Server Trace¶

The inference server includes that capability to generate a detailed trace for individual inference requests. If you are building your own inference server you must use the -DTRTIS_ENABLE_TRACING=ON option when configuring cmake.

Tracing is enable by command-line arguments when running the trtserver executable. For example:

$ trtserver --trace-file=/tmp/trace.json --trace-rate=100 --trace-level=MAX ...

The --trace-file options indicates where the trace output should be written. The --trace-rate option specifies the sampling rate. In this example every 100-th inference request will be traced. The --trace-level option indicates the level of trace detail that should be collected. Use the --help option to get more information.

JSON Trace Output¶

The trace output is a JSON file with the following schema:

[
  {
    "model_name": $string,
    "model_version": $number,
    "id": $number
    "parent_id": $number,
    "timestamps": [
      { "name" : $string, "ns" : $number },
      ...
    ]
  },
  ...
]

Each trace indicates the model name and version of the inference request. Each trace is assigned a unique “id”. If the trace is from a model run as part of an ensemble the “parent_id” will indicate the “id” of the containing ensemble. Each trace will have one or more “timestamps” with each timestamp having a name and the timestamp in nanoseconds (“ns”). For example:

[
  {
    "model_name": "simple",
    "model_version": -1,
    "id":1,
    "timestamps" : [
      { "name": "http recv start", "ns": 2259961222771924 },
      { "name": "http recv end", "ns": 2259961222820985 },
      { "name": "request handler start", "ns": 2259961223164078 },
      { "name": "queue start", "ns": 2259961223182400 },
      { "name": "compute start", "ns": 2259961223232405 },
      { "name": "compute end", "ns": 2259961230206777 },
      { "name": "request handler end", "ns": 2259961230211887 },
      { "name": "http send start", "ns": 2259961230529606 },
      { "name": "http send end", "ns": 2259961230543930 } ]
   }
 ]

Trace Summary Tool¶

An example trace summary tool can be used to summarize a set of traces collected from the inference server. Basic usage is:

$ trace_summary.py <trace file>

This produces a summary report for all traces in the file. HTTP and GRPC inference requests are reported separately:

File: trace.json
Summary for simple (-1): trace count = 1
HTTP infer request (avg): 378us
      Receive (avg): 21us
      Send (avg): 7us
      Overhead (avg): 79us
      Handler (avg): 269us
              Overhead (avg): 11us
              Queue (avg): 15us
              Compute (avg): 242us
                      Input (avg): 18us
                      Infer (avg): 208us
                      Output (avg): 15us
Summary for simple (-1): trace count = 1
GRPC infer request (avg): 21441us
      Wait/Read (avg): 20923us
      Send (avg): 74us
      Overhead (avg): 46us
      Handler (avg): 395us
              Overhead (avg): 16us
              Queue (avg): 47us
              Compute (avg): 331us
                      Input (avg): 30us
                      Infer (avg): 286us
                      Output (avg): 14us

Use the -t option to get a summary for each trace in the file. This summary shows the time, in microseconds, between different points in the processing of an inference request. For example, the below output shows that it took 15us from the start of handling the request until the request was enqueued in the scheduling queue:

$ trace_summary.py -t <trace file>
...
simple (-1):
      grpc wait/read start
              26529us
      grpc wait/read end
              39us
      request handler start
              15us
      queue start
              20us
      compute start
              266us
      compute end
              4us
      request handler end
              19us
      grpc send start
              77us
      grpc send end
...

The meaning of the trace timestamps is:

GRPC Request Wait/Read: Collected only for inference requests that use the GRPC protocol. The time spent waiting for a request to arrive at the server and for that request to be read. Because wait time is included in the time it is not a useful measure of how much time is spent reading a request from the network. Tracing an HTTP request will provide an accurate measure of the read time.
HTTP Request Receive: Collected only for inference requests that use the HTTP protocol. The time required to read the inference request from the network.
Send: The time required to send the inference response.
Overhead: Additional time required in the HTTP or GRPC endpoint to process the inference request and response.
Handler: The total time spent handling the inference request, not including the HTTP and GRPC request/response handling.
- Queue: The time the inference request spent in the scheduling queue.
- Compute: The time the inference request spent executing the actual inference. This time includes the time spent copying input and output tensors. If —trace-level=MAX then a breakdown of the compute time will be provided as follows:
  - Input: The time to copy input tensor data as required by the inference framework / backend. This includes the time to copy input tensor data to the GPU.
  - Infer: The time spent executing the model to perform the inference.
  - Output: The time to copy output tensor data as required by the inference framework / backend. This includes the time to copy output tensor data from the GPU.
- Overhead: Additional time required for request handling not covered by Queue or Compute times.