Triton Performance Analyzer

Triton Performance Analyzer is CLI tool which can help you optimize the
inference performance of models running on Triton Inference Server by measuring
changes in performance as you experiment with different optimization strategies.

⚠️ Warning: genai-perf is being deprecated. Please migrate to AIPerf for continued support and enhanced features.

Features

Inference Load Modes

Concurrency Mode simlulates
load by maintaining a specific concurrency of outgoing requests to the
server
Request Rate Mode simulates
load by sending consecutive requests at a specific rate to the server
Custom Interval Mode
simulates load by sending consecutive requests at specific intervals to the
server

Performance Measurement Modes

Time Windows Mode measures model
performance repeatedly over a specific time interval until performance has
stabilized
Count Windows Mode measures
model performance repeatedly over a specific number of requests until
performance has stabilized

Other Features

Sequence Models,
Ensemble Models,
and
Decoupled Models
can be profiled in addition to standard/stateless/coupled models
Input Data to model inferences can be auto-generated or
specified as well as verifying output
TorchServe can be used as the
inference server in addition to the default Triton server

Quick Start

The steps below will guide you on how to start using Perf Analyzer.

Step 1: Start Triton Container

export RELEASE=<yy.mm> # e.g. to use the release from the end of February of 2023, do `export RELEASE=23.02`

docker pull nvcr.io/nvidia/tritonserver:${RELEASE}-py3

docker run --gpus all --rm -it --net host nvcr.io/nvidia/tritonserver:${RELEASE}-py3

Step 2: Download `simple` Model

# inside triton container
git clone --depth 1 https://github.com/triton-inference-server/server

mkdir model_repository ; cp -r server/docs/examples/model_repository/simple model_repository

Step 3: Start Triton Server

# inside triton container
tritonserver --model-repository $(pwd)/model_repository &> server.log &

# confirm server is ready, look for 'HTTP/1.1 200 OK'
curl -v localhost:8000/v2/health/ready

# detach (CTRL-p CTRL-q)

Step 4: Start Triton SDK Container

docker pull nvcr.io/nvidia/tritonserver:${RELEASE}-py3-sdk

docker run --gpus all --rm -it --net host nvcr.io/nvidia/tritonserver:${RELEASE}-py3-sdk

Step 5: Run Perf Analyzer

# inside sdk container
perf_analyzer -m simple

See the full quick start guide for additional tips on
how to analyze output.

Documentation

Contributing

Contributions to Triton Perf Analyzer are more than welcome. To contribute
please review the contribution
guidelines,
then fork and create a pull request.

Reporting problems, asking questions

We appreciate any feedback, questions or bug reporting regarding this
project. When help with code is needed, follow the process outlined in
the Stack Overflow (https://stackoverflow.com/help/mcve)
document. Ensure posted examples are:

minimal - use as little code as possible that still produces the
same problem
complete - provide all parts needed to reproduce the problem. Check
if you can strip external dependency and still show the problem. The
less time we spend on reproducing problems the more time we have to
fix it
verifiable - test the code you're about to provide to make sure it
reproduces the problem. Remove all other problems that are not
related to your request/question.

Package profile

perf-analyzer

Dependencies

Details

Package description