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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.

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

Used in: AllocatorMemoryUsed

• int64 alloc_micros = 1

  The timestamp of the operation.

• int64 alloc_bytes = 2

  Number of bytes allocated, or de-allocated if negative.

Used in: NodeExecStats

• string allocator_name = 1

• int64 total_bytes = 2

  These are per-node allocator memory stats.

• int64 peak_bytes = 3

• int64 live_bytes = 4

  The bytes that are not deallocated.

• repeated AllocationRecord allocation_records = 6

  The allocation and deallocation timeline.

• int64 allocator_bytes_in_use = 5

  These are snapshots of the overall allocator memory stats. The number of live bytes currently allocated by the allocator.

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

Used in: MetaGraphDef

• optional TensorInfo tensor_info = 1

  The tensor to bind the asset filename to.

• string filename = 2

  The filename within an assets directory. Note: does not include the path prefix, i.e. directories. For an asset at /tmp/path/vocab.txt, the filename would be "vocab.txt".

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, 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".

LINT.IfChange

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)"

• repeated NameAttrList func = 9

  "list(attr)"

Batching parameters. Each individual parameter is optional. If omitted, the default value from the relevant batching config struct (SharedBatchScheduler ::Options or BatchSchedulerRetrier::Options) is used.

SharedBatchScheduler options (see shared_batch_scheduler.h):

Used in: SessionBundleConfig

• optional google.protobuf.Int64Value max_batch_size = 1

  The maximum size of each batch. IMPORTANT: As discussed above, use 'max_batch_size * 2' client threads to achieve high throughput with batching.

• optional google.protobuf.Int64Value batch_timeout_micros = 2

  If a task has been enqueued for this amount of time (in microseconds), and a thread is available, the scheduler will immediately form a batch from enqueued tasks and assign the batch to the thread for processing, even if the batch's size is below 'max_batch_size'.

• optional google.protobuf.Int64Value max_enqueued_batches = 3

  The maximum length of the queue, in terms of the number of batches. (A batch that has been scheduled on a thread is considered to have been removed from the queue.)

• optional google.protobuf.Int64Value num_batch_threads = 4

  The number of threads to use to process batches. Must be >= 1, and should be tuned carefully.

• optional google.protobuf.StringValue thread_pool_name = 5

  The name to use for the pool of batch threads.

• repeated int64 allowed_batch_sizes = 6

  The allowed batch sizes. (Ignored if left empty.) Requirements: - The entries must be in increasing order. - The final entry must equal 'max_batch_size'.

• bool pad_variable_length_inputs = 7

  Whether to pad variable-length inputs when a batch is formed.

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.GLOBAL_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: RunMetadata

• repeated CostGraphDef.Node node = 1

Used in: CostGraphDef

• string name = 1

  The name of the node. Names are globally unique.

• string device = 2

  The device of the node. Can be empty if the node is mapped to the default partition or partitioning hasn't been run yet.

• int32 id = 3

  The id of the node. Node ids are only unique inside a partition.

• repeated Node.InputInfo input_info = 4

• repeated Node.OutputInfo output_info = 5

• int64 temporary_memory_size = 6

  Temporary memory used by this node.

• int64 persistent_memory_size = 12

  Persistent memory used by this node.

• int64 host_temp_memory_size = 10

• int64 device_temp_memory_size = 11

• int64 device_persistent_memory_size = 16

• int64 compute_cost = 9

  Estimate of the computational cost of this node, in microseconds.

• int64 compute_time = 14

  Analytical estimate of the computational cost of this node, in microseconds.

• int64 memory_time = 15

  Analytical estimate of the memory access cost of this node, in microseconds.

• bool is_final = 7

  If true, the output is permanent: it can't be discarded, because this node is part of the "final output". Nodes may depend on final nodes.

• repeated int32 control_input = 8

  Ids of the control inputs for this node.

• bool inaccurate = 17

  Are the costs inaccurate?

Inputs of this node. They must be executed before this node can be executed. An input is a particular output of another node, specified by the node id and the output index.

Used in: Node

• int32 preceding_node = 1

• int32 preceding_port = 2

Outputs of this node.

Used in: Node

• int64 size = 1

• int64 alias_input_port = 2

  If >= 0, the output is an alias of an input. Note that an alias input may itself be an alias. The algorithm will therefore need to follow those pointers.

