package tensorflow

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View/edit binary Protocol Buffers messages

Used in: NodeExecStats, TensorDescription

• int64 requested_bytes = 1

  Total number of bytes requested

• int64 allocated_bytes = 2

  Total number of bytes allocated if known

• string allocator_name = 3

  Name of the allocator used

• int64 allocation_id = 4

  Identifier of the allocated buffer if known

• bool has_single_reference = 5

  Set if this tensor only has one remaining reference

• uint64 ptr = 6

  Address of the allocation.

Used in: NodeExecStats

• string allocator_name = 1

• int64 total_bytes = 2

• int64 peak_bytes = 3

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.

Used in: FunctionDef.Node, KernelDef.AttrConstraint, NameAttrList, NodeDef, OpDef.AttrDef

• oneof value

  • bytes s = 2

    "string"

  • int64 i = 3

    "int"

  • float f = 4

    "float"

  • bool b = 5

    "bool"

  • DataType type = 6

    "type"

  • TensorShapeProto shape = 7

    "shape"

  • TensorProto tensor = 8

    "tensor"

  • AttrValue.ListValue list = 1

    any "list(...)"

  • NameAttrList func = 10

    "func" represents a function. func.name is a function's name or a primitive op's name. func.attr.first is the name of an attr defined for that function. func.attr.second is the value for that attr in the instantiation.

  • string placeholder = 9

    This is a placeholder only used in nodes defined inside a function. It indicates the attr value will be supplied when the function is instantiated. For example, let us suppose a node "N" in function "FN". "N" has an attr "A" with value placeholder = "foo". When FN is instantiated with attr "foo" set to "bar", the instantiated node N's attr A will have been given the value "bar".

Used in: AttrValue

• repeated bytes s = 2

  "list(string)"

• repeated int64 i = 3

  "list(int)"

• repeated float f = 4

  "list(float)"

• repeated bool b = 5

  "list(bool)"

• repeated DataType type = 6

  "list(type)"

• repeated TensorShapeProto shape = 7

  "list(shape)"

• repeated TensorProto tensor = 8

  "list(tensor)"

Used in: TestResults

• repeated BenchmarkEntry entry = 1

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. This BenchmarkEntry should be emitted by each unit test or benchmark reporter.

Used in: BenchmarkEntries

• string name = 1

  The name of the specific benchmark or test (e.g. BM_AdjustContrast_gpu_B_W_H)

• int64 iters = 2

  If a benchmark, how many iterations it was run for

• double cpu_time = 3

  Total cpu time used for all iterations (in seconds)

• double wall_time = 4

  Total wall time used for all iterations (in seconds)

• double throughput = 5

  Throughput (in MB/s)

• map<string, EntryValue> extras = 6

  Generic map from result key to value.

Used in: TestResults

• string mode = 1

  opt, dbg, etc

• repeated string config = 2

  asan, cuda, etc

• repeated string copts = 3

  -mavx, etc.

BusAdjacency identifies the ability of a device to participate in maximally efficient DMA operations within the local context of a process. This is currently ignored.

Used in: DeviceAttributes, RecvTensorRequest

• BUS_0 = 0

• BUS_1 = 1

• BUS_ANY = 2

• BUS_NUM_ADJACENCIES = 3

Containers to hold repeated fundamental values.

Used in: Feature

• repeated bytes value = 1

Used in: MachineConfiguration

• double mhz_per_cpu = 1

  How fast are these cpus?

• string cpu_info = 2

  Additional cpu information. For example, Intel Ivybridge with HyperThreading (24 cores) dL1:32KB dL2:256KB dL3:30MB

• string cpu_governor = 3

  What kind of cpu scaling is enabled on the host. Examples include "performance", "ondemand", "conservative", "mixed".

• map<string, int64> cache_size = 4

  Cache sizes (in bytes), e.g. "L2": 262144 (for 256KB)

Protocol buffer representing the checkpoint state. TODO(touts): Add other attributes as needed.

• string model_checkpoint_path = 1

  Path to the most-recent model checkpoint.

• repeated string all_model_checkpoint_paths = 2

  Paths to all not-yet-deleted model checkpoints, sorted from oldest to newest. Note that the value of model_checkpoint_path should be the last item in this list.

Used as request type in: grpc.WorkerService.CleanupAll

• repeated string container = 1

  A list of container names. If 'container' is not empty, releases resoures in the given containers in all devices. If 'container' is empty, releases resources in the default container in all devices.

Used as response type in: grpc.WorkerService.CleanupAll

(message has no fields)

Used as request type in: grpc.WorkerService.CleanupGraph

• int64 step_id = 1

Used as response type in: grpc.WorkerService.CleanupGraph

(message has no fields)

Used as request type in: grpc.MasterService.CloseSession

• string session_handle = 1

  REQUIRED: session_handle must be returned by a CreateSession call to the same master service.

Used as response type in: grpc.MasterService.CloseSession

(message has no fields)

Defines a TensorFlow cluster as a set of jobs.

Used in: ServerDef

• repeated JobDef job = 1

  The jobs that comprise the cluster.

