A critical part of optimizing the inference performance of your model is being able to measure changes in performance as you experiment with different optimization strategies. The perf_analyzer application (previously known as perf_client) performs this task for the Triton Inference Server. The perf_analyzer is included with the client examples which are available from several sources.
The perf_analyzer application generates inference requests to your model and measures the throughput and latency of those requests. To get representative results, perf_analyzer measures the throughput and latency over a time window, and then repeats the measurements until it gets stable values. By default perf_analyzer uses average latency to determine stability but you can use the --percentile flag to stabilize results based on that confidence level. For example, if --percentile=95 is used the results will be stabilized using the 95-th percentile request latency. For example,
$ perf_analyzer -m inception_graphdef --percentile=95
*** Measurement Settings ***
Batch size: 1
Measurement window: 5000 msec
Using synchronous calls for inference
Stabilizing using p95 latency
Request concurrency: 1
Client:
Request count: 348
Throughput: 69.6 infer/sec
p50 latency: 13936 usec
p90 latency: 18682 usec
p95 latency: 19673 usec
p99 latency: 21859 usec
Avg HTTP time: 14017 usec (send/recv 200 usec + response wait 13817 usec)
Server:
Inference count: 428
Execution count: 428
Successful request count: 428
Avg request latency: 12005 usec (overhead 36 usec + queue 42 usec + compute input 164 usec + compute infer 11748 usec + compute output 15 usec)
Inferences/Second vs. Client p95 Batch Latency
Concurrency: 1, throughput: 69.6 infer/sec, latency 19673 usec
By default perf_analyzer measures your model's latency and throughput using the lowest possible load on the model. To do this perf_analyzer sends one inference request to Triton and waits for the response. When that response is received, the perf_analyzer immediately sends another request, and then repeats this process during the measurement windows. The number of outstanding inference requests is referred to as the request concurrency, and so by default perf_analyzer uses a request concurrency of 1.
Using the --concurrency-range <start>:<end>:<step> option you can have perf_analyzer collect data for a range of request concurrency levels. Use the --help option to see complete documentation for this and other options. For example, to see the latency and throughput of your model for request concurrency values from 1 to 4:
$ perf_analyzer -m inception_graphdef --concurrency-range 1:4
*** Measurement Settings ***
Batch size: 1
Measurement window: 5000 msec
Latency limit: 0 msec
Concurrency limit: 4 concurrent requests
Using synchronous calls for inference
Stabilizing using average latency
Request concurrency: 1
Client:
Request count: 339
Throughput: 67.8 infer/sec
Avg latency: 14710 usec (standard deviation 2539 usec)
p50 latency: 13665 usec
...
Request concurrency: 4
Client:
Request count: 415
Throughput: 83 infer/sec
Avg latency: 48064 usec (standard deviation 6412 usec)
p50 latency: 47975 usec
p90 latency: 56670 usec
p95 latency: 59118 usec
p99 latency: 63609 usec
Avg HTTP time: 48166 usec (send/recv 264 usec + response wait 47902 usec)
Server:
Inference count: 498
Execution count: 498
Successful request count: 498
Avg request latency: 45602 usec (overhead 39 usec + queue 33577 usec + compute input 217 usec + compute infer 11753 usec + compute output 16 usec)
Inferences/Second vs. Client Average Batch Latency
Concurrency: 1, throughput: 67.8 infer/sec, latency 14710 usec
Concurrency: 2, throughput: 89.8 infer/sec, latency 22280 usec
Concurrency: 3, throughput: 80.4 infer/sec, latency 37283 usec
Concurrency: 4, throughput: 83 infer/sec, latency 48064 usec
For each request concurrency level perf_analyzer reports latency and throughput as seen from the client (that is, as seen by perf_analyzer) and also the average request latency on the server.
The server latency measures the total time from when the request is received at the server until the response is sent from the server. Because of the HTTP and GRPC libraries used to implement the server endpoints, total server latency is typically more accurate for HTTP requests as it measures time from first byte received until last byte sent. For both HTTP and GRPC the total server latency is broken-down into the following components:
- queue: The average time spent in the inference schedule queue by a request waiting for an instance of the model to become available.
- compute: The average time spent performing the actual inference, including any time needed to copy data to/from the GPU.
