---
id: tritonclient
version: "2.71.0"
license: BSD
license_treatment: permissive
maintenance: active
---

# tritonclient — Python client library and utilities for communicating with Triton Inference Server

License: permissive · Maintenance: active · Downloads: 14.4M/mo

## 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 above — verify before relying.

tritonclient is a Python client library for communicating with Triton Inference Server over gRPC or HTTP, enabling you to send inference requests to remote model-serving deployments.

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

pip install tritonclient
uv add tritonclient
poetry add tritonclient

## Installing tritonclient

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.

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=[...])

Requires a running Triton Inference Server instance to connect to; the client alone cannot perform inference without a server.

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: unspecified
- Install friction: low
- Maintenance: active
- Downloads: 14.4M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

triton inference server client, grpc http inference requests, triton model serving python, nvidia triton client library, remote inference server communication, tensorrt model deployment client, inference server python api, inference-serving, model-deployment, nvidia-triton

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