CollectionDef should cover most collections. To add a user-defined collection, do one of the following: 1. For simple data types, such as string, int, float: tf.add_to_collection("your_collection_name", your_simple_value) strings will be stored as bytes_list. 2. For Protobuf types, there are three ways to add them: 1) tf.add_to_collection("your_collection_name", your_proto.SerializeToString()) collection_def { key: "user_defined_bytes_collection" value { bytes_list { value: "queue_name: \"test_queue\"\n" } } } or 2) tf.add_to_collection("your_collection_name", str(your_proto)) collection_def { key: "user_defined_string_collection" value { bytes_list { value: "\n\ntest_queue" } } } or 3) any_buf = any_pb2.Any() tf.add_to_collection("your_collection_name", any_buf.Pack(your_proto)) collection_def { key: "user_defined_any_collection" value { any_list { value { type_url: "type.googleapis.com/tensorflow.QueueRunnerDef" value: "\n\ntest_queue" } } } } 3. For Python objects, implement to_proto() and from_proto(), and register them in the following manner: ops.register_proto_function("your_collection_name", proto_type, to_proto=YourPythonObject.to_proto, from_proto=YourPythonObject.from_proto) These functions will be invoked to serialize and de-serialize the collection. For example, ops.register_proto_function(ops.GraphKeys.VARIABLES, proto_type=variable_pb2.VariableDef, to_proto=Variable.to_proto, from_proto=Variable.from_proto)

Used in: MetaGraphDef

• oneof kind

  • CollectionDef.NodeList node_list = 1

  • CollectionDef.BytesList bytes_list = 2

  • CollectionDef.Int64List int64_list = 3

  • CollectionDef.FloatList float_list = 4

  • CollectionDef.AnyList any_list = 5

AnyList is used for collecting Any protos.

Used in: CollectionDef

• repeated google.protobuf.Any value = 1

BytesList is used for collecting strings and serialized protobufs. For example: collection_def { key: "trainable_variables" value { bytes_list { value: "\n\017conv1/weights:0\022\024conv1/weights/Assign \032\024conv1/weights/read:0" value: "\n\016conv1/biases:0\022\023conv1/biases/Assign\032 \023conv1/biases/read:0" } } }

Used in: CollectionDef

• repeated bytes value = 1

FloatList is used for collecting float values.

Used in: CollectionDef

• repeated float value = 1

Int64List is used for collecting int, int64 and long values.

Used in: CollectionDef

• repeated int64 value = 1

NodeList is used for collecting nodes in graph. For example collection_def { key: "summaries" value { node_list { value: "input_producer/ScalarSummary:0" value: "shuffle_batch/ScalarSummary:0" value: "ImageSummary:0" } }

Used in: CollectionDef

• repeated string value = 1

Used in: TestResults

• oneof kind

  • int64 changelist = 1

  • string hash = 2

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

Used in: CreateSessionRequest, ServerDef

• map<string, int32> device_count = 1

  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.

• int32 intra_op_parallelism_threads = 2

  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.

• int32 inter_op_parallelism_threads = 5

  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. 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.

• bool use_per_session_threads = 9

  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.

• int32 placement_period = 3

  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).

• repeated string device_filters = 4

  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.

• optional GPUOptions gpu_options = 6

  Options that apply to all GPUs.

• bool allow_soft_placement = 7

  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 log_device_placement = 8

  Whether device placements should be logged.

• optional GraphOptions graph_options = 10

  Options that apply to all graphs.

• int64 operation_timeout_in_ms = 11

  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.

Used as request type in: grpc.MasterService.CreateSession

• optional GraphDef graph_def = 1

  The initial graph definition.

• optional ConfigProto config = 2

  Configuration options.

Used as response type in: grpc.MasterService.CreateSession

• string session_handle = 1

  The session handle to be used in subsequent calls for the created session. The client must arrange to call CloseSession with this returned session handle to close the session.

• int64 graph_version = 2

  The initial version number for the graph, to be used in the next call to ExtendSession.

Used in: AttrValue, AttrValue.ListValue, OpDef.ArgDef, SavedSliceMeta, TensorDescription, TensorProto

• DT_INVALID = 0

  Not a legal value for DataType. Used to indicate a DataType field has not been set.

• DT_FLOAT = 1

  Data types that all computation devices are expected to be capable to support.

• DT_DOUBLE = 2

• DT_INT32 = 3

• DT_UINT8 = 4

• DT_INT16 = 5

• DT_INT8 = 6

• DT_STRING = 7

• DT_COMPLEX64 = 8

  Single-precision complex

• DT_INT64 = 9

• DT_BOOL = 10

• DT_QINT8 = 11

  Quantized int8

• DT_QUINT8 = 12

  Quantized uint8

• DT_QINT32 = 13

  Quantized int32

• DT_BFLOAT16 = 14

  Float32 truncated to 16 bits. Only for cast ops.

• DT_QINT16 = 15

  Quantized int16

• DT_QUINT16 = 16

  Quantized uint16

• DT_UINT16 = 17

• DT_FLOAT_REF = 101

  Do not use! These are only for parameters. Every enum above should have a corresponding value below (verified by types_test).

• DT_DOUBLE_REF = 102

• DT_INT32_REF = 103

• DT_UINT8_REF = 104

• DT_INT16_REF = 105

• DT_INT8_REF = 106

• DT_STRING_REF = 107

• DT_COMPLEX64_REF = 108

• DT_INT64_REF = 109

• DT_BOOL_REF = 110

• DT_QINT8_REF = 111

• DT_QUINT8_REF = 112

• DT_QINT32_REF = 113

• DT_BFLOAT16_REF = 114

• DT_QINT16_REF = 115

• DT_QUINT16_REF = 116

• DT_UINT16_REF = 117

Used as request type in: grpc.WorkerService.DeregisterGraph

• string graph_handle = 1

  REQUIRED: graph_handle must be returned by a RegisterGraph call to the same WorkerService.

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

Used as response type in: grpc.WorkerService.DeregisterGraph

(message has no fields)

Used in: GetStatusResponse, ListDevicesResponse

• string name = 1

• string device_type = 2

  String representation of device_type.

• int64 memory_limit = 4

  Memory capacity of device in bytes.

• BusAdjacency bus_adjacency = 5

• fixed64 incarnation = 6

  A device is assigned a global unique number each time it is initialized. "incarnation" should never be 0.

• string physical_device_desc = 7

  String representation of the physical device that this device maps to.

Used in: StepStats

• string device = 1

• repeated NodeExecStats node_stats = 2

Used in: BenchmarkEntry

• oneof kind

  • double double_value = 1

  • string string_value = 2

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

• double wall_time = 1

  Timestamp of the event.

• int64 step = 2

  Global step of the event.

