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PaddlePaddle

English | 简体中文

Build StatusDocumentation StatusDocumentation StatusReleaseLicense

Welcome to the PaddlePaddle GitHub.

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.

Our vision is to enable deep learning for everyone via PaddlePaddle. Please refer to our release announcement to track the latest feature of PaddlePaddle.

Latest PaddlePaddle Release: Fluid 1.5.1

Install Latest Stable Release:

# Linux CPU
pip install paddlepaddle
# Linux GPU cuda10cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.5.1.post87
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu==1.5.1.post97
# For installation on other platform, refer to http://paddlepaddle.org/

Now our developers could acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you would obtain 12 hours to train models online per day. If you could insist on that for five consecutive days, then you would own extra 48 hours. Click here to start.

Features

  • Flexibility

    PaddlePaddle supports a wide range of neural network architectures and optimization algorithms. It is easy to configure complex models such as neural machine translation model with attention mechanism or complex memory connection.

  • Efficiency

    In order to unleash the power of heterogeneous computing resource, optimization occurs at different levels of PaddlePaddle, including computing, memory, architecture and communication. The following are some examples:

    • Optimized math operations through SSE/AVX intrinsics, BLAS libraries (e.g. MKL, OpenBLAS, cuBLAS) or customized CPU/GPU kernels.
    • Optimized CNN networks through MKL-DNN library.
    • Highly optimized recurrent networks which can handle variable-length sequence without padding.
    • Optimized local and distributed training for models with high dimensional sparse data.

  • Scalability

    With PaddlePaddle, it is easy to use many CPUs/GPUs and machines to speed up your training. PaddlePaddle can achieve high throughput and performance via optimized communication.

  • Connected to Products

    In addition, PaddlePaddle is also designed to be easily deployable. At Baidu, PaddlePaddle has been deployed into products and services with a vast number of users, including ad click-through rate (CTR) prediction, large-scale image classification, optical character recognition(OCR), search ranking, computer virus detection, recommendation, etc. It is widely utilized in products at Baidu and it has achieved a significant impact. We hope you can also explore the capability of PaddlePaddle to make an impact on your product.

Installation

It is recommended to read this doc on our website.

Documentation

We provide English and Chinese documentation.

Communication

  • Github Issues: bug reports, feature requests, install issues, usage issues, etc.
  • QQ discussion group: 432676488 (PaddlePaddle).
  • Forums: discuss implementations, research, etc.

Copyright and License

PaddlePaddle is provided under the Apache-2.0 license.

About

PArallel Distributed Deep LEarning （『飞桨』核心框架，高性能单机、分布式训练和跨平台部署）

www.paddlepaddle.org/

Resources

Readme
Apache-2.0 license

Code of conduct

Code of conduct

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
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Repository files navigation
PaddlePaddle
English | 简体中文
Build StatusDocumentation StatusDocumentation StatusReleaseLicense
Welcome to the PaddlePaddle GitHub.
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.
Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our release announcement to track the latest feature of PaddlePaddle.
Latest PaddlePaddle Release: Fluid 1.5.1
Install Latest Stable Release:
# Linux CPU
pip install paddlepaddle
# Linux GPU cuda10cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.5.1.post87
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu==1.5.1.post97
# For installation on other platform, refer to http://paddlepaddle.org/