• optional TensorShapeProto shape = 3

• DataType dtype = 4

LINT.IfChange

Used in: AttrValue, AttrValue.ListValue, CostGraphDef.Node.OutputInfo, OpDef.ArgDef, TensorDescription, TensorInfo, TensorProto, serving.GetModelStatusResponse, serving.Instance

• 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_COMPLEX128 = 18

  Double-precision complex

• DT_HALF = 19

• DT_RESOURCE = 20

• DT_VARIANT = 21

  Arbitrary C++ data types

• DT_UINT32 = 22

• DT_UINT64 = 23

• 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

• DT_COMPLEX128_REF = 118

• DT_HALF_REF = 119

• DT_RESOURCE_REF = 120

• DT_VARIANT_REF = 121

• DT_UINT32_REF = 122

• DT_UINT64_REF = 123

Options for initializing DebuggerState in TensorFlow Debugger (tfdbg).

Used in: RunOptions

• repeated DebugTensorWatch debug_tensor_watch_opts = 4

  Debugging options

• int64 global_step = 10

  Caller-specified global step count. Note that this is distinct from the session run count and the executor step count.

• bool reset_disk_byte_usage = 11

  Whether the total disk usage of tfdbg is to be reset to zero in this Session.run call. This is used by wrappers and hooks such as the local CLI ones to indicate that the dumped tensors are cleaned up from the disk after each Session.run.

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

Used in: DebugOptions

• string node_name = 1

  Name of the node to watch.

• int32 output_slot = 2

  Output slot to watch. The semantics of output_slot == -1 is that the node is only watched for completion, but not for any output tensors. See NodeCompletionCallback in debug_gateway.h. TODO(cais): Implement this semantics.

• repeated string debug_ops = 3

  Name(s) of the debugging op(s). One or more than one probes on a tensor. e.g., {"DebugIdentity", "DebugNanCount"}

• repeated string debug_urls = 4

  URL(s) for debug targets(s). Supported URL formats are: - file:///foo/tfdbg_dump: Writes out Event content to file /foo/tfdbg_dump. Assumes all directories can be created if they don't already exist. - grpc://localhost:11011: Sends an RPC request to an EventListener service running at localhost:11011 with the event. - memcbk:///event_key: Routes tensors to clients using the callback registered with the DebugCallbackRegistry for event_key. Each debug op listed in debug_ops will publish its output tensor (debug signal) to all URLs in debug_urls. N.B. Session::Run() supports concurrent invocations of the same inputs (feed keys), outputs and target nodes. If such concurrent invocations are to be debugged, the callers of Session::Run() must use distinct debug_urls to make sure that the streamed or dumped events do not overlap among the invocations. TODO(cais): More visible documentation of this in g3docs.

• bool tolerate_debug_op_creation_failures = 5

  Do not error out if debug op creation fails (e.g., due to dtype incompatibility). Instead, just log the failure.

Used in: DebuggedSourceFiles

• string host = 1

  The host name on which a source code file is located.

• string file_path = 2

  Path to the source code file.

• int64 last_modified = 3

  The timestamp at which the source code file is last modified.

• int64 bytes = 4

  Byte size of the file.

• repeated string lines = 5

  Line-by-line content of the source code file.

• repeated DebuggedSourceFile source_files = 1

  A collection of source code files.

Used in: StepStats

• string device = 1

• repeated NodeExecStats node_stats = 2

Config proto for FileSystemStoragePathSource.

• repeated FileSystemStoragePathSourceConfig.ServableToMonitor servables = 5

  The servables to monitor for new versions, and aspire.

• string servable_name = 1

  A single servable name/base_path pair to monitor. DEPRECATED: Use 'servables' instead. TODO(b/30898016): Stop using these fields, and ultimately remove them here.

• string base_path = 2

• int64 file_system_poll_wait_seconds = 3

  How long to wait between file-system polling to look for children of 'base_path', in seconds. For testing use only: a negative value disables the polling thread.

• bool fail_if_zero_versions_at_startup = 4

  If true, then FileSystemStoragePathSource::Create() and ::UpdateConfig() fail if, for any configured servables, the file system doesn't currently contain at least one version under the base path. (Otherwise, it will emit a warning and keep pinging the file system to check for a version to appear later.)

