paddlex
Low-code development tool based on PaddlePaddle.
Decision gist · record as of 2026-08-14
Yes, if you need rapid prototyping or production deployment of computer vision or document processing models. The low-code design, broad model library, and multi-hardware support reduce development friction. Active maintenance and zero known vulnerabilities are positive signals. The 18 runtime dependencies are substantial; verify they fit your environment. Apache-2.0 licensing poses no barrier for most use cases.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PaddlePaddle framework installed separately; GPU recommended for practical inference performance on large models.
- Low install friction with a pure-Python wheel.
- Active maintenance (released 50 days ago).
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with attribution and liability disclaimers.
last release 2026-06-25 (50 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,526,189 downloads/mo, #3,021 on PyPI
Alternatives
Verify before relying
pip install paddlex
import paddlex as pdx
# Load a pre-trained OCR model and run inference
model = pdx.load_model('ocr_en')
result = model.predict('image.jpg')- Whether all 18 runtime dependencies are strictly required or if some are optional for specific use cases.
- Specific Python version performance or compatibility differences across the 3.8–3.13 range.
- Whether the framework requires GPU hardware or if CPU-only inference is practical for production workloads.
What it is and what it does
PaddleX is a low-code development framework built on PaddlePaddle that bundles pre-trained models and unified inference APIs for computer vision and document processing tasks. It abstracts away model-specific complexity by organizing models into 33 pipelines (e.g., general OCR, document information extraction, object detection, image classification, semantic segmentation, time-series forecasting) and 39 single-function modules, allowing developers to load, run inference, and deploy models with minimal Python code.
The framework supports training, high-performance inference, service-based deployment, and edge deployment across multiple hardware platforms—NVIDIA GPUs, Kunlun, Ascend, Cambricon, and others. It integrates with AIStudio, ModelScope, and Hugging Face Hub for model hosting and provides both programmatic APIs and a cloud-based graphical interface for end-to-end workflows. Recent releases (v3.2–v3.3) added CUDA 12 support, ONNX export, multi-language OCR models, and fine-grained performance benchmarking.
Use it for
- Deploy OCR pipelines (general text, multi-language, formula, seal text recognition) without writing custom model code.
- Extract structured information from documents (tables, layouts, key fields) using pre-trained document understanding models.
- Build object detection or image classification systems by loading a pre-trained model and calling inference on images or batches.
- Perform time-series forecasting, anomaly detection, or classification using unified pipeline APIs.
- Export trained models to ONNX format or deploy to edge devices (mobile, embedded) via the framework's deployment tools.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need rapid prototyping or production deployment of computer vision or document processing models.
The low-code design, broad model library, and multi-hardware support reduce development friction. Active maintenance and zero known vulnerabilities are positive signals. The 18 runtime dependencies are substantial; verify they fit your environment. Apache-2.0 licensing poses no barrier for most use cases.
Install
paddlex on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance (released 50 days ago). Depends on 18 runtime packages including numpy, pandas, pillow, huggingface-hub, and modelscope for model hosting and inference.
Requires PaddlePaddle framework installed separately; GPU recommended for practical inference performance on large models.
License in practice
Apache-2.0 permissive license allows commercial and private use with attribution and liability disclaimers.
Quickstart
pip install paddlex
import paddlex as pdx
# Load a pre-trained OCR model and run inference
model = pdx.load_model('ocr_en')
result = model.predict('image.jpg')Verify before relying
- Whether all 18 runtime dependencies are strictly required or if some are optional for specific use cases.
- Specific Python version performance or compatibility differences across the 3.8–3.13 range.
- Whether the framework requires GPU hardware or if CPU-only inference is practical for production workloads.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 18 packagesaistudio-sdkchardetcolorlogfilelockhuggingface-hubmodelscopenumpypackagingpandaspillowprettytablepy-cpuinfopydanticPyYAMLrequestsruamel.yamltyping-extensionsujson |
| Maintenance | Actively maintained 50 days since the last release |
| First released | |
| Downloads | 2,526,189 / month, #3,021 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: paddlex-3.7.2-py3-none-any.whl
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See also paddleocr · paddlepaddle · rapidocr · cnstd · rapidocr-onnxruntime · cnocr · ddddocr · mineru · marker-pdf · dlib