#111111th of 126 in ML frameworks and libraries
paddlex
Low-code toolkit for training and deploying PaddlePaddle OCR and vision models
- Stars
- 6.3k
- License
- Apache-2.0
- Last commit
- Jun 2026
- Last release
- Jun 2026
- Language
- Python
6,057 on 10 Apr 20266,290 on 30 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
PaddleX 3.0 wraps 200+ PaddlePaddle models into 33 task pipelines (OCR, document parsing, detection, segmentation, time series, speech) callable through a Python API or a unified command line. It covers training, inference, high-performance inference, serving and on-device deployment. Hardware support extends beyond NVIDIA GPUs to Kunlunxin, Ascend, Cambricon and Hygon chips.
Who it is for: Engineers building OCR, document and vision pipelines on PaddlePaddle
Strengths
- 33 prebuilt pipelines over 200+ models, including PP-OCRv5 and PP-StructureV3 document parsing
- Same toolkit covers training, high-performance inference, serving and on-device deployment
- Runs on CPU, NVIDIA GPU, Kunlunxin, Ascend, Cambricon and Hygon hardware
- Per-pipeline benchmarks measure end-to-end and per-module inference time
Weaknesses
- Tied to the PaddlePaddle framework (3.0.0 or newer); not usable with PyTorch models
- Main README is in Chinese; the English version is a separate file
- Many pipelines marked unfinished for on-device deployment or secondary development
- Online demos and the graphical pipeline builder run on Baidu AI Studio, not locally
What it needs
- GPU optional
- Docker
- Needs PaddlePaddle >= 3.0.0, Python 3.8-3.13
- Models: PaddlePaddle models, ONNX (via Paddle2ONNX), PP-OCRv5, PP-StructureV3, PaddleOCR-VL
Quick answers
Is paddlex open source?
Yes. Its code is on GitHub under the Apache-2.0 license.
Can you run paddlex with Docker?
Yes. Its repository has a Docker setup: a Dockerfile, a Compose file or a published image.
Does paddlex need a GPU?
No. It runs on a CPU and can use a GPU when you have one.
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