A servable name and base path to look for versions of the servable.

Used in: FileSystemStoragePathSourceConfig

• string servable_name = 1

  The servable name to supply in aspired-versions callback calls. Child paths of 'base_path' are considered to be versions of this servable.

• string base_path = 2

  The path to monitor, i.e. look for child paths of the form base_path/123.

• optional ServableVersionPolicy servable_version_policy = 4

  The policy to determines the number of versions of the servable to be served at the same time.

A policy that dictates which version(s) of a servable should be served.

Used in: ServableToMonitor, ModelConfig

• oneof policy_choice

  • ServableVersionPolicy.Latest latest = 100

  • ServableVersionPolicy.All all = 101

  • ServableVersionPolicy.Specific specific = 102

Serve all versions found on disk.

Used in: ServableVersionPolicy

(message has no fields)

Serve the latest versions (i.e. the ones with the highest version numbers), among those found on disk. This is the default policy, with the default number of versions as 1.

Used in: ServableVersionPolicy

• uint32 num_versions = 1

  Number of latest versions to serve. (The default is 1.)

Serve a specific version (or set of versions). This policy is useful for rolling back to a specific version, or for canarying a specific version while still serving a separate stable version.

Used in: ServableVersionPolicy

• repeated int64 versions = 1

  The version numbers to serve.

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.

• map<string, AttrValue> attr = 5

  Attributes specific to this function definition.

• repeated NodeDef node_def = 3

  By convention, "op" in node_def is resolved by consulting with a user-defined library first. If not resolved, "func" is assumed to be a builtin op.

• map<string, string> ret = 4

  A mapping from the output arg names from `signature` to the outputs from `node_def` that should be returned by the function.

A library is a set of named functions.

Used in: GraphDef

• repeated FunctionDef function = 1

• repeated GradientDef gradient = 2

GradientDef defines the gradient function of a function defined in a function library. A gradient function g (specified by gradient_func) for a function f (specified by function_name) must follow the following: The function 'f' must be a numerical function which takes N inputs and produces M outputs. Its gradient function 'g', which is a function taking N + M inputs and produces N outputs. I.e. if we have (y1, y2, ..., y_M) = f(x1, x2, ..., x_N), then, g is (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N, dL/dy1, dL/dy2, ..., dL/dy_M), where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the loss function). dL/dx_i is the partial derivative of L with respect to x_i.

Used in: FunctionDefLibrary

• string function_name = 1

  The function name.

• string gradient_func = 2

  The gradient function's name.

Represents the graph of operations

Used in: MetaGraphDef, RunMetadata

• 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: LoggingConfig

• string type = 1

  Identifies the type of the LogCollector we will use to collect these logs.

• string filename_prefix = 2

  The prefix to use for the filenames of the logs.

Metadata logged along with the request logs.

• optional serving.ModelSpec model_spec = 1

• optional SamplingConfig sampling_config = 2

  TODO(b/33279154): Add more metadata as mentioned in the bug.

Configuration for logging query/responses.

Used in: ModelConfig

• optional LogCollectorConfig log_collector_config = 1

• optional SamplingConfig sampling_config = 2

For memory tracking.

Used in: NodeExecStats

• int64 temp_memory_size = 1

• int64 persistent_memory_size = 3

• repeated int64 persistent_tensor_alloc_ids = 5

• int64 device_temp_memory_size = 2

• int64 device_persistent_memory_size = 4

• repeated int64 device_persistent_tensor_alloc_ids = 6

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 TensorInfo SignatureDef

Used in: SavedModel

• 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.

• map<string, SignatureDef> signature_def = 5

  signature_def: Map from user supplied key for a signature to a single SignatureDef.

• repeated AssetFileDef asset_file_def = 6

  Asset file def to be used with the defined graph.

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

  User specified 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.

• repeated string tags = 4

  User supplied tag(s) on the meta_graph and included graph_def. MetaGraphDefs should be tagged with their capabilities or use-cases. Examples: "train", "serve", "gpu", "tpu", etc. These tags enable loaders to access the MetaGraph(s) appropriate for a specific use-case or runtime environment.

