42 lines
1.8 KiB
Protocol Buffer
42 lines
1.8 KiB
Protocol Buffer
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syntax = "proto3";
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package tensorflow;
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option go_package = "github.com/google/tsl/tsl/go/protobuf/for_core_protos_go_proto";
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// RPC options for distributed runtime.
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message RPCOptions {
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// If true, always use RPC to contact the session target.
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//
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// If false (the default option), TensorFlow may use an optimized
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// transport for client-master communication that avoids the RPC
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// stack. This option is primarily for used testing the RPC stack.
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bool use_rpc_for_inprocess_master = 1;
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// The compression algorithm to be used. One of "deflate", "gzip".
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string compression_algorithm = 2;
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// If compression_algorithm is set, the compression level to be used.
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// From 0 (no compression), up to 3.
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int32 compression_level = 3;
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// Setting cache_rpc_response to true will enable sender side caching of
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// response for RecvTensorAsync and RecvBufAsync to allow receiver to retry
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// requests . This is only necessary when the network fabric is experiencing a
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// significant error rate. Without it we'll fail a step on an network error,
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// while with it we'll be able to complete long steps (like complex
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// initializations) in the face of some network errors during RecvTensor.
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bool cache_rpc_response = 4;
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// Disables TCP connection sharing when opening a new RPC channel.
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bool disable_session_connection_sharing = 5;
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// Setting num_channels_per_target > 0 allows uses of multiple channels to
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// communicate to the same target. This can be used to improve the aggregate
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// throughput on high speed links (e.g 100G) where single connection is not
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// sufficient to maximize link utilization. Note that a single RPC only goes
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// on a single channel, this only helps in situations where there are multiple
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// transfers to the same target overlapping in time.
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int32 num_channels_per_target = 6;
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}
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