openvino-dev
OpenVINO(TM) Development Tools
Decision gist · record as of 2026-08-14
No—do not install for new projects. The package is officially deprecated and will be discontinued with the 2025.0 release. If you need OpenVINO inference capabilities, use the `openvino` runtime package directly. Only install if you are maintaining legacy code that already depends on openvino-dev and cannot migrate to a successor tool.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- C++ libraries required on Windows (Visual Studio Redistributable); virtual environment recommended to avoid dependency conflicts with existing deep learning frameworks.
- Low install friction with a pure-wheel distribution.
- Actively maintained with recent commits and a large repository (10655 stars).
License · maintenance · safety
permissive license (permissive) — Licensed under Apache Software License (permissive), which allows commercial and private use with minimal restrictions, though you must include a copy of the license and state significant changes.
last release 2024-12-19 (603 days) · last repo commit 2026-08-14 · 10,655 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 196,859 downloads/mo, #9,774 on PyPI
Alternatives
Verify before relying
pip install openvino-dev
python -c "from openvino import Core; print(Core().available_devices)"
mo -h # Verify model conversion tool is available- Whether the deprecation timeline affects your deployment horizon and whether migration to a successor tool is documented.
- Compatibility matrix with specific versions of TensorFlow, PyTorch, ONNX, or other frameworks you plan to use.
- Performance characteristics and optimization results on your target hardware and model types.
What it is and what it does
OpenVINO Development Tools is a deprecated package from Intel that provides model conversion, optimization, and deployment utilities for deep learning inference. It wraps the OpenVINO Runtime (installed as a dependency) and adds command-line tools like `mo` for model conversion and `omz_downloader` for accessing pre-trained models from the Open Model Zoo. The package supports converting models trained in TensorFlow, PyTorch, ONNX, Caffe, MXNet, and PaddlePaddle into OpenVINO's intermediate representation (IR) format, which can then be optimized for edge and cloud deployment.
The package is designed for developers who need to take existing trained models and prepare them for inference using OpenVINO's runtime. It includes model quantization, conversion from multiple source frameworks, and access to a curated collection of pre-trained models to accelerate development. However, Intel has announced that this package will be discontinued with the 2025.0 release, so it is not recommended for new projects unless you have a specific requirement to work with existing OpenVINO IR models or a legacy workflow.
Use it for
- Convert a TensorFlow or PyTorch model to OpenVINO IR format for optimized inference on edge devices.
- Download and evaluate pre-trained models from Open Model Zoo without training from scratch.
- Quantize and optimize deep learning models to reduce size and latency for deployment.
- Integrate model conversion into a CI/CD pipeline for automated model preparation.
- Benchmark and profile model performance across different hardware targets using OpenVINO tools.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No—do not install for new projects.
The package is officially deprecated and will be discontinued with the 2025.0 release. If you need OpenVINO inference capabilities, use the `openvino` runtime package directly. Only install if you are maintaining legacy code that already depends on openvino-dev and cannot migrate to a successor tool.
Install
openvino-dev on PyPI
Before you install
Low install friction with a pure-wheel distribution. Actively maintained with recent commits and a large repository (10655 stars). However, the package is officially deprecated and will be discontinued with the 2025.0 release, making it unsuitable for new projects.
C++ libraries required on Windows (Visual Studio Redistributable); virtual environment recommended to avoid dependency conflicts with existing deep learning frameworks.
License in practice
Licensed under Apache Software License (permissive), which allows commercial and private use with minimal restrictions, though you must include a copy of the license and state significant changes.
Quickstart
pip install openvino-dev
python -c "from openvino import Core; print(Core().available_devices)"
mo -h # Verify model conversion tool is availableVerify before relying
- Whether the deprecation timeline affects your deployment horizon and whether migration to a successor tool is documented.
- Compatibility matrix with specific versions of TensorFlow, PyTorch, ONNX, or other frameworks you plan to use.
- Performance characteristics and optimization results on your target hardware and model types.
Package facts
| License | permissive license permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesdefusedxmlnetworkxnumpyopenvino-telemetrypackagingpyyamlrequestsopenvino |
| Maintenance | Actively maintained 603 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 196,859 / month, #9,774 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Operating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: openvino_dev-2024.6.0-17404-py3-none-any.whl
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See also openvino · optimum-intel · nncf · onnxruntime-openvino · keras-nightly · keras · netron · openvino-tokenizers · openvino-genai · openvino-telemetry