• string tensorflow_version = 5

  The __version__ string of the tensorflow build used to write this graph. This will be populated by the framework, which will overwrite any user supplied value.

• string tensorflow_git_version = 6

  The __git_version__ string of the tensorflow build used to write this graph. This will be populated by the framework, which will overwrite any user supplied value.

• bool stripped_default_attrs = 7

  A flag to denote whether default-valued attrs have been stripped from the nodes in this graph_def.

Common configuration for loading a model being served.

Used in: ModelConfigList

• string name = 1

  Name of the model.

• string base_path = 2

  Base path to the model, excluding the version directory. E.g> for a model at /foo/bar/my_model/123, where 123 is the version, the base path is /foo/bar/my_model. (This can be changed once a model is in serving, *if* the underlying data remains the same. Otherwise there are no guarantees about whether the old or new data will be used for model versions currently loaded.)

• ModelType model_type = 3

  Type of model. TODO(b/31336131): DEPRECATED. Please use 'model_platform' instead.

• string model_platform = 4

  Type of model (e.g. "tensorflow"). (This cannot be changed once a model is in serving.)

• optional FileSystemStoragePathSourceConfig.ServableVersionPolicy model_version_policy = 7

  Version policy for the model indicating which version(s) of the model to load and make available for serving simultaneously. The default option is to serve only the latest version of the model. (This can be changed once a model is in serving.)

• map<string, int64> version_labels = 8

  String labels to associate with versions of the model, allowing inference queries to refer to versions by label instead of number. Multiple labels can map to the same version, but not vice-versa. An envisioned use-case for these labels is canarying tentative versions. For example, one can assign labels "stable" and "canary" to two specific versions. Perhaps initially "stable" is assigned to version 0 and "canary" to version 1. Once version 1 passes canary, one can shift the "stable" label to refer to version 1 (at that point both labels map to the same version -- version 1 -- which is fine). Later once version 2 is ready to canary one can move the "canary" label to version 2. And so on.

• optional LoggingConfig logging_config = 6

  Configures logging requests and responses, to the model. (This can be changed once a model is in serving.)

Static list of models to be loaded for serving.

Used in: ModelServerConfig

• repeated ModelConfig config = 1

ModelServer config.

Used in: serving.ReloadConfigRequest

• oneof config

  ModelServer takes either a static file-based model config list or an Any proto representing custom model config that is fetched dynamically at runtime (through network RPC, custom service, etc.).

  • ModelConfigList model_config_list = 1

  • google.protobuf.Any custom_model_config = 2

The type of model. TODO(b/31336131): DEPRECATED.

Used in: ModelConfig

• MODEL_TYPE_UNSPECIFIED = 0

• TENSORFLOW = 1

• OTHER = 2

Configuration for monitoring.

• optional PrometheusConfig prometheus_config = 1

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, AttrValue.ListValue

• string name = 1

• map<string, AttrValue> attr = 2

A pair of tensor name and tensor values.

Used in: SessionBundleConfig, serving.SessionRunRequest, serving.SessionRunResponse

• 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: FunctionDef, 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 ::= PARTIAL_SPEC PARTIAL_SPEC ::= ("/" CONSTRAINT) * CONSTRAINT ::= ("job:" JOB_NAME) | ("replica:" [1-9][0-9]*) | ("task:" [1-9][0-9]*) | ("device:" [A-Za-z]* ":" ([1-9][0-9]* | "*") ) Valid values for this string include: * "/job:worker/replica:0/task:1/device:GPU:3" (full specification) * "/job:worker/device: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

• optional MemoryStats memory_stats = 12

• int64 all_start_nanos = 13

• int64 op_start_rel_nanos = 14

• int64 op_end_rel_nanos = 15

• int64 all_end_rel_nanos = 16

• int64 scheduled_nanos = 17

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. LINT.IfChange

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

• optional OpDeprecation deprecation = 8

  Optional deprecation based on GraphDef versions.

• 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

  Ops are marked as stateful if their behavior depends on some state beyond their input tensors (e.g. variable reading op) or if they have a side-effect (e.g. printing or asserting ops). Equivalently, stateless ops must always produce the same output for the same input and have no side-effects. 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" field of AttrValue). If type == "type" or "list(type)" above, then the "type" field of "allowed_values.list" has the set of allowed DataTypes. If type == "string" or "list(string)", then the "s" field of "allowed_values.list" has the set of allowed strings.

