---
id: paddlepaddle
version: "3.3.1"
license: Apache Software License
license_treatment: permissive
maintenance: active
---

# paddlepaddle — Parallel Distributed Deep Learning

License: permissive · Maintenance: active · Downloads: 2.4M/mo

## What it is and what it does

PaddlePaddle is an open-source deep learning framework designed for both research and industrial deployment. It supports declarative and imperative programming styles, allowing developers to build neural networks flexibly while maintaining high runtime performance. The framework includes pre-built models, training utilities, and inference acceleration tools, with particular focus on distributed training at scale and cross-platform deployment from cloud servers to edge devices.

The package depends on numpy for tensor operations, protobuf for serialization, Pillow for image handling, httpx, opt_einsum, networkx, typing_extensions, safetensors, and setuptools. It is actively maintained and classified as production-stable. Installation is straightforward via pip, though platform-specific wheels are required and GPU support needs a separate variant. The framework is licensed under Apache-2.0, making it suitable for both open and commercial projects.

Use it for:

- Train large-scale neural networks on distributed clusters using the framework's distributed training infrastructure.
- Deploy trained models to edge devices and mobile platforms using inference acceleration tools.
- Prototype and experiment with deep learning models using pre-built mainstream models from the framework's library.
- Build computer vision applications leveraging Pillow integration and pre-trained vision models.
- Develop natural language processing systems using the framework's NLP model collections and utilities.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

PaddlePaddle is a deep learning framework for building, training, and deploying neural networks across multiple platforms and devices.

Yes, if you are building or deploying deep learning models and want an actively maintained framework with strong industrial backing and cross-platform support. The permissive Apache-2.0 license, stable production status, and zero known vulnerabilities support adoption. Medium install friction is typical for ML frameworks and not a barrier. Best suited for teams already familiar with deep learning frameworks or those targeting the framework's specific strengths in distributed training and edge deployment.

## Install

pip install paddlepaddle
uv add paddlepaddle
poetry add paddlepaddle

## Installing paddlepaddle

Before you install: Medium install friction due to platform-specific wheels for Python 3.9–3.13 across macOS ARM64, Linux x86_64, and Windows. Nine runtime dependencies including numpy, protobuf, and Pillow add weight. Active maintenance with recent commits and stable production status mitigate friction.

License in practice: Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes, but no copyleft obligations apply to your own code.

Quickstart:

pip install paddlepaddle

import paddlepaddle

GPU version requires separate installation; CPU version installs via standard pip. Python 3.9–3.13 supported on Windows, Linux, macOS (CPU only on macOS).

Verify before relying:

- Whether the 2.3 million developers claim and industrial adoption scope remain current as of version 3.3.1.
- Performance characteristics and inference speed improvements on specific hardware relative to other frameworks.
- Completeness and maintenance status of the 100+ mainstream models and 200+ pre-training models mentioned.

## Package facts

- License: Apache Software License (permissive)
- Python support: unspecified
- Install friction: medium
- Maintenance: active
- Downloads: 2.4M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

deep learning framework, neural network training, machine learning platform, distributed deep learning, model inference deployment, tensor computation library, AI framework Python, distributed-training, model-inference, edge-deployment

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