• oneof what

  • string file_version = 3

    An event file was started, with the specified version. This is use to identify the contents of the record IO files easily. Current version is "brain.Event:2". All versions start with "brain.Event:".

  • bytes graph_def = 4

    An encoded version of a GraphDef.

  • Summary summary = 5

    A summary was generated.

  • LogMessage log_message = 6

    The user output a log message. Not all messages are logged, only ones generated via the Python tensorboard_logging module.

  • SessionLog session_log = 7

    The state of the session which can be used for restarting after crashes.

• optional Features features = 1

Options specific to the execution of a single step.

Used in: RunGraphRequest

• bool record_costs = 1

• bool record_timeline = 3

Used as request type in: grpc.MasterService.ExtendSession

• string session_handle = 1

  REQUIRED: session_handle must be returned by a CreateSession call to the same master service.

• optional GraphDef graph_def = 2

  REQUIRED: The nodes to be added to the session's graph. If any node has the same name as an existing node, the operation will fail with ILLEGAL_ARGUMENT.

• int64 current_graph_version = 3

  REQUIRED: The version number of the graph to be extended. This will be tested against the current server-side version number, and the operation will fail with FAILED_PRECONDITION if they do not match.

TODO(mrry): Return something about the operation?

Used as response type in: grpc.MasterService.ExtendSession

• int64 new_graph_version = 4

  The new version number for the extended graph, to be used in the next call to ExtendSession.

Containers for non-sequential data.

Used in: FeatureList, Features

• oneof kind

  Each feature can be exactly one kind.

  • BytesList bytes_list = 1

  • FloatList float_list = 2

  • Int64List int64_list = 3

Containers for sequential data. A FeatureList contains lists of Features. These may hold zero or more Feature values. FeatureLists are organized into categories by name. The FeatureLists message contains the mapping from name to FeatureList.

Used in: FeatureLists

• repeated Feature feature = 1

Used in: SequenceExample

• map<string, FeatureList> feature_list = 1

  Map from feature name to feature list.

Used in: Example, InferenceExample, SequenceExample

• map<string, Feature> feature = 1

  Map from feature name to feature.

Used in: Feature

• repeated float value = 1

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. TODO(zhifengc): * device spec, etc.

Used in: FunctionDefLibrary

• optional OpDef signature = 1

  The definition of the function's name, arguments, return values, attrs etc.

• repeated FunctionDef.Node node = 2

  The body of the function.

  function.node.ret[*] are unique.

A node is a multi-value assignment: (ret[0], ret[1], ...) = func(arg[0], arg[1], ...) By convention, "func" is resolved by consulting with a user-defined library first. If not resolved, "func" is assumed to be a builtin op.

Used in: FunctionDef

• repeated string ret = 1

  This node produces multiple outputs. They are named ret[0], ret[1], ..., etc. REQUIRES: function.node.ret[*] are unique across all nodes. REQUIRES: ret.size == func/op def's number of output args.

• string op = 2

  The op/function name.

• repeated string arg = 3

  Arguments passed to this func/op. arg[i] must be either one of function.signature.input_args[*].name or one of function.node[*].ret[*]. REQUIRES: arg.size == func/op def's number of input args.

• repeated string dep = 4

  Control dependencies. dep[i] must be one of function.node[*].ret[*] or one of function.signature.input_args[*].name.

• map<string, AttrValue> attr = 5

  Attrs. 'attr' maps names defined by 'func's attr defs to attr values. attr values may have placeholders which are substituted recursively by concrete values when this node is instantiated. These placeholders must name an attr listed in the FunctionDef's signature.

A library is a set of named functions.

Used in: GraphDef

• repeated FunctionDef function = 1

• string model = 1

  e.g. "Tesla K40c"

• string uuid = 2

  Final entry in output of "nvidia-smi -L"

Used in: ConfigProto

• double per_process_gpu_memory_fraction = 1

  A value between 0 and 1 that indicates what fraction of the available GPU memory to pre-allocate for each process. 1 means to pre-allocate all of the GPU memory, 0.5 means the process allocates ~50% of the available GPU memory.

• string allocator_type = 2

  The type of GPU allocation strategy to use. Allowed values: "": The empty string (default) uses a system-chosen default which may change over time. "BFC": A "Best-fit with coalescing" algorithm, simplified from a version of dlmalloc.

• int64 deferred_deletion_bytes = 3

  Delay deletion of up to this many bytes to reduce the number of interactions with gpu driver code. If 0, the system chooses a reasonable default (several MBs).

Used as request type in: grpc.WorkerService.GetStatus

(message has no fields)

Used as response type in: grpc.WorkerService.GetStatus

• repeated DeviceAttributes device_attributes = 1

Represents the graph of operations

Used in: CreateSessionRequest, ExtendSessionRequest, MetaGraphDef, RegisterGraphRequest

• repeated NodeDef node = 1

• optional VersionDef versions = 4

  Compatibility versions of the graph. See core/public/version.h for version history. The GraphDef version is distinct from the TensorFlow version, and each release of TensorFlow will support a range of GraphDef versions.

• int32 version = 3

  Deprecated single version field; use versions above instead. Since all GraphDef changes before "versions" was introduced were forward compatible, this field is entirely ignored.

• optional FunctionDefLibrary library = 2

  EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET. "library" provides user-defined functions. Naming: * library.function.name are in a flat namespace. NOTE: We may need to change it to be hierarchical to support different orgs. E.g., { "/google/nn", { ... }}, { "/google/vision", { ... }} { "/org_foo/module_bar", {...}} map<string, FunctionDefLib> named_lib; * If node[i].op is the name of one function in "library", node[i] is deemed as a function call. Otherwise, node[i].op must be a primitive operation supported by the runtime. Function call semantics: * The callee may start execution as soon as some of its inputs are ready. The caller may want to use Tuple() mechanism to ensure all inputs are ready in the same time. * The consumer of return values may start executing as soon as the return values the consumer depends on are ready. The consumer may want to use Tuple() mechanism to ensure the consumer does not start until all return values of the callee function are ready.

