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57 lines
2.0 KiB
Protocol Buffer
57 lines
2.0 KiB
Protocol Buffer
syntax = "proto3";
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package tensorflow.serving;
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import "tensorflow/core/protobuf/config.proto";
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import "tensorflow/core/protobuf/named_tensor.proto";
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import "tensorflow_serving/apis/model.proto";
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option cc_enable_arenas = true;
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message SessionRunRequest {
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// Model Specification. If version is not specified, will use the latest
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// (numerical) version.
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ModelSpec model_spec = 1;
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// Tensors to be fed in the step. Each feed is a named tensor.
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repeated NamedTensorProto feed = 2;
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// Fetches. A list of tensor names. The caller expects a tensor to
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// be returned for each fetch[i] (see RunResponse.tensor). The
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// order of specified fetches does not change the execution order.
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repeated string fetch = 3;
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// Target Nodes. A list of node names. The named nodes will be run
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// to but their outputs will not be fetched.
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repeated string target = 4;
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// If true, treat names in feed/fetch/target as alias names than actual tensor
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// names (that appear in the TF graph). Alias names are resolved to actual
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// names using `SignatureDef` in SavedModel associated with the model.
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bool tensor_name_is_alias = 6;
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// Options for the run call. **Currently ignored.**
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RunOptions options = 5;
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}
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message SessionRunResponse {
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// Effective Model Specification used for session run.
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ModelSpec model_spec = 3;
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// NOTE: The order of the returned tensors may or may not match
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// the fetch order specified in RunRequest.
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repeated NamedTensorProto tensor = 1;
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// Returned metadata if requested in the options.
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RunMetadata metadata = 2;
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}
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// SessionService defines a service with which a client can interact to execute
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// Tensorflow model inference. The SessionService::SessionRun method is similar
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// to MasterService::RunStep of Tensorflow, except that all sessions are ready
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// to run, and you request a specific model/session with ModelSpec.
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service SessionService {
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// Runs inference of a given model.
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rpc SessionRun(SessionRunRequest) returns (SessionRunResponse);
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}
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