[][src]Module tensorflow_proto::tensorflow

Modules

api_def
attr_value
autotune_result
boosted_trees
bundle_header_proto
call_traceback
collection_def
commit_id
config_proto
contrib
control_flow_context_def
cost_graph_def
cpp_shape_inference_result
data
decision_trees
eager
entry_value
error
event
event_reply
feature
feature_configuration
function_def
gpu_options
graph_debug_info
graph_transfer_info
grpc
h_param_def
kernel_def
log_message
meta_graph_def
node_def
op_def
op_info
op_performance
optimizer_options
profiler
remote_fused_graph_execute_info
replay_op
resource_handle_proto
rewriter_config
run_metadata
run_options
saved_object
saver_def
serving
session_log
structured_value
summary
summary_metadata
tensor_info
tensor_shape_proto
tensor_slice_proto
tensor_tracer_report
tensorforest
tensorrt
test
test_results
tf2xla
tfprof
tpu
trackable_object_graph
type_spec_proto
verifier_config
xla_auto_clustering_summary
xla_optimization_remark

Structs

AllocationDescription
AllocationRecord

An allocation/de-allocation operation performed by the allocator.

AllocatorMemoryUsed
ApiDef

Used to specify and override the default API & behavior in the generated code for client languages, from what you would get from the OpDef alone. There will be a set of ApiDefs that are common to all client languages, and another set per client language. The per-client-language ApiDefs will inherit values from the common ApiDefs which it can either replace or modify.

ApiDefs
AssetFileDef

An asset file def for a single file or a set of sharded files with the same name.

AttrValue

Protocol buffer representing the value for an attr used to configure an Op. Comment indicates the corresponding attr type. Only the field matching the attr type may be filled.

AutoParallelOptions
AutotuneResult
AutotuningLog
AvailableDeviceInfo

Matches DeviceAttributes

BenchmarkEntries
BenchmarkEntry

Each unit test or benchmark in a test or benchmark run provides some set of information. Here we provide some reasonable keys one would expect to see, with optional key/value pairs for things we haven't considered.

BigQueryTablePartition

This proto specifies a table partition in BigQuery.

BuildConfiguration
BundleEntryProto

Describes the metadata related to a checkpointed tensor.

BundleHeaderProto

Special header that is associated with a bundle.

BytesList

Containers to hold repeated fundamental values.

CallTraceback

Data on the traceback of a debugged call, e.g., a Session.run() call, or the execution of an eager operation.

CallableOptions

Defines a subgraph in another GraphDef as a set of feed points and nodes to be fetched or executed.

Channel
CheckpointState

Protocol buffer representing the checkpoint state.

CleanupAllRequest
CleanupAllResponse
CleanupGraphRequest
CleanupGraphResponse
CloseSessionRequest
CloseSessionResponse
ClusterDef

Defines a TensorFlow cluster as a set of jobs.

CollectionDef

CollectionDef should cover most collections. To add a user-defined collection, do one of the following:

CommitId
CompleteGroupRequest

Supplies one or more device names as members of the group identified by group_key. Service will respond when all group_size devices become known. All devices in group must have same type.

CompleteGroupResponse

Gives the complete membership of the group identified by group_key.

CompleteInstanceRequest

Supplies data about one collective op belonging to the instance identified by instance_key. Service will respond when all group_size ops have become known. Most of the data being sent is for correctness checking, to ensure that all ops in the instance share common attributes.

CompleteInstanceResponse

Confirms that every op in the instance has consistently declared itself. Also gives the source_rank in case of broadcast.

ComputeCapability
CondContextDef

Protocol buffer representing a CondContext object.

ConfigProto

Session configuration parameters. The system picks appropriate values for fields that are not set.

ControlFlowContextDef

Container for any kind of control flow context. Any other control flow contexts that are added below should also be added here.

ConvolutionProto

A convolution. Currently it's only used for logging. In the future, we may want to use it in the API as well.

CostGraphDef
CppShapeInferenceInputsNeeded
CppShapeInferenceResult
CpuInfo
CreateSessionRequest
CreateSessionResponse
CreateWorkerSessionRequest
CreateWorkerSessionResponse
CriticalSectionDef

Protocol buffer representing a CriticalSection.