The client latency time is broken-down further for HTTP and GRPC as follows:
- HTTP: send/recv indicates the time on the client spent sending the request and receiving the response. response wait indicates time waiting for the response from the server.
- GRPC: (un)marshal request/response indicates the time spent marshalling the request data into the GRPC protobuf and unmarshalling the response data from the GRPC protobuf. response wait indicates time writing the GRPC request to the network, waiting for the response, and reading the GRPC response from the network.
Use the verbose (-v) option to perf_analyzer to see more output, including the stabilization passes run for each request concurrency level.
The perf_analyzer provides the -f option to generate a file containing CSV output of the results.
$ perf_analyzer -m inception_graphdef --concurrency-range 1:4 -f perf.csv
$ cat perf.csv
Concurrency,Inferences/Second,Client Send,Network+Server Send/Recv,Server Queue,Server Compute Input,Server Compute Infer,Server Compute Output,Client Recv,p50 latency,p90 latency,p95 latency,p99 latency
1,69.2,225,2148,64,206,11781,19,0,13891,18795,19753,21018
3,84.2,237,1768,21673,209,11742,17,0,35398,43984,47085,51701
4,84.2,279,1604,33669,233,11731,18,1,47045,56545,59225,64886
2,87.2,235,1973,9151,190,11346,17,0,21874,28557,29768,34766
NOTE: The rows in the CSV file are sorted in an increasing order of throughput (Inferences/Second).
You can import the CSV file into a spreadsheet to help visualize the latency vs inferences/second tradeoff as well as see some components of the latency. Follow these steps:
- Open this spreadsheet
- Make a copy from the File menu "Make a copy..."
- Open the copy
- Select the A1 cell on the "Raw Data" tab
- From the File menu select "Import..."
- Select "Upload" and upload the file
- Select "Replace data at selected cell" and then select the "Import data" button
Use the --help option to see complete documentation for all input data options. By default perf_analyzer sends random data to all the inputs of your model. You can select a different input data mode with the --input-data option:
- random: (default) Send random data for each input.
- zero: Send zeros for each input.
- directory path: A path to a directory containing a binary file for each input, named the same as the input. Each binary file must contain the data required for that input for a batch-1 request. Each file should contain the raw binary representation of the input in row-major order.
- file path: A path to a JSON file containing data to be used with every inference request. See the "Real Input Data" section for further details. --input-data can be provided multiple times with different file paths to specific multiple JSON files.
For tensors with with STRING/BYTES datatype there are additional options --string-length and --string-data that may be used in some cases (see --help for full documentation).
For models that support batching you can use the -b option to indicate the batch-size of the requests that perf_analyzer should send. For models with variable-sized inputs you must provide the --shape argument so that perf_analyzer knows what shape tensors to use. For example, for a model that has an input called IMAGE that has shape [ 3, N, M ], where N and M are variable-size dimensions, to tell perf_analyzer to send batch-size 4 requests of shape [ 3, 224, 224 ]:
$ perf_analyzer -m mymodel -b 4 --shape IMAGE:3,224,224
The performance of some models is highly dependent on the data used. For such cases you can provide data to be used with every inference request made by analyzer in a JSON file. The perf_analyzer will use the provided data in a round-robin order when sending inference requests.
Each entry in the "data" array must specify all input tensors with the exact size expected by the model from a single batch. The following example describes data for a model with inputs named, INPUT0 and INPUT1, shape [4, 4] and data type INT32:
{
"data" :
[
{
"INPUT0" : [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"INPUT1" : [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
},
{
"INPUT0" : [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"INPUT1" : [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
},
{
"INPUT0" : [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"INPUT1" : [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
},
{
"INPUT0" : [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"INPUT1" : [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
}
...
]
}
Note that the [4, 4] tensor has been flattened in a row-major format for the inputs. In addition to specifying explicit tensors, you can also provide Base64 encoded binary data for the tensors. Each data object must list its data in a row-major order. Binary data must be in little-endian byte order. The following example highlights how this can be acheived:
{
"data" :
[
{
"INPUT0" : {"b64": "YmFzZTY0IGRlY29kZXI="},
"INPUT1" : {"b64": "YmFzZTY0IGRlY29kZXI="}
},
{
"INPUT0" : {"b64": "YmFzZTY0IGRlY29kZXI="},
"INPUT1" : {"b64": "YmFzZTY0IGRlY29kZXI="}
},
{
"INPUT0" : {"b64": "YmFzZTY0IGRlY29kZXI="},
"INPUT1" : {"b64": "YmFzZTY0IGRlY29kZXI="}
},
...
