Description
PaddlePaddle (PArallel Distributed Deep LEarning) is an easy-to-use, efficient, flexible and scalable deep learning platform, which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu.
PaddlePaddle alternatives and similar packages
Based on the "Machine Learning" category.
Alternatively, view PaddlePaddle alternatives based on common mentions on social networks and blogs.
-
tensorflow
An Open Source Machine Learning Framework for Everyone -
gym
A toolkit for developing and comparing reinforcement learning algorithms. -
xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow -
CNTK
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit -
Prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. -
TFLearn
Deep learning library featuring a higher-level API for TensorFlow. -
NuPIC
Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex. -
H2O
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc. -
Pyro.ai
Deep universal probabilistic programming with Python and PyTorch -
dspy
DSPy: The framework for programming—not prompting—foundation models -
Surprise
A Python scikit for building and analyzing recommender systems -
LightFM
A Python implementation of LightFM, a hybrid recommendation algorithm. -
Pylearn2
Warning: This project does not have any current developer. See bellow. -
skflow
Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning -
Sacred
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA. -
Clairvoyant
Software designed to identify and monitor social/historical cues for short term stock movement -
Metrics
Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave -
python-recsys
A python library for implementing a recommender system -
karateclub
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020) -
awesome-embedding-models
A curated list of awesome embedding models tutorials, projects and communities. -
Crab
Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib). -
seqeval
A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...) -
adaptive
:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions -
Xorbits
Scalable Python DS & ML, in an API compatible & lightning fast way. -
TrueSkill, the video game rating system
An implementation of the TrueSkill rating system for Python -
SciKit-Learn Laboratory
SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments. -
rwa
Machine Learning on Sequential Data Using a Recurrent Weighted Average -
Feature Forge
A set of tools for creating and testing machine learning features, with a scikit-learn compatible API -
Data Flow Facilitator for Machine Learning (dffml)
The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease. -
brew
Multiple Classifier Systems and Ensemble Learning Library in Python. -
bodywork
ML pipeline orchestration and model deployments on Kubernetes. -
Robocorp Action Server
Create 🐍 Python AI Actions and 🤖 Automations, and deploy & operate them anywhere -
MLP Classifier
A handwritten multilayer perceptron classifer using numpy. -
OptaPy
OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems. -
vowpal_porpoise
lightweight python wrapper for vowpal wabbit -
omega-ml
MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle -
ChaiPy
A developer interface for creating advanced chatbots for the Chai app.
InfluxDB - Power Real-Time Data Analytics at Scale
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
Do you think we are missing an alternative of PaddlePaddle or a related project?
README
English | [简体中文](./README_cn.md)
Welcome to the PaddlePaddle GitHub.
PaddlePaddle, as the first independent R&D deep learning platform in China, has been officially open-sourced to professional communities since 2016. It is an industrial platform with advanced technologies and rich features that cover core deep learning frameworks, basic model libraries, end-to-end development kits, tools & components as well as service platforms. PaddlePaddle is originated from industrial practices with dedication and commitments to industrialization. It has been widely adopted by a wide range of sectors including manufacturing, agriculture, enterprise service, and so on while serving more than 4.7 million developers, 180,000 companies and generating 560,000 models. With such advantages, PaddlePaddle has helped an increasing number of partners commercialize AI.
Installation
Latest PaddlePaddle Release: v2.3
Our vision is to enable deep learning for everyone via PaddlePaddle. Please refer to our release announcement to track the latest features of PaddlePaddle.
Install Latest Stable Release:
# CPU
pip install paddlepaddle
# GPU
pip install paddlepaddle-gpu
For more information about installation, please view Quick Install
Now our developers can acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you will obtain 8 hours to train models online per day. Click here to start.
FOUR LEADING TECHNOLOGIES
Agile Framework for Industrial Development of Deep Neural Networks
The PaddlePaddle deep learning framework facilitates the development while lowering the technical burden, through leveraging a programmable scheme to architect the neural networks. It supports both declarative programming and imperative programming with both development flexibility and high runtime performance preserved. The neural architectures could be automatically designed by algorithms with better performance than the ones designed by human experts.
Support Ultra-Large-Scale Training of Deep Neural Networks
PaddlePaddle has made breakthroughs in ultra-large-scale deep neural networks training. It launched the world's first large-scale open-source training platform that supports the training of deep networks with 100 billion features and trillions of parameters using data sources distributed over hundreds of nodes. PaddlePaddle overcomes the online deep learning challenges for ultra-large-scale deep learning models, and further achieved real-time model updating with more than 1 trillion parameters. Click here to learn more
High-Performance Inference Engines for Comprehensive Deployment Environments
PaddlePaddle is not only compatible with models trained in 3rd party open-source frameworks , but also offers complete inference products for various production scenarios. Our inference product line includes Paddle Inference: Native inference library for high-performance server and cloud inference; Paddle Serving: A service-oriented framework suitable for distributed and pipeline productions; Paddle Lite: Ultra-Lightweight inference engine for mobile and IoT environments; Paddle.js: A frontend inference engine for browser and mini-apps. Furthermore, by great amounts of optimization with leading hardware in each scenario, Paddle inference engines outperform most of the other mainstream frameworks.
Industry-Oriented Models and Libraries with Open Source Repositories
PaddlePaddle includes and maintains more than 100 mainstream models that have been practiced and polished for a long time in the industry. Some of these models have won major prizes from key international competitions. In the meanwhile, PaddlePaddle has further more than 200 pre-training models (some of them with source codes) to facilitate the rapid development of industrial applications. Click here to learn more
Documentation
We provide English and Chinese documentation.
You might want to start from how to implement deep learning basics with PaddlePaddle.
So far you have already been familiar with Fluid. And the next step should be building a more efficient model or inventing your original Operator.
Our new API enables much shorter programs.
We appreciate your contributions!
Communication
- Github Issues: bug reports, feature requests, install issues, usage issues, etc.
- QQ discussion group: 441226485 (PaddlePaddle).
- Forums: discuss implementations, research, etc.
Courses
- Server Deployments: Courses intorducing high performance server deployments via local and remote services.
- Edge Deployments: Courses intorducing edge deployments from mobile, IoT to web and applets.
Copyright and License
PaddlePaddle is provided under the [Apache-2.0 license](LICENSE).
*Note that all licence references and agreements mentioned in the PaddlePaddle README section above
are relevant to that project's source code only.