Used in: ConfigProto, RegisterGraphRequest

• bool enable_recv_scheduling = 2

  If true, use control flow to schedule the activation of Recv nodes. (Currently ignored.)

• optional OptimizerOptions optimizer_options = 3

  Options controlling how graph is optimized.

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

Used in: Summary.Value

• double min = 1

• double max = 2

• double num = 3

• double sum = 4

• double sum_squares = 5

• repeated double bucket_limit = 6

  Parallel arrays encoding the bucket boundaries and the bucket values. bucket(i) is the count for the bucket i. The range for a bucket is: i == 0: -DBL_MAX .. bucket_limit(0) i != 0: bucket_limit(i-1) .. bucket_limit(i)

• repeated double bucket = 7

• optional Features context = 1

• repeated Features features = 2

Used in: Feature

• repeated int64 value = 1

Defines a single job in a TensorFlow cluster.

Used in: ClusterDef

• string name = 1

  The name of this job.

• map<int32, string> tasks = 2

  Mapping from task ID to "hostname:port" string. If the `name` field contains "worker", and the `tasks` map contains a mapping from 7 to "example.org:2222", then the device prefix "/job:worker/task:7" will be assigned to "example.org:2222". NOTE(mrry): Currently, only a dense task ID space starting at 0 is supported.

• string op = 1

  Must match the name of an Op.

• string device_type = 2

  Type of device this kernel runs on.

• repeated KernelDef.AttrConstraint constraint = 3

• repeated string host_memory_arg = 4

  Names of the Op's input_/output_args that reside in host memory instead of device memory.

• string label = 5

  This allows experimental kernels to be registered for an op that won't be used unless the user specifies a "_kernel" attr with value matching this.

Used in: KernelDef

• string name = 1

  Name of an attr from the Op.

• optional AttrValue allowed_values = 2

  A list of values that this kernel supports for this attr. Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.

Used in: LoggingResponse

• int64 step_id = 1

• optional StepStats step_stats = 2

Used as request type in: grpc.MasterService.ListDevices

(message has no fields)

Used as response type in: grpc.MasterService.ListDevices

• repeated DeviceAttributes local_device = 1

• repeated DeviceAttributes remote_device = 2

Protocol buffer used for logging messages to the events file.

Used in: Event

• LogMessage.Level level = 1

• string message = 2

Used in: LogMessage

• UNKNOWN = 0

• DEBUG = 10

• INFO = 20

• WARN = 30

• ERROR = 40

• FATAL = 50

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

Used as request type in: grpc.WorkerService.Logging

• bool rpc_logging = 1

  If true, RPC logging will be activated.

• bool clear = 2

  If true, discard any saved logging data (for all steps).

• repeated int64 fetch_step_id = 3

  When set, requests all saved log data pertaining to the step. Any log data retrieved is eliminated from the store and cannot be retrieved again.

Used as response type in: grpc.WorkerService.Logging

• repeated LabeledStepStats step = 1

Used in: TestResults

• string hostname = 1

  Host name of machine that ran the benchmark.

• optional PlatformInfo platform_info = 2

  Additional platform information.

• optional CPUInfo cpu_info = 3

  CPU Information.

• repeated google.protobuf.Any device_info = 4

  Other devices that are attached and relevant (e.g. GPUInfo).

• int64 step_id = 1

  Process-unique step id.

• string operation = 2

  Name of the operation making the allocation.

• int64 num_bytes = 3

  Number of bytes in the allocation.

• uint64 ptr = 4

  Address of the allocation.

• int64 allocation_id = 5

  Id of the tensor buffer being allocated, used to match to a corresponding deallocation.

• string allocator_name = 6

  Name of the allocator used.

• int64 step_id = 1

  Process-unique step id.

• string operation = 2

  Name of the operation making the deallocation.

• int64 allocation_id = 3

  Id of the tensor buffer being deallocated, used to match to a corresponding allocation.

• string allocator_name = 4

  Name of the allocator used.

• bool deferred = 5

  True if the deallocation is queued and will be performed later, e.g. for GPU lazy freeing of buffers.

• int64 step_id = 1

  Process-unique step id.

• string handle = 2

  Handle describing the feeds and fetches of the step.

• int64 step_id = 1

  Process-unique step id.

• string kernel_name = 2

  Name of the kernel making the allocation as set in GraphDef, e.g., "affine2/weights/Assign".

• optional TensorDescription tensor = 3

  Allocated tensor details.

• int64 allocation_id = 1

  Id of the tensor buffer being deallocated, used to match to a corresponding allocation.

• string allocator_name = 2

  Name of the allocator used.

• int64 step_id = 1

  Process-unique step id.

• string kernel_name = 2

  Name of the kernel producing an output as set in GraphDef, e.g., "affine2/weights/Assign".

• int32 index = 3

  Index of the output being set.

• optional TensorDescription tensor = 4

  Output tensor details.

NOTE: This protocol buffer is evolving, and will go through revisions in the coming months. Protocol buffer containing the following which are necessary to restart training, run inference. It can be used to serialize/de-serialize memory objects necessary for running computation in a graph when crossing the process boundary. It can be used for long term storage of graphs, cross-language execution of graphs, etc. MetaInfoDef GraphDef SaverDef CollectionDef

• optional MetaGraphDef.MetaInfoDef meta_info_def = 1

• optional GraphDef graph_def = 2

  GraphDef.

• optional SaverDef saver_def = 3

  SaverDef.

• map<string, CollectionDef> collection_def = 4

  collection_def: Map from collection name to collections. See CollectionDef section for details.

Meta information regarding the graph to be exported. To be used by users of this protocol buffer to encode information regarding their meta graph.

Used in: MetaGraphDef

• string meta_graph_version = 1

  Version string. Can be the name of the model and revision, steps this model has been trained to, etc.

• optional OpList stripped_op_list = 2

  A copy of the OpDefs used by the producer of this graph_def. Descriptions and Ops not used in graph_def are stripped out.

