuilder ()

public static ConfigProto.Builder newBuilder (ConfigProto prototype)

public ConfigProto.Builder newBuilderForType ()

public static ConfigProto parseDelimitedFrom (InputStream input)

Throws
IOException

public static ConfigProto parseDelimitedFrom (InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Throws
IOException

public static ConfigProto parseFrom (ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Throws
InvalidProtocolBufferException

public static ConfigProto parseFrom (com.google.protobuf.CodedInputStream input)

Throws
IOException

public static ConfigProto parseFrom (byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Throws
InvalidProtocolBufferException

public static ConfigProto parseFrom (ByteBuffer data)

Throws
InvalidProtocolBufferException

public static ConfigProto parseFrom (com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Throws
IOException

public static ConfigProto parseFrom (com.google.protobuf.ByteString data)

Throws
InvalidProtocolBufferException

public static ConfigProto parseFrom (InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Throws
IOException

public static ConfigProto parseFrom (com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Throws
InvalidProtocolBufferException

public static parser ()

public ConfigProto.Builder toBuilder ()

public void writeTo (com.google.protobuf.CodedOutputStream output)

Throws
IOException