CriticalSectionExecutionDef

Protocol buffer representing a CriticalSection execution.

CudnnVersion
DebugOptions

Options for initializing DebuggerState in TensorFlow Debugger (tfdbg).

DebugTensorWatch

Option for watching a node in TensorFlow Debugger (tfdbg).

DebuggedSourceFile
DebuggedSourceFiles
DeleteWorkerSessionRequest
DeleteWorkerSessionResponse
DeregisterGraphRequest
DeregisterGraphResponse

TODO(mrry): Optionally add summary stats for the graph.

DeviceAttributes
DeviceLocality
DeviceProperties
DeviceStepStats
DictValue

Represents a Python dict keyed by str. The comment on Unicode from Value.string_value applies analogously.

EmbeddingInfo
EntryValue
EnumProfileSessionsAndToolsRequest
EnumProfileSessionsAndToolsResponse
ErrorStatusProto
Event

Protocol buffer representing an event that happened during the execution of a Brain model.

EventReply

Reply message from EventListener to the client, i.e., to the source of the Event protocol buffers, e.g., debug ops inserted by a debugged runtime to a TensorFlow graph being executed.

Example
ExampleParserConfiguration
ExampleWithExtras

This message is parallel to Example, but with additional fields to test unknown fields handling in example_proto_fast_parsing_test.cc.

ExecutorOpts

Options specific to the execution of a single step.

ExtendSessionRequest
ExtendSessionResponse

TODO(mrry): Return something about the operation?

Feature

Containers for non-sequential data.

FeatureConfiguration
FeatureList

Containers for sequential data.

FeatureLists
Features
FixedLenFeatureProto
FloatList
FunctionDef

A function can be instantiated when the runtime can bind every attr with a value. When a GraphDef has a call to a function, it must have binding for every attr defined in the signature.

FunctionDefLibrary

A library is a set of named functions.

FunctionSpec

Represents FunctionSpec used in Function. This represents a function that has been wrapped as a TensorFlow Function.

GetRemoteAddressRequest
GetRemoteAddressResponse
GetStatusRequest
GetStatusResponse
GetStepSequenceRequest

Request for next agreed-upon step_id for the specified graph_keys. This is used to enable multiple graphs containing nodes from a common collective instance to coordinate using the same step_ids.

GetStepSequenceResponse

Next valid step_ids for one or more graph_keys.

GpuInfo
GpuOptions
GradientDef

GradientDef defines the gradient function of a function defined in a function library.

GraphDebugInfo
GraphDef

Represents the graph of operations

GraphOptions
GraphTransferConstNodeInfo
GraphTransferGraphInputNodeInfo
GraphTransferGraphOutputNodeInfo
GraphTransferInfo

Protocol buffer representing a handle to a tensorflow resource. Handles are not valid across executions, but can be serialized back and forth from within a single run.

GraphTransferNodeInfo
GraphTransferNodeInput
GraphTransferNodeInputInfo
GraphTransferNodeOutputInfo
HParamDef

Protocol buffer holding hyper parameters. Examples of hyper parameters: learning_rate = 0.1, num_hidden_units = 100, activations = ['relu', 'tanh']

HistogramProto

Serialization format for histogram module in core/lib/histogram/histogram.h

Int64List
InterconnectLink
JobDef

Defines a single job in a TensorFlow cluster.

KernelDef
KernelList

A collection of KernelDefs

LabeledStepStats
ListDevicesRequest
ListDevicesResponse
ListValue

Represents a Python list.

LocalLinks
LogMessage

Protocol buffer used for logging messages to the events file.

LogNormalDistribution
LoggingRequest

Out-of-band request to begin or end logging, or to retrieve logs for particular steps.

LoggingResponse
MachineConfiguration
MakeCallableRequest
MakeCallableResponse
MarkRecvFinishedRequest

Message for managing the response cache maintained on the sender side. Currently only used by the gRPC worker service.

MarkRecvFinishedResponse
MemmappedFileSystemDirectory

A directory of regions in a memmapped file.

MemmappedFileSystemDirectoryElement

A message that describes one region of memmapped file.