]
}
In case of sequence models, multiple data streams can be specified in the JSON file. Each sequence will get a data stream of its own and the analyzer will ensure the data from each stream is played back to the same correlation id. The below example highlights how to specify data for multiple streams for a sequence model with a single input named INPUT, shape [1] and data type STRING:
{
"data" :
[
[
{
"INPUT" : ["1"]
},
{
"INPUT" : ["2"]
},
{
"INPUT" : ["3"]
},
{
"INPUT" : ["4"]
}
],
[
{
"INPUT" : ["1"]
},
{
"INPUT" : ["1"]
},
{
"INPUT" : ["1"]
}
],
[
{
"INPUT" : ["1"]
},
{
"INPUT" : ["1"]
}
]
]
}
The above example describes three data streams with lengths 4, 3 and 2 respectively. The perf_analyzer will hence produce sequences of length 4, 3 and 2 in this case.
You can also provide an optional "shape" field to the tensors. This is especially useful while profiling the models with variable-sized tensors as input. Additionally note that when providing the "shape" field, tensor contents must be provided separately in "content" field in row-major order. The specified shape values will override default input shapes provided as a command line option (see --shape) for variable-sized inputs. In the absence of "shape" field, the provided defaults will be used. There is no need to specify shape as a command line option if all the data steps provide shape values for variable tensors. Below is an example json file for a model with single input "INPUT", shape [-1,-1] and data type INT32:
{
"data" :
[
{
"INPUT" :
{
"content": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"shape": [2,8]
}
},
{
"INPUT" :
{
"content": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"shape": [8,2]
}
},
{
"INPUT" :
{
"content": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
}
},
{
"INPUT" :
{
"content": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"shape": [4,4]
}
}
...
]
}
The following is the example to provide contents as base64 string with explicit shapes:
{
"data": [{
"INPUT": {
"content": {"b64": "/9j/4AAQSkZ(...)"},
"shape": [7964]
}},
(...)]
}
By default perf_analyzer sends input tensor data and receives output tensor data over the network. You can instead instruct perf_analyzer to use system shared memory or CUDA shared memory to communicate tensor data. By using these options you can model the performance that you can achieve by using shared memory in your application. Use --shared-memory=system to use system (CPU) shared memory or --shared-memory=cuda to use CUDA shared memory.
By default perf_analyzer uses HTTP to communicate with Triton. The GRPC protocol can be specificed with the -i option. If GRPC is selected the --streaming option can also be specified for GRPC streaming.
Besides using HTTP or gRPC server endpoints to communicate with Triton, perf_analyzer also allows user to benchmark Triton directly using C API. HTTP/gRPC endpoints introduce an additional latency in the pipeline which may not be of interest to the user who is using Triton via C API within their application. Specifically, this feature is useful to benchmark bare minimum Triton without additional overheads from HTTP/gRPC communication.
Pull the Triton SDK and the Inference Server container images on target machine. Since you will need access to the Tritonserver install, it might be easier if you copy the perf_analyzer binary to the Inference Server container.
Use the --help option to see complete list of supported command line arguments.
By default perf_analyzer expects the Triton instance to already be running. You can configure the C API mode using the --service-kind option. In additon, you will need to point
perf_analyzer to the Triton server library path using the --triton-server-directory option and the model
repository path using the --model-repository option.
If the server is run successfully, there is a prompt: "server is alive!" and perf_analyzer will print the stats, as normal.
An example run would look like:
perf_analyzer -m graphdef_int32_int32_int32 --service-kind=triton_c_api --triton-server-directory=/opt/tritonserver --model-repository=/workspace/qa/L0_perf_analyzer_capi/models
There are a few functionalities that are missing from the C API. They are:
- Async mode (
-a) - Using shared memory mode (
--shared-memory=cudaor--shared-memory=system) - Request rate range mode
- For additonal known non-working cases, please refer to qa/L0_perf_analyzer_capi/test.sh