Information about version-dependent deprecation of an op

Used in: OpDef

• int32 version = 1

  First GraphDef version at which the op is disallowed.

• string explanation = 2

  Explanation of why it was deprecated and what to use instead.

A collection of OpDefs

Used in: MetaGraphDef.MetaInfoDef

• repeated OpDef op = 1

Configuration for a servable platform e.g. tensorflow or other ML systems.

Used in: PlatformConfigMap

• optional google.protobuf.Any source_adapter_config = 1

  The config proto for a SourceAdapter in the StoragePathSourceAdapter registry.

• map<string, PlatformConfig> platform_configs = 1

  A map from a platform name to a platform config. The platform name is used in ModelConfig.model_platform.

Configuration for Prometheus monitoring.

Used in: MonitoringConfig

• bool enable = 1

  Whether to expose Prometheus metrics.

• string path = 2

  The endpoint to expose Prometheus metrics. If not specified, PrometheusExporter::kPrometheusPath value is used.

One kind of resource on one device (or type of device).

Used in: ResourceAllocation.Entry

• string device = 1

  The type of device on which the resource resides, e.g. CPU or GPU.

• optional google.protobuf.UInt32Value device_instance = 2

  A specific instance of the device of type 'device' to which the resources are bound (instances are assumed to be numbered 0, 1, ...). When representing the resources required by a servable that has yet to be loaded, this field is optional. If not set, it denotes that the servable's resources are not (yet) bound to a specific instance.

• string kind = 3

  The kind of resource on the device (instance), e.g. RAM or compute share. A given type of resource should have a standard unit that represents the smallest useful quantization. We strongly recommend including the unit (e.g. bytes or millicores) in this string, as in "ram_bytes".

An allocation of one or more kinds of resources, along with the quantity of each. Used to denote the resources that a servable (or collection of servables) will use or is currently using. Also used to denote resources available to the serving system for loading more servables.

• repeated ResourceAllocation.Entry resource_quantities = 1

A collection of resources, each with a quantity. Treated as a resource-> quantity map, i.e. no resource can repeat and the order is immaterial.

Used in: ResourceAllocation

• optional Resource resource = 1

• uint64 quantity = 2

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.

Used in: TensorProto

• string device = 1

  Unique name for the device containing the resource.

• string container = 2

  Container in which this resource is placed.

• string name = 3

  Unique name of this resource.

• uint64 hash_code = 4

  Hash code for the type of the resource. Is only valid in the same device and in the same execution.

• string maybe_type_name = 5

  For debug-only, the name of the type pointed to by this handle, if available.

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

Used in: serving.SessionRunResponse

• 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.

• optional CostGraphDef cost_graph = 2

  The cost graph for the computation defined by the run call.

• repeated GraphDef partition_graphs = 3

  Graphs of the partitions executed by executors.

Options for a single Run() call.

Used in: serving.SessionRunRequest

• RunOptions.TraceLevel trace_level = 1

• int64 timeout_in_ms = 2

  Time to wait for operation to complete in milliseconds.

• int32 inter_op_thread_pool = 3

  The thread pool to use, if session_inter_op_thread_pool is configured. To use the caller thread set this to -1 - this uses the caller thread to execute Session::Run() and thus avoids a context switch. Using the caller thread to execute Session::Run() should be done ONLY for simple graphs, where the overhead of an additional context switch is comparable with the overhead of Session::Run().

• bool output_partition_graphs = 5

  Whether the partition graph(s) executed by the executor(s) should be outputted via RunMetadata.

• optional DebugOptions debug_options = 6

  EXPERIMENTAL. Options used to initialize DebuggerState, if enabled.

• bool report_tensor_allocations_upon_oom = 7

  When enabled, causes tensor allocation information to be included in the error message when the Run() call fails because the allocator ran out of memory (OOM). Enabling this option can slow down the Run() call.

• optional RunOptions.Experimental experimental = 8

Everything inside Experimental is subject to change and is not subject to API stability guarantees in https://www.tensorflow.org/guide/version_compat.