Now our developers could acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you would obtain 12 hours to train models online per day. If you could insist on that for five consecutive days, then you would own extra 48 hours. Click here to start.
Features
  • Flexibility
    PaddlePaddle supports a wide range of neural network architectures and
optimization algorithms. It is easy to configure complex models such as
neural machine translation model with attention mechanism or complex memory
connection.
  • Efficiency
    In order to unleash the power of heterogeneous computing resource,
optimization occurs at different levels of PaddlePaddle, including
computing, memory, architecture and communication. The following are some
examples:
    • Optimized math operations through SSE/AVX intrinsics, BLAS libraries
(e.g. MKL, OpenBLAS, cuBLAS) or customized CPU/GPU kernels.
    • Optimized CNN networks through MKL-DNN library.
    • Highly optimized recurrent networks which can handle variable-length
sequence without padding.
    • Optimized local and distributed training for models with high dimensional
sparse data.
  • Scalability
    With PaddlePaddle, it is easy to use many CPUs/GPUs and machines to speed
up your training. PaddlePaddle can achieve high throughput and performance
via optimized communication.
  • Connected to Products
    In addition, PaddlePaddle is also designed to be easily deployable. At Baidu,
PaddlePaddle has been deployed into products and services with a vast number
of users, including ad click-through rate (CTR) prediction, large-scale image
classification, optical character recognition(OCR), search ranking, computer
virus detection, recommendation, etc. It is widely utilized in products at
Baidu and it has achieved a significant impact. We hope you can also explore
the capability of PaddlePaddle to make an impact on your product.
Installation
It is recommended to read this doc on our website.
Documentation
We provide English and
Chinese documentation.
Communication
  • Github Issues: bug reports, feature requests, install issues, usage issues, etc.
  • QQ discussion group: 432676488 (PaddlePaddle).
  • Forums: discuss implementations, research, etc.
Copyright and License
PaddlePaddle is provided under the Apache-2.0 license.
About
PArallel Distributed Deep LEarning （『飞桨』核心框架，高性能单机、分布式训练和跨平台部署）
www.paddlepaddle.org/
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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PaddlePaddle
English | 简体中文
Build StatusDocumentation StatusDocumentation StatusReleaseLicense
Welcome to the PaddlePaddle GitHub.
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.
Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our release announcement to track the latest feature of PaddlePaddle.
Latest PaddlePaddle Release: Fluid 1.5.1
Install Latest Stable Release:
# Linux CPU
pip install paddlepaddle
# Linux GPU cuda10cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.5.1.post87
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu==1.5.1.post97
# For installation on other platform, refer to http://paddlepaddle.org/

Now our developers could acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you would obtain 12 hours to train models online per day. If you could insist on that for five consecutive days, then you would own extra 48 hours. Click here to start.
Features
  • Flexibility
    PaddlePaddle supports a wide range of neural network architectures and
optimization algorithms. It is easy to configure complex models such as
neural machine translation model with attention mechanism or complex memory
connection.
  • Efficiency
    In order to unleash the power of heterogeneous computing resource,
optimization occurs at different levels of PaddlePaddle, including
computing, memory, architecture and communication. The following are some
examples:
    • Optimized math operations through SSE/AVX intrinsics, BLAS libraries
(e.g. MKL, OpenBLAS, cuBLAS) or customized CPU/GPU kernels.
    • Optimized CNN networks through MKL-DNN library.
    • Highly optimized recurrent networks which can handle variable-length
sequence without padding.
    • Optimized local and distributed training for models with high dimensional
sparse data.
  • Scalability
    With PaddlePaddle, it is easy to use many CPUs/GPUs and machines to speed
up your training. PaddlePaddle can achieve high throughput and performance
via optimized communication.
  • Connected to Products
    In addition, PaddlePaddle is also designed to be easily deployable. At Baidu,
PaddlePaddle has been deployed into products and services with a vast number
of users, including ad click-through rate (CTR) prediction, large-scale image
classification, optical character recognition(OCR), search ranking, computer
virus detection, recommendation, etc. It is widely utilized in products at
Baidu and it has achieved a significant impact. We hope you can also explore
the capability of PaddlePaddle to make an impact on your product.
Installation
It is recommended to read this doc on our website.
Documentation
We provide English and
Chinese documentation.
Communication
  • Github Issues: bug reports, feature requests, install issues, usage issues, etc.
  • QQ discussion group: 432676488 (PaddlePaddle).
  • Forums: discuss implementations, research, etc.
Copyright and License
PaddlePaddle is provided under the Apache-2.0 license.
About
PArallel Distributed Deep LEarning （『飞桨』核心框架，高性能单机、分布式训练和跨平台部署）
www.paddlepaddle.org/
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
PaddlePaddle
English | 简体中文
Build StatusDocumentation StatusDocumentation StatusReleaseLicense
Welcome to the PaddlePaddle GitHub.
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.
Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our release announcement to track the latest feature of PaddlePaddle.
Latest PaddlePaddle Release: Fluid 1.5.1
Install Latest Stable Release:
# Linux CPU
pip install paddlepaddle
# Linux GPU cuda10cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.5.1.post87
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu==1.5.1.post97
# For installation on other platform, refer to http://paddlepaddle.org/