• optional google.protobuf.Any any_info = 3

  A serialized protobuf. Can be the time this meta graph is created, or modified, or name of the model.

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

Used in: AttrValue

• string name = 1

• map<string, AttrValue> attr = 2

A pair of tensor name and tensor values.

Used in: RunGraphRequest, RunGraphResponse

• string key = 1

  The name of the named tensor.

• optional TensorProto val = 2

  The value of the named tensor.

A pair of tensor name and tensor values.

Used in: RunStepRequest, RunStepResponse

• string name = 1

  Name of the tensor.

• optional TensorProto tensor = 2

  The client can populate a TensorProto using a tensorflow::Tensor`, or directly using the protobuf field accessors. The client specifies whether the returned tensor values should be filled tensor fields (float_val, int_val, etc.) or encoded in a compact form in tensor.tensor_content.

Used in: GraphDef

• string name = 1

  The name given to this operator. Used for naming inputs, logging, visualization, etc. Unique within a single GraphDef. Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".

• string op = 2

  The operation name. There may be custom parameters in attrs. Op names starting with an underscore are reserved for internal use.

• repeated string input = 3

  Each input is "node:src_output" with "node" being a string name and "src_output" indicating which output tensor to use from "node". If "src_output" is 0 the ":0" suffix can be omitted. Regular inputs may optionally be followed by control inputs that have the format "^node".

• string device = 4

  A (possibly partial) specification for the device on which this node should be placed. The expected syntax for this string is as follows: DEVICE_SPEC ::= COLOCATED_NODE | PARTIAL_SPEC COLOCATED_NODE ::= "@" NODE_NAME // See NodeDef.name above. PARTIAL_SPEC ::= ("/" CONSTRAINT) * CONSTRAINT ::= ("job:" JOB_NAME) | ("replica:" [1-9][0-9]*) | ("task:" [1-9][0-9]*) | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") ) Valid values for this string include: * "@other/node" (colocate with "other/node") * "/job:worker/replica:0/task:1/gpu:3" (full specification) * "/job:worker/gpu:3" (partial specification) * "" (no specification) If the constraints do not resolve to a single device (or if this field is empty or not present), the runtime will attempt to choose a device automatically.

• map<string, AttrValue> attr = 5

  Operation-specific graph-construction-time configuration. Note that this should include all attrs defined in the corresponding OpDef, including those with a value matching the default -- this allows the default to change and makes NodeDefs easier to interpret on their own. However, if an attr with a default is not specified in this list, the default will be used. The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and one of the names from the corresponding OpDef's attr field). The values must have a type matching the corresponding OpDef attr's type field. TODO(josh11b): Add some examples here showing best practices.

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

Used in: DeviceStepStats

• string node_name = 1

  TODO(tucker): Use some more compact form of node identity than the full string name. Either all processes should agree on a global id (cost_id?) for each node, or we should use a hash of the name.

• int64 all_start_micros = 2

• int64 op_start_rel_micros = 3

• int64 op_end_rel_micros = 4

• int64 all_end_rel_micros = 5

• repeated AllocatorMemoryUsed memory = 6

• repeated NodeOutput output = 7

• string timeline_label = 8

• int64 scheduled_micros = 9

• uint32 thread_id = 10

• repeated AllocationDescription referenced_tensor = 11

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

Used in: NodeExecStats

• int32 slot = 1

• optional TensorDescription tensor_description = 3

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

Used in: FunctionDef, OpList

• string name = 1

  Op names starting with an underscore are reserved for internal use. Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".

• repeated OpDef.ArgDef input_arg = 2

  Description of the input(s).

• repeated OpDef.ArgDef output_arg = 3

  Description of the output(s).

• repeated OpDef.AttrDef attr = 4

• string summary = 5

  One-line human-readable description of what the Op does.

• string description = 6

  Additional, longer human-readable description of what the Op does.

• bool is_commutative = 18

  True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)

• bool is_aggregate = 16

  If is_aggregate is true, then this operation accepts N >= 2 inputs and produces 1 output all of the same type. Should be associative and commutative, and produce output with the same shape as the input. The optimizer may replace an aggregate op taking input from multiple devices with a tree of aggregate ops that aggregate locally within each device (and possibly within groups of nearby devices) before communicating. TODO(josh11b): Implement that optimization.

  for things like add

• bool is_stateful = 17

  By default Ops may be moved between devices. Stateful ops should either not be moved, or should only be moved if that state can also be moved (e.g. via some sort of save / restore). Stateful ops are guaranteed to never be optimized away by Common Subexpression Elimination (CSE).

  for things like variables, queue

• bool allows_uninitialized_input = 19

  By default, all inputs to an Op must be initialized Tensors. Ops that may initialize tensors for the first time should set this field to true, to allow the Op to take an uninitialized Tensor as input.

  for Assign, etc.

For describing inputs and outputs.

Used in: OpDef

• string name = 1

  Name for the input/output. Should match the regexp "[a-z][a-z0-9_]*".

• string description = 2

  Human readable description.

• DataType type = 3

  Describes the type of one or more tensors that are accepted/produced by this input/output arg. The only legal combinations are: * For a single tensor: either the "type" field is set or the "type_attr" field is set to the name of an attr with type "type". * For a sequence of tensors with the same type: the "number_attr" field will be set to the name of an attr with type "int", and either the "type" or "type_attr" field will be set as for single tensors. * For a sequence of tensors, the "type_list_attr" field will be set to the name of an attr with type "list(type)".

• string type_attr = 4

  if specified, attr must have type "type"

• string number_attr = 5

  if specified, attr must have type "int"

• string type_list_attr = 6

  If specified, attr must have type "list(type)", and none of type, type_attr, and number_attr may be specified.

• bool is_ref = 16

  For inputs: if true, the inputs are required to be refs. By default, inputs can be either refs or non-refs. For outputs: if true, outputs are refs, otherwise they are not.