Used in: RunOptions

• int64 collective_graph_key = 1

  If non-zero, declares that this graph is going to use collective ops and must synchronize step_ids with any other graph with this same group_key value (in a distributed computation where tasks run disjoint graphs).

TODO(pbar) Turn this into a TraceOptions proto which allows tracing to be controlled in a more orthogonal manner?

Used in: RunOptions

• NO_TRACE = 0

• SOFTWARE_TRACE = 1

• HARDWARE_TRACE = 2

• FULL_TRACE = 3

Used in: LogMetadata, LoggingConfig

• double sampling_rate = 1

  Requests will be logged uniformly at random with this probability. Valid range: [0, 1.0].

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

• int64 saved_model_schema_version = 1

  The schema version of the SavedModel instance. Used for versioning when making future changes to the specification/implementation. Initial value at release will be 1.

• repeated MetaGraphDef meta_graphs = 2

  One or more MetaGraphs.

Config proto for SavedModelBundleSourceAdapter.

• string adapter_class_name = 10000

  A SessionBundleConfig. FOR INTERNAL USE ONLY DURING TRANSITION TO SAVED_MODEL. WILL BE DEPRECATED. TODO(b/32248363): Replace this field with the "real" field(s).

• optional SessionBundleConfig legacy_config = 1000

Protocol buffer representing the configuration of a Saver.

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.

• SaverDef.CheckpointFormatVersion version = 7

A version number that identifies a different on-disk checkpoint format. Usually, each subclass of BaseSaverBuilder works with a particular version/format. However, it is possible that the same builder may be upgraded to support a newer checkpoint format in the future.

Used in: SaverDef

• LEGACY = 0

  Internal legacy format.

• V1 = 1

  Deprecated format: tf.Saver() which works with tensorflow::table::Table.

• V2 = 2

  Current format: more efficient.

Configuration parameters for a SessionBundle, with optional batching.

Used in: SavedModelBundleSourceAdapterConfig, SessionBundleSourceAdapterConfig

• string session_target = 1

  The TensorFlow runtime to connect to. See full documentation in tensorflow/core/public/session_options.h. For single machine serving, we recommend using the empty string "", which will configure the local TensorFlow runtime implementation. This provides the best isolation currently available across multiple Session servables.

• optional BatchingParameters batching_parameters = 3

  If set, each emitted session is wrapped with a layer that schedules Run() calls in batches. The batching layer is transparent to the client (implements the tensorflow::Session API). IMPORTANT: With batching enabled, client threads will spend most of their time blocked on Session::Run() calls, waiting for enough peer threads to also call Session::Run() such that a large batch can be formed. For good throughput, we recommend setting the number of client threads equal to roughly twice the maximum batch size ('max_batch_size' below). The batching layer uses a SharedBatchScheduler to coordinate batching across multiple session servables emitted by this source adapter. A BatchSchedulerRetrier is added on top of each batching session.

• optional google.protobuf.Int32Value session_run_load_threadpool_index = 4

  If set, session run calls use a separate threadpool for restore and init ops as part of loading the session-bundle. The value of this field should correspond to the index of the tensorflow::ThreadPoolOptionProto defined as part of `session_config.session_inter_op_thread_pool`.

• uint64 experimental_transient_ram_bytes_during_load = 5

  EXPERIMENTAL. THIS FIELD MAY CHANGE OR GO AWAY. USE WITH CAUTION. Transient memory used while loading a model, which is released once the loading phase has completed. (This is on top of the memory used in steady- state while the model is in memory after it has finished loading.) TODO(b/38376838): This is a temporary hack, and it applies to all models. Remove it once resource estimates are moved inside SavedModel.

• repeated string saved_model_tags = 6

  Set of SavedModel tags identifying the specific meta graph def to be loaded.

• repeated NamedTensorProto experimental_fixed_input_tensors = 778

  EXPERIMENTAL. THIS FIELD MAY CHANGE OR GO AWAY. USE WITH CAUTION. Input tensors to append to every Session::Run() call.

• bool enable_model_warmup = 779

  Enables model warmup.

Config proto for SessionBundleSourceAdapter.

