tritonclient
Python client library and utilities for communicating with Triton Inference Server
Decision gist · record as of 2026-08-14
Yes. tritonclient is the standard client for Triton Inference Server, actively maintained, production-stable, permissively licensed, and has low install friction. Install it if you are using Triton for model serving and need to query it from Python. It is not useful without a Triton server to connect to.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running Triton Inference Server instance to connect to; the client alone cannot perform inference without a server.
- Low install friction with a pure-wheel distribution.
- Actively maintained with a release 16 days ago.
License · maintenance · safety
BSD (permissive) — BSD license is permissive, allowing use in commercial and proprietary projects with minimal restrictions beyond attribution.
last release 2026-07-29 (16 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 14,363,804 downloads/mo, #1,231 on PyPI
Alternatives
Verify before relying
pip install tritonclient
import tritonclient.http as httpclient
client = httpclient.InferenceServerClient(url='localhost')
result = client.infer(model_name='model', inputs=[...])- Whether gRPC support requires additional system libraries or compilation steps beyond the wheel.
- Specific Python version constraints (requires_python is unspecified in metadata).
- Performance characteristics and latency overhead of the client library itself.
- Exact port or endpoint configuration requirements for server connection.
What it is and what it does
tritonclient is the official Python client library for NVIDIA's Triton Inference Server, a production-grade model-serving platform. It provides both gRPC and HTTP interfaces to send inference requests to models deployed on a Triton server, handling serialization, communication, and response parsing. The library depends on ml-dtypes, numpy, python-rapidjson, and urllib3, making it lightweight and compatible with standard data science workflows.
You use tritonclient when you have models running on a Triton server (either locally or remotely) and need to query them from Python code. It abstracts away the network protocol details, letting you focus on preparing input tensors and consuming predictions. The package is actively maintained, production-stable, and widely used in ML inference pipelines where Triton is the serving layer.
Use it for
- Send inference requests from a Python application to models deployed on a Triton server.
- Build batch prediction pipelines that query remote Triton-served models over HTTP or gRPC.
- Integrate model inference into microservices or REST APIs that call Triton backends.
- Prototype and test model serving configurations before deploying to production.
- Monitor and benchmark inference latency and throughput against a Triton deployment.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
tritonclient is the standard client for Triton Inference Server, actively maintained, production-stable, permissively licensed, and has low install friction. Install it if you are using Triton for model serving and need to query it from Python. It is not useful without a Triton server to connect to.
Install
tritonclient on PyPI
Before you install
Low install friction with a pure-wheel distribution. Actively maintained with a release 16 days ago. Four runtime dependencies are all well-established packages.
Requires a running Triton Inference Server instance to connect to; the client alone cannot perform inference without a server.
License in practice
BSD license is permissive, allowing use in commercial and proprietary projects with minimal restrictions beyond attribution.
Quickstart
pip install tritonclient
import tritonclient.http as httpclient
client = httpclient.InferenceServerClient(url='localhost')
result = client.infer(model_name='model', inputs=[...])Verify before relying
- Whether gRPC support requires additional system libraries or compilation steps beyond the wheel.
- Specific Python version constraints (requires_python is unspecified in metadata).
- Performance characteristics and latency overhead of the client library itself.
- Exact port or endpoint configuration requirements for server connection.
Package facts
| License | BSD permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesml-dtypesnumpypython-rapidjsonurllib3 |
| Maintenance | Actively maintained 16 days since the last release |
| First released | |
| Downloads | 14,363,804 / month, #1,231 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleIntended Audience :: DevelopersIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Image RecognitionTopic :: Software Development :: LibrariesTopic :: Utilities |
Evidence: tritonclient-2.71.0-py3-none-any.whl
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See also tensorflow-serving-api · perf-analyzer · tensorrt-cu13 · clarifai-grpc · triton-windows · tpu-inference · sit4onnx · inference-models · tensorrt-cu12-libs · elastic-agent-client