Description of the graph-construction-time configuration of this Op. That is to say, this describes the attr fields that will be specified in the NodeDef.

Used in: OpDef

• string name = 1

  A descriptive name for the argument. May be used, e.g. by the Python client, as a keyword argument name, and so should match the regexp "[a-z][a-z0-9_]+".

• string type = 2

  One of the type names from attr_value.proto ("string", "list(string)", "int", etc.).

• optional AttrValue default_value = 3

  A reasonable default for this attribute if the user does not supply a value. If not specified, the user must supply a value.

• string description = 4

  Human-readable description.

• bool has_minimum = 5

  For type == "int", this is a minimum value. For "list(___)" types, this is the minimum length.

• int64 minimum = 6

• optional AttrValue allowed_values = 7

  The set of allowed values. Has type that is the "list" version of the "type" field above (uses the ..._list, fields of AttrValue). If type == "type" or "list(type)" above, then the type_list field of allowed_values has the set of allowed DataTypes. If type == "string" or "list(string)", then the s_list field has the set of allowed strings.

A collection of OpDefs

Used in: MetaGraphDef.MetaInfoDef

• repeated OpDef op = 1

Options passed to the graph optimizer

Used in: GraphOptions

• bool do_common_subexpression_elimination = 1

  If true, optimize the graph using common subexpression elimination.

• bool do_constant_folding = 2

  If true, perform constant folding optimization on the graph.

• bool do_function_inlining = 4

  If true, perform function inlining on the graph.

• OptimizerOptions.Level opt_level = 3

Optimization level

Used in: OptimizerOptions

• L1 = 0

  L1 is the default level. Optimization performed at L1 : 1. Common subexpression elimination

• L2 = 2

  Optimization performed at L2 : 1. Common subexpression elimination 2. Constant folding

• L0 = -1

  No optimizations

Used in: MachineConfiguration

• string bits = 1

  e.g. '64bit'

• string linkage = 2

  e.g. 'ELF'

• string machine = 3

  e.g. 'i386'

• string processor = 4

  e.g. 'amdk6' (the real processor name)

• string release = 5

  e.g. '3.13.0-76-generic'

• string system = 6

  e.g. 'Linux'

• string version = 7

  e.g. '#120-Ubuntu SMP Mon Jan 18 15:59:10 UTC 2016'

Protocol buffer representing a QueueRunner.

• string queue_name = 1

  Queue name.

• repeated string enqueue_op_name = 2

  A list of enqueue operations.

• string close_op_name = 3

  The operation to run to close the queue.

• string cancel_op_name = 4

  The operation to run to cancel the queue.

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

• int64 work_started = 1

• int64 work_finished = 2

• int64 num_records_produced = 3

• bytes current_work = 4

Used as request type in: grpc.WorkerService.RecvTensor

• int64 step_id = 1

  The step in which the tensor will be produced. REQUIRED: This must eventually correspond to the `step_id` passed into a RunGraph call on the same WorkerService.

• string rendezvous_key = 2

  A key that identifies the tensor to be received.

• bool dma_ok = 3

  If true, use an out-of-band DMA mechanism to transfer the received tensor.

• BusAdjacency client_bus_adjacency = 4

  NIC bus preference on the request originator side

• BusAdjacency server_bus_adjacency = 5

  NIC bus preference on the request receiver side

Used as response type in: grpc.WorkerService.RecvTensor

• optional TensorProto tensor = 1

  The tensor as a proto.

• bool is_dead = 2

  If true, this tensor was the output of a dead node, and the content is invalid.

• int64 send_start_micros = 3

  The time at which tensor was available and started to be returned.

• optional google.protobuf.Any transport_options = 4

  Optional additional information about how to receive the tensor, in the event that `RecvTensorRequest.dma_ok` was true.

Used as request type in: grpc.WorkerService.RegisterGraph

• string session_handle = 1

  Subgraphs are scoped within one session.

• optional GraphDef graph_def = 2

  "graph_def" has the subgraph of nodes for this worker, with each node having its device_name filled in.

• bool has_control_flow = 3

  True iff the graph (before partitioning) contains control flow nodes. As of 01/11/2015, this is no longer set by clients.

• optional GraphOptions graph_options = 4

  Configuration options for the session in which this graph was created.

Used as response type in: grpc.WorkerService.RegisterGraph

• string graph_handle = 1

  If the registration succeeds, returns an opaque graph_handle to the master. The master calls RunGraph with graph_handle to compute different steps.

Used as request type in: grpc.MasterService.Reset

• repeated string container = 1

  A list of container names, which may be empty. If 'container' is not empty, releases resoures in the given containers in all devices. If 'container' is empty, releases resources in the default container in all devices.

Used as response type in: grpc.MasterService.Reset

(message has no fields)

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

Used in: TestResults

• repeated string arguments = 1

Used as request type in: grpc.WorkerService.RunGraph

• string graph_handle = 1

  REQUIRED: graph_handle must be returned by a RegisterGraph call to the same WorkerService.

• int64 step_id = 2

  A unique ID to distinguish different runs of the same graph. The master generates a global unique `step_id` to dinstinguish different runs of the graph computation. Subgraphs communicate (e.g., send/recv ops) with each other using `step_id` to distinguish tensors generated by different runs.

• optional ExecutorOpts exec_opts = 5

  Options for this step.

• repeated NamedTensor send = 3

  Runs the graph. Sends the tensors in "send" into the graph before the run and fetches the keys into `RunGraphResponse.recv` after the run.

• repeated string recv_key = 4

Used as response type in: grpc.WorkerService.RunGraph

• repeated NamedTensor recv = 1

  A list of tensors corresponding to those requested by `RunGraphRequest.recv_key`.

• optional StepStats step_stats = 2

  If the request asked for execution stats, these are returned here.

EXPERIMENTAL. Options for a single Run() call.

• RunOptions.TraceLevel trace_level = 1

• int64 timeout_in_ms = 2

  Time to wait for operation to complete in milliseconds.