• string adapter_class_name = 100

• optional SessionBundleConfig config = 1

SignatureDef defines the signature of a computation supported by a TensorFlow graph. For example, a model with two loss computations, sharing a single input, might have the following signature_def map. Note that across the two SignatureDefs "loss_A" and "loss_B", the input key, output key, and method_name are identical, and will be used by system(s) that implement or rely upon this particular loss method. The output tensor names differ, demonstrating how different outputs can exist for the same method. signature_def { key: "loss_A" value { inputs { key: "input" value { name: "input:0" dtype: DT_STRING tensor_shape: ... } } outputs { key: "loss_output" value { name: "loss_output_A:0" dtype: DT_FLOAT tensor_shape: ... } } } ... method_name: "some/package/compute_loss" } signature_def { key: "loss_B" value { inputs { key: "input" value { name: "input:0" dtype: DT_STRING tensor_shape: ... } } outputs { key: "loss_output" value { name: "loss_output_B:0" dtype: DT_FLOAT tensor_shape: ... } } } ... method_name: "some/package/compute_loss" }

Used in: MetaGraphDef

• map<string, TensorInfo> inputs = 1

  Named input parameters.

• map<string, TensorInfo> outputs = 2

  Named output parameters.

• string method_name = 3

  Extensible method_name information enabling third-party users to mark a SignatureDef as supporting a particular method. This enables producers and consumers of SignatureDefs, e.g. a model definition library and a serving library to have a clear hand-off regarding the semantics of a computation. Note that multiple SignatureDefs in a single MetaGraphDef may have the same method_name. This is commonly used to support multi-headed computation, where a single graph computation may return multiple results.

Used in: RunMetadata

• repeated DeviceStepStats dev_stats = 1

Used in: 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

Information about a Tensor necessary for feeding or retrieval.

Used in: AssetFileDef, SignatureDef

• oneof encoding

  • string name = 1

    For dense `Tensor`s, the name of the tensor in the graph.

  • TensorInfo.CooSparse coo_sparse = 4

    There are many possible encodings of sparse matrices (https://en.wikipedia.org/wiki/Sparse_matrix). Currently, TensorFlow uses only the COO encoding. This is supported and documented in the SparseTensor Python class.

• DataType dtype = 2

• optional TensorShapeProto tensor_shape = 3

  The static shape should be recorded here, to the extent that it can be known in advance. In the case of a SparseTensor, this field describes the logical shape of the represented tensor (aka dense_shape).

For sparse tensors, The COO encoding stores a triple of values, indices, and shape.

Used in: TensorInfo

• string values_tensor_name = 1

  The shape of the values Tensor is [?]. Its dtype must be the dtype of the SparseTensor as a whole, given in the enclosing TensorInfo.

• string indices_tensor_name = 2

  The indices Tensor must have dtype int64 and shape [?, ?].

• string dense_shape_tensor_name = 3

  The dynamic logical shape represented by the SparseTensor is recorded in the Tensor referenced here. It must have dtype int64 and shape [?].

Protocol buffer representing a tensor.

Used in: AttrValue, AttrValue.ListValue, NamedTensorProto, VariantTensorDataProto

• 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 raw tensor content from either Tensor::AsProtoTensorContent or memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation can be used for all tensor types. The purpose of this representation is to reduce serialization overhead during RPC call by avoiding serialization of many repeated small items.

• repeated int32 half_val = 13

  DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll have some pointless zero padding for each value here.

• 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

• repeated double dcomplex_val = 12

  DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real and imaginary parts of i-th double precision complex.

• repeated ResourceHandleProto resource_handle_val = 14

  DT_RESOURCE

• repeated VariantTensorDataProto variant_val = 15

  DT_VARIANT

• repeated uint32 uint32_val = 16

  DT_UINT32

• repeated uint64 uint64_val = 17

  DT_UINT64

Dimensions of a tensor.

Used in: AttrValue, AttrValue.ListValue, CostGraphDef.Node.OutputInfo, TensorDescription, TensorInfo, TensorProto, serving.Instance

• 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.

Protocol buffer representing the serialization format of DT_VARIANT tensors.

Used in: TensorProto

• string type_name = 1

  Name of the type of objects being serialized.

• bytes metadata = 2

  Portions of the object that are not Tensors.

• repeated TensorProto tensors = 3

  Tensors contained within objects being serialized.

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

• 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).