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syntax = "proto3";
package tensorflow;
import "tensorflow/core/framework/graph.proto";
import "tensorflow/core/protobuf/config.proto";
import "tensorflow/contrib/tpu/profiler/op_profile.proto";
// The TPUProfiler service retrieves performance information about
// the programs running on connected TPUs over a period of time.
service TPUProfiler {
// Starts a profiling session, blocks until it completes, and returns data.
rpc Profile(ProfileRequest) returns (ProfileResponse) {
}
}
message ProfileOptions {
// We don't collect the dataset ops by default for better trace-viewer
// scalability. The caller can mannually set this field to include the ops.
bool include_dataset_ops = 1;
// next-field: 2
}
message ToolRequestOptions {
// Required formats for the tool, it should be one of "json", "proto", "raw"
// etc. If not specified (backward compatible), use default format, i.e. most
// tools use json format.
string output_formats = 2;
// Whether save the result directly to repository or pass it back to caller.
// Default to false for backward compatibilities.
bool save_to_repo = 3;
}
message ProfileRequest {
// In future, the caller will be able to customize when profiling starts and
// stops. For now, it collects `duration_ms` milliseconds worth of data.
uint64 duration_ms = 1;
// The maximum number of events to return. By default (value 0), return all
// events.
uint64 max_events = 2;
// Required profiling tools name such as "input_pipeline_analyzer" etc
repeated string tools = 3;
// Specifies the requirement for each tools.
map<string, ToolRequestOptions> tool_options = 8;
// Optional profiling options that control how a TF session will be profiled.
ProfileOptions opts = 4;
// The place where we will dump profile data. We will normally use
// MODEL_DIR/plugin/profile/ as our repository root.
string repository_root = 5;
// The user provided profile session identifier.
string session_id = 6;
// The hostname of system where the profile should happen.
// We use it as identifier in part of our output filename.
string host_name = 7;
// In future, the caller will indicate which TF session is being profiled, and
// only data relating to that program will be returned. For now, we assume
// all activity during the profiling period is relevant.
// next-field: 9
}
message ProfileToolData {
// The file name which this data is associated (e.g. "input_pipeline.json",
// "cluster_xxx.memory_viewer.json").
string name = 1;
// The data payload (likely json) for the specific tool.
bytes data = 2;
}
message ProfileResponse {
reserved 1; // was uint64 placeholder for returning something meaningful.
// Graphs of programs executed on TPUs during the profiling period.
repeated GraphDef computation_graph = 2;
// Performance profile that can be used to annotate HLO operations in the
// computation graph.
RunMetadata hlo_metadata = 5;
// Encoded Trace proto message that contains metadata about the trace captured
// during the profiling period. Describes the devices and resources that
// 'trace_events' refers to.
bytes encoded_trace = 3;
// Assembles a hierarchical performance profile based on HLOs in trace events.
// If the trace covers multiple programs, the longest-running one is analyzed.
// See op_profile.proto for the detailed semantics of the returned profile.
tpu.op_profile.Profile op_profile = 4;
// Data payload for each required tools.
repeated ProfileToolData tool_data = 6;
// When we write profiling data directly to repository directory, we need a
// way to figure out whether the captured trace is empty (due to idle TPU).
bool empty_trace = 7;
// next-field: 8
}
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