Used in: RunOptions

• NO_TRACE = 0

• FULL_TRACE = 1

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

• optional StepStats step_stats = 1

  Statistics traced for this step. Populated if tracing is turned on via the "RunOptions" proto. EXPERIMENTAL: The format and set of events may change in future versions.

Used as request type in: grpc.MasterService.RunStep

• string session_handle = 1

  REQUIRED: session_handle must be returned by a CreateSession call to the same master service.

• repeated NamedTensorProto feed = 2

  Tensors to be fed in the step. Each feed is a named tensor.

• repeated string fetch = 3

  Fetches. A list of tensor names. The caller expects a tensor to be returned for each fetch[i] (see RunStepResponse.tensor). The order of specified fetches does not change the execution order.

• repeated string target = 4

  Target Nodes. A list of node names. The named nodes will be run to but their outputs will not be fetched.

Used as response type in: grpc.MasterService.RunStep

• repeated NamedTensorProto tensor = 1

  NOTE: The order of the returned tensors may or may not match the fetch order specified in RunStepRequest.

Used in: VariableDef

• string full_name = 1

  Name of the full variable of which this is a slice.

• repeated int32 full_shape = 2

  Shape of the full variable.

• repeated int32 var_offset = 3

  Offset of this variable into the full variable.

• repeated int32 var_shape = 4

  Shape of this variable.

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

Used in: SavedTensorSlices

• string name = 1

  Name of the tensor that this slice belongs to. This must be identical to the name used to encode the key for this record.

• optional TensorSliceProto slice = 2

  Extent of the slice. Must have one entry for each of the dimension of the tensor that this slice belongs to.

• optional TensorProto data = 3

  The raw data of the slice is stored as a TensorProto. Only raw data are stored (we don't fill in fields such as dtype or tensor_shape).

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

Used in: SavedTensorSliceMeta

• string name = 1

  Name of the tensor.

• optional TensorShapeProto shape = 2

  Shape of the tensor

• DataType type = 3

  Type of the tensor

• repeated TensorSliceProto slice = 4

  Explicit list of slices saved in the checkpoint file.

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

Used in: SavedTensorSlices

• repeated SavedSliceMeta tensor = 1

  Each SavedSliceMeta describes the slices for one tensor.

• optional VersionDef versions = 2

  Compatibility version of this checkpoint. See core/public/version.h for version history.

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

• optional SavedTensorSliceMeta meta = 1

  This is only present at the first item of each checkpoint file and serves as a table of contents, listing all the tensor slices saved in this file.

• optional SavedSlice data = 2

  This exists in all but the first item of each checkpoint file.

Protocol buffer representing the configuration of a SaveRestoreHelper.

Used in: MetaGraphDef

• string filename_tensor_name = 1

  The name of the tensor in which to specify the filename when saving or restoring a model checkpoint.

• string save_tensor_name = 2

  The operation to run when saving a model checkpoint.

• string restore_op_name = 3

  The operation to run when restoring a model checkpoint.

• int32 max_to_keep = 4

  Maximum number of checkpoints to keep. If 0, no checkpoints are deleted.

• bool sharded = 5

  Shard the save files, one per device that has Variable nodes.

• float keep_checkpoint_every_n_hours = 6

  How often to keep an additional checkpoint. If not specified, only the last "max_to_keep" checkpoints are kept; if specified, in addition to keeping the last "max_to_keep" checkpoints, an additional checkpoint will be kept for every n hours of training.

• optional Features context = 1

• optional FeatureLists feature_lists = 2

Defines the configuration of a single TensorFlow server.

• optional ClusterDef cluster = 1

  The cluster of which this server is a member.

• string job_name = 2

  The name of the job of which this server is a member. NOTE(mrry): The `cluster` field must contain a `JobDef` with a `name` field that matches this name.

• int32 task_index = 3

  The task index of this server in its job. NOTE: The `cluster` field must contain a `JobDef` with a matching `name` and a mapping in its `tasks` field for this index.

• optional ConfigProto default_session_config = 4

  The default configuration for sessions that run on this server.

• string protocol = 5

  The protocol to be used by this server. Acceptable values include: "grpc".

Protocol buffer used for logging session state.

Used in: Event

• SessionLog.SessionStatus status = 1

• string checkpoint_path = 2

  This checkpoint_path contains both the path and filename.

• string msg = 3

Used in: SessionLog

• STATUS_UNSPECIFIED = 0

• START = 1

• STOP = 2

• CHECKPOINT = 3

Used in: LabeledStepStats, RunGraphResponse, RunOutputs

• repeated DeviceStepStats dev_stats = 1

A Summary is a set of named values to be displayed by the visualizer. Summaries are produced regularly during training, as controlled by the "summary_interval_secs" attribute of the training operation. Summaries are also produced at the end of an evaluation.

Used in: Event

• repeated Summary.Value value = 1

  Set of values for the summary.

Used in: Value

• int32 height = 1

  Dimensions of the image.

• int32 width = 2

• int32 colorspace = 3

  Valid colorspace values are 1 - grayscale 2 - grayscale + alpha 3 - RGB 4 - RGBA 5 - DIGITAL_YUV 6 - BGRA

• bytes encoded_image_string = 4

  Image data in encoded format. All image formats supported by image_codec::CoderUtil can be stored here.

Used in: Summary

• string tag = 1

  Tag name for the data. Will be used as the title of the graph in the visualizer. Tag is usually "op_name:value_name", where "op_name" itself can have structure to indicate grouping.

• oneof value

  Value associated with the tag.

  • float simple_value = 2

  • bytes obsolete_old_style_histogram = 3

  • Image image = 4

  • HistogramProto histo = 5

Used in: MemoryLogTensorAllocation, MemoryLogTensorOutput, NodeOutput

• DataType dtype = 1

  Data type of tensor elements

• optional TensorShapeProto shape = 2

  Shape of the tensor.