MemoryInfo
MemoryLogRawAllocation
MemoryLogRawDeallocation
MemoryLogStep
MemoryLogTensorAllocation
MemoryLogTensorDeallocation
MemoryLogTensorOutput
MemoryRegion
MemoryStats

For memory tracking.

MetaGraphDef

NOTE: This protocol buffer is evolving, and will go through revisions in the coming months.

MetricEntry
MonitorRequest
MonitorResponse
MpiRecvTensorResponse
NameAttrList

A list of attr names and their values. The whole list is attached with a string name. E.g., MatMul[T=float].

NamedDevice
NamedTensorProto

A pair of tensor name and tensor values.

NamedTupleValue

Represents Python's namedtuple.

NewProfileSessionRequest
NewProfileSessionResponse
NewReplaySession

Records the creation of a new replay session. We record the device listing here to capture the state of the cluster.

NodeDef
NodeExecStats

Time/size stats recorded for a single execution of a graph node.

NodeOutput

Output sizes recorded for a single execution of a graph node.

NoneValue

Represents None.

NormalDistribution
OpDef

Defines an operation. A NodeDef in a GraphDef specifies an Op by using the "op" field which should match the name of a OpDef. LINT.IfChange

OpDeprecation

Information about version-dependent deprecation of an op

OpInfo

Description of an operation as well as the parameters expected to impact its performance.

OpList

A collection of OpDefs

OpPerformance

Performance data for tensorflow operations

OpPerformanceList

A collection of OpPerformance data points.

OptimizerOptions

Options passed to the graph optimizer

PairValue

Represents a (key, value) pair.

PartialRunSetupRequest
PartialRunSetupResponse
PlatformInfo
ProfileOptions
ProfileRequest
ProfileResponse
ProfileSessionDataRequest
ProfileSessionDataResponse
ProfileSessionInfo
ProfileToolData
ProjectorConfig
QueueRunnerDef

Protocol buffer representing a QueueRunner.

ReaderBaseState

For serializing and restoring the state of ReaderBase, see reader_base.h for details.

RecvBufRequest

Use of the fields below may vary by implementation. For example the buf_ptr and num_bytes may be set only for local operations and not sent on the wire, or only sent on the wire in one direction.

RecvBufRespExtra

Extra data needed on a non-RDMA RecvBufResponse.

RecvBufResponse

Use of the fields below may vary by implementation. Comments give intended use.

RecvTensorRequest
RecvTensorResponse
RegisterGraphRequest
RegisterGraphResponse
ReleaseCallableRequest
ReleaseCallableResponse
RemoteFusedGraphExecuteInfo

Protocol buffer representing a handle to a tensorflow resource. Handles are not valid across executions, but can be serialized back and forth from within a single run.

RemoteMemoryRegion
ReplayOp
ResetRequest

Reset() allows misbehaving or slow sessions to be aborted and closed, and causes their resources eventually to be released. Reset() does not wait for the computations in old sessions to cease; it merely starts the process of tearing them down. However, if a new session is started after a Reset(), the new session is isolated from changes that old sessions (started prior to the Reset()) may continue to make to resources, provided all those resources are in containers listed in "containers".

ResetResponse
ResourceHandleProto

Protocol buffer representing a handle to a tensorflow resource. Handles are not valid across executions, but can be serialized back and forth from within a single run.

RewriterConfig

Graph rewriting is experimental and subject to change, not covered by any API stability guarantees.

RpcOptions
RunCallableRequest
RunCallableResponse
RunConfiguration

Run-specific items such as arguments to the test / benchmark.

RunGraphRequest
RunGraphResponse
RunMetadata

Metadata output (i.e., non-Tensor) for a single Run() call.

RunOptions

Options for a single Run() call.

RunStepRequest
RunStepResponse
SaveSliceInfoDef
SavedAsset

A SavedAsset points to an asset in the MetaGraph.

SavedBareConcreteFunction
SavedConcreteFunction

Stores low-level information about a concrete function. Referenced in either a SavedFunction or a SavedBareConcreteFunction.

SavedConstant
SavedFunction

A function with multiple signatures, possibly with non-Tensor arguments.

SavedModel

SavedModel is the high level serialization format for TensorFlow Models. See [todo: doc links, similar to session_bundle] for more information.

