treelite-runtime
Treelite runtime
Decision gist · record as of 2026-08-14
Yes, if you have a pre-compiled Treelite model and need fast, portable inference in Python. The package is stable, permissively licensed, and actively maintained. Install friction is moderate due to platform-specific wheels, but that is handled transparently by pip. Not useful without an existing Treelite model file.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a pre-compiled Treelite model file (.so on Linux/macOS, .dll on Windows); the runtime does not compile or train models, only executes them.
- Medium install friction due to platform-specific wheels across macOS, Linux, and Windows.
- Active maintenance with last commit on 2026-08-12 and production-stable status; latest release was 2023-09-13, indicating a mature package with stable API.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions beyond attribution and liability disclaimers.
last release 2023-09-13 (1066 days) · last repo commit 2026-08-12 · 828 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 165,158 downloads/mo, #10,529 on PyPI
Alternatives
Verify before relying
pip install treelite-runtime
import treelite_runtime
predictor = treelite_runtime.Predictor('model.so')
predictions = predictor.predict(data)- Whether the package works with current versions of numpy and scipy beyond the stated Python 3.7–3.8 classifier support.
- Performance characteristics and typical inference latency compared to other tree runtime formats.
- Whether model files from older Treelite versions remain compatible with runtime 3.9.1.
What it is and what it does
Treelite-runtime is a Python wrapper around a C++ runtime for executing decision tree forest models. It loads pre-compiled tree models serialized in Treelite's universal format and performs inference on new data. The package is designed to be lightweight and fast, relying on numpy and scipy for numerical operations.
The runtime does not train or compile models—it only executes them. Users must first export a tree model to Treelite's serialized format using the main Treelite library, then load and run predictions with this runtime package. It is primarily useful for production inference pipelines where model serving speed and portability across platforms matter.
Use it for
- Serving tree-based models in production with low-latency inference requirements.
- Deploying models across heterogeneous platforms (Linux, macOS, Windows) using a single serialized format.
- Embedding fast tree inference in Python applications without retraining or recompiling models.
- Benchmarking or comparing inference performance of tree models across different runtime implementations.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have a pre-compiled Treelite model and need fast, portable inference in Python.
The package is stable, permissively licensed, and actively maintained. Install friction is moderate due to platform-specific wheels, but that is handled transparently by pip. Not useful without an existing Treelite model file.
Install
treelite-runtime on PyPI
Before you install
Medium install friction due to platform-specific wheels across macOS, Linux, and Windows. Active maintenance with last commit on 2026-08-12 and production-stable status; latest release was 2023-09-13, indicating a mature package with stable API.
Requires a pre-compiled Treelite model file (.so on Linux/macOS, .dll on Windows); the runtime does not compile or train models, only executes them.
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions beyond attribution and liability disclaimers.
Quickstart
pip install treelite-runtime
import treelite_runtime
predictor = treelite_runtime.Predictor('model.so')
predictions = predictor.predict(data)Verify before relying
- Whether the package works with current versions of numpy and scipy beyond the stated Python 3.7–3.8 classifier support.
- Performance characteristics and typical inference latency compared to other tree runtime formats.
- Whether model files from older Treelite versions remain compatible with runtime 3.9.1.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 2 packagesnumpyscipy |
| Maintenance | Actively maintained 1,066 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 165,158 / month, #10,529 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/StableLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8 |
Evidence: treelite_runtime-3.9.1-py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.macosx_12_0_x86_64.whl; treelite_runtime-3.9.1-py3-none-macosx_12_0_arm64.whl; treelite_runtime-3.9.1-py3-none-manylinux2014_x86_64.whl; treelite_runtime-3.9.1-py3-none-win_amd64.whl
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