• optional AllocationDescription allocation_description = 4

  Information about the size and allocator used for the data

Protocol buffer representing a tensor.

Used in: AttrValue, AttrValue.ListValue, NamedTensor, NamedTensorProto, RecvTensorResponse, SavedSlice

• DataType dtype = 1

• optional TensorShapeProto tensor_shape = 2

  Shape of the tensor. TODO(touts): sort out the 0-rank issues.

• int32 version_number = 3

  Version number. In version 0, if the "repeated xxx" representations contain only one element, that element is repeated to fill the shape. This makes it easy to represent a constant Tensor with a single value.

• bytes tensor_content = 4

  Serialized content from Tensor::AsProtoTensorContent(). This representation can be used for all tensor types.

• repeated float float_val = 5

  DT_FLOAT.

• repeated double double_val = 6

  DT_DOUBLE.

• repeated int32 int_val = 7

  DT_INT32, DT_INT16, DT_INT8, DT_UINT8.

• repeated bytes string_val = 8

  DT_STRING

• repeated float scomplex_val = 9

  DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real and imaginary parts of i-th single precision complex.

• repeated int64 int64_val = 10

  DT_INT64

• repeated bool bool_val = 11

  DT_BOOL

Dimensions of a tensor.

Used in: AttrValue, AttrValue.ListValue, SavedSliceMeta, TensorDescription, TensorProto

• repeated TensorShapeProto.Dim dim = 2

  Dimensions of the tensor, such as {"input", 30}, {"output", 40} for a 30 x 40 2D tensor. If an entry has size -1, this corresponds to a dimension of unknown size. The names are optional. The order of entries in "dim" matters: It indicates the layout of the values in the tensor in-memory representation. The first entry in "dim" is the outermost dimension used to layout the values, the last entry is the innermost dimension. This matches the in-memory layout of RowMajor Eigen tensors. If "dim.size()" > 0, "unknown_rank" must be false.

• bool unknown_rank = 3

  If true, the number of dimensions in the shape is unknown. If true, "dim.size()" must be 0.

One dimension of the tensor.

Used in: TensorShapeProto

• int64 size = 1

  Size of the tensor in that dimension. This value must be >= -1, but values of -1 are reserved for "unknown" shapes (values of -1 mean "unknown" dimension). Certain wrappers that work with TensorShapeProto may fail at runtime when deserializing a TensorShapeProto containing a dim value of -1.

• string name = 2

  Optional name of the tensor dimension.

Can only be interpreted if you know the corresponding TensorShape.

Used in: SavedSlice, SavedSliceMeta

• repeated TensorSliceProto.Extent extent = 1

  Extent of the slice in all tensor dimensions. Must have one entry for each of the dimension of the tensor that this slice belongs to. The order of sizes is the same as the order of dimensions in the TensorShape.

Extent of the slice in one dimension.

Either both or no attributes must be set. When no attribute is set means: All data in that dimension.

Used in: TensorSliceProto

• int64 start = 1

  Start index of the slice, starting at 0.

• oneof has_length

  Length of the slice: if the length is missing or -1 we will interpret this as "everything in this dimension". We use "oneof" to preserve information about whether the length is present without changing the serialization format from the prior proto2 version of this proto.

  • int64 length = 2

The output of one benchmark / test run. Each run contains a list of tests or benchmarks, stored as BenchmarkEntry messages. This message should be emitted by the reporter (which runs the test / BM in a subprocess and then reads the emitted BenchmarkEntry messages; usually from a serialized json file, finally collecting them along with additional information about the test run.

• string target = 1

  The target of the run, e.g.: //tensorflow/core:kernels_adjust_contrast_op_benchmark_test

• optional BenchmarkEntries entries = 2

  The list of tests or benchmarks in this run.

• optional BuildConfiguration build_configuration = 3

  The configuration of the build (compiled opt? with cuda? any copts?)

• optional CommitId commit_id = 4

  The commit id (git hash or changelist)

• int64 start_time = 5

  The time the run started (in seconds of UTC time since Unix epoch)

• double run_time = 6

  The amount of time the total run took (wall time in seconds)

• optional MachineConfiguration machine_configuration = 7

  Machine-specific parameters (Platform and CPU info)

• optional RunConfiguration run_configuration = 8

  Run-specific parameters (arguments, etc)

Used in: TracingRequest

• double duration = 1

  Length of the trace to be taken, in seconds.

• bool use_step_profiler = 2

  If true, capture step profile locally in each worker. Currently unimplemented.

• bool use_kernel_profiler = 3

  If true, capture kernel events from each worker.

• bool use_extended_profiler = 4

  If true, capture extended profiling events from TensorFlow process.

• bool use_gpu_profiler = 5

  If true, capture GPU profiling events locally on each machine. Currently unimplemented.

• bool use_sample_profiler = 6

  If true, collect sampled profile events. Currently unimplemented.

Out-of-band request to configure distributed tracing.

Used as request type in: grpc.WorkerService.Tracing

• optional TraceOpts options = 1

Used as response type in: grpc.WorkerService.Tracing

(message has no fields)

Protocol buffer representing a Variable.

• string variable_name = 1

  Name of the variable tensor.

• string initializer_name = 2

  Name of the initializer op.

• string snapshot_name = 3

  Name of the snapshot tensor.

• optional SaveSliceInfoDef save_slice_info_def = 4

  Support for saving variables as slices of a larger variable.

Version information for a piece of serialized data There are different types of versions for each type of data (GraphDef, etc.), but they all have the same common shape described here. Each consumer has "consumer" and "min_producer" versions (specified elsewhere). A consumer is allowed to consume this data if producer >= min_producer consumer >= min_consumer consumer not in bad_consumers

Used in: GraphDef, SavedTensorSliceMeta

• int32 producer = 1

  The version of the code that produced this data.

• int32 min_consumer = 2

  Any consumer below this version is not allowed to consume this data.

• repeated int32 bad_consumers = 3

  Specific consumer versions which are disallowed (e.g. due to bugs).