SavedObject
SavedObjectGraph
SavedResource

A SavedResource represents a TF object that holds state during its lifetime. An object of this type can have a reference to a: create_resource() and an initialize() function.

SavedSlice

Saved tensor slice: it stores the name of the tensors, the slice, and the raw data.

SavedSliceMeta

Metadata describing the set of slices of the same tensor saved in a checkpoint file.

SavedTensorSliceMeta

Metadata describing the set of tensor slices saved in a checkpoint file. It is always stored at the beginning of each checkpoint file.

SavedTensorSlices

Each record in a v3 checkpoint file is a serialized SavedTensorSlices message.

SavedUserObject

A SavedUserObject is an object (in the object-oriented language of the TensorFlow program) of some user- or framework-defined class other than those handled specifically by the other kinds of SavedObjects.

SavedVariable

Represents a Variable that is initialized by loading the contents from the checkpoint.

SaverDef

Protocol buffer representing the configuration of a Saver.

ScopedAllocatorOptions
SequenceExample
ServerDef

Defines the configuration of a single TensorFlow server.

SessionInfo

Description of the session when an op is run.

SessionLog

Protocol buffer used for logging session state.

SessionMetadata

Metadata about the session.

SignatureDef

SignatureDef defines the signature of a computation supported by a TensorFlow graph.

SpriteMetadata
StepSequence
StepStats
StructuredValue

StructuredValue represents a dynamically typed value representing various data structures that are inspired by Python data structures typically used in TensorFlow functions as inputs and outputs.

Summary

A Summary is a set of named values to be displayed by the visualizer.

SummaryDescription

Metadata associated with a series of Summary data

SummaryMetadata

A SummaryMetadata encapsulates information on which plugins are able to make use of a certain summary value.

TaggedRunMetadata

For logging the metadata output for a single session.run() call.

TensorConnection

Defines a connection between two tensors in a GraphDef.

TensorDescription
TensorInfo

Information about a Tensor necessary for feeding or retrieval.

TensorProto

Protocol buffer representing a tensor.

TensorShapeProto

Dimensions of a tensor.

TensorSliceProto

Can only be interpreted if you know the corresponding TensorShape.

TensorSpecProto

A protobuf to tf.TensorSpec.

TensorTracerReport

Tensor Tracer Report proto gives information about the trace including:

TestResults

The output of one benchmark / test run. Each run contains a list of tests or benchmarks, stored as BenchmarkEntry messages.

ThreadPoolOptionProto
ToolRequestOptions
TraceOpts
TracingRequest

Out-of-band request to configure distributed tracing.

TracingResponse
TrackableObjectGraph
TupleValue

Represents a Python tuple.

TypeSpecProto

Represents a tf.TypeSpec

ValuesDef

Protocol buffer representing the values in ControlFlowContext.

VarLenFeatureProto
VariableDef

Protocol buffer representing a Variable.

VariantTensorDataProto

Protocol buffer representing the serialization format of DT_VARIANT tensors.

VerifierConfig

The config for graph verifiers.

VersionDef

Version information for a piece of serialized data

WatchdogConfig
WhileContextDef

Protocol buffer representing a WhileContext object.

WorkerHeartbeatRequest
WorkerHeartbeatResponse
XlaAutoClusteringActivity

Listeners listening for auto clustering events get messages of this type.

XlaAutoClusteringSummary

Summarizes the results of auto-clustering a TensorFlow graph.

XlaJitCompilationActivity

Listeners listening for JIT compilation events get messages of this type. Each instance of XlaJitCompilationActivity corresponds to a single compilation of a single XLA cluster. E.g. if a graph has two clusters, A and B, and A is compiled 5 times and B is compiled 2 times then we will generate 7 instances of XlaJitCompilationActivity.

XlaOptimizationRemark

LINT.IfChange

Enums

DataType

(== suppress_warning documentation-presence ==) LINT.IfChange

VariableAggregation

Indicates how a distributed variable will be aggregated.

VariableSynchronization

Indicates when a distributed variable will be synced.

WorkerHealth

Current health status of a worker.

WorkerShutdownMode

Indicates the behavior of the worker when an internal error or shutdown signal is received.