Now our developers could acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you would obtain 12 hours to train models online per day. If you could insist on that for five consecutive days, then you would own extra 48 hours. Click here to start.
Features
  • Flexibility
    PaddlePaddle supports a wide range of neural network architectures and
optimization algorithms. It is easy to configure complex models such as
neural machine translation model with attention mechanism or complex memory
connection.
  • Efficiency
    In order to unleash the power of heterogeneous computing resource,
optimization occurs at different levels of PaddlePaddle, including
computing, memory, architecture and communication. The following are some
examples:
    • Optimized math operations through SSE/AVX intrinsics, BLAS libraries
(e.g. MKL, OpenBLAS, cuBLAS) or customized CPU/GPU kernels.
    • Optimized CNN networks through MKL-DNN library.
    • Highly optimized recurrent networks which can handle variable-length
sequence without padding.
    • Optimized local and distributed training for models with high dimensional
sparse data.
  • Scalability
    With PaddlePaddle, it is easy to use many CPUs/GPUs and machines to speed
up your training. PaddlePaddle can achieve high throughput and performance
via optimized communication.
  • Connected to Products
    In addition, PaddlePaddle is also designed to be easily deployable. At Baidu,
PaddlePaddle has been deployed into products and services with a vast number
of users, including ad click-through rate (CTR) prediction, large-scale image
classification, optical character recognition(OCR), search ranking, computer
virus detection, recommendation, etc. It is widely utilized in products at
Baidu and it has achieved a significant impact. We hope you can also explore
the capability of PaddlePaddle to make an impact on your product.
Installation
It is recommended to read this doc on our website.
Documentation
We provide English and
Chinese documentation.
Communication
  • Github Issues: bug reports, feature requests, install issues, usage issues, etc.
  • QQ discussion group: 432676488 (PaddlePaddle).
  • Forums: discuss implementations, research, etc.
Copyright and License
PaddlePaddle is provided under the Apache-2.0 license.
About
PArallel Distributed Deep LEarning （『飞桨』核心框架，高性能单机、分布式训练和跨平台部署）
www.paddlepaddle.org/
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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PaddlePaddle
English | 简体中文
Build StatusDocumentation StatusDocumentation StatusReleaseLicense
Welcome to the PaddlePaddle GitHub.
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.
Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our release announcement to track the latest feature of PaddlePaddle.
Latest PaddlePaddle Release: Fluid 1.5.1
Install Latest Stable Release:
# Linux CPU
pip install paddlepaddle
# Linux GPU cuda10cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.5.1.post87
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu==1.5.1.post97
# For installation on other platform, refer to http://paddlepaddle.org/

Now our developers could acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you would obtain 12 hours to train models online per day. If you could insist on that for five consecutive days, then you would own extra 48 hours. Click here to start.
Features
  • Flexibility
    PaddlePaddle supports a wide range of neural network architectures and
optimization algorithms. It is easy to configure complex models such as
neural machine translation model with attention mechanism or complex memory
connection.
  • Efficiency
    In order to unleash the power of heterogeneous computing resource,
optimization occurs at different levels of PaddlePaddle, including
computing, memory, architecture and communication. The following are some
examples:
    • Optimized math operations through SSE/AVX intrinsics, BLAS libraries
(e.g. MKL, OpenBLAS, cuBLAS) or customized CPU/GPU kernels.
    • Optimized CNN networks through MKL-DNN library.
    • Highly optimized recurrent networks which can handle variable-length
sequence without padding.
    • Optimized local and distributed training for models with high dimensional
sparse data.
  • Scalability
    With PaddlePaddle, it is easy to use many CPUs/GPUs and machines to speed
up your training. PaddlePaddle can achieve high throughput and performance
via optimized communication.
  • Connected to Products
    In addition, PaddlePaddle is also designed to be easily deployable. At Baidu,
PaddlePaddle has been deployed into products and services with a vast number
of users, including ad click-through rate (CTR) prediction, large-scale image
classification, optical character recognition(OCR), search ranking, computer
virus detection, recommendation, etc. It is widely utilized in products at
Baidu and it has achieved a significant impact. We hope you can also explore
the capability of PaddlePaddle to make an impact on your product.
Installation
It is recommended to read this doc on our website.
Documentation
We provide English and
Chinese documentation.
Communication
  • Github Issues: bug reports, feature requests, install issues, usage issues, etc.
  • QQ discussion group: 432676488 (PaddlePaddle).
  • Forums: discuss implementations, research, etc.
Copyright and License
PaddlePaddle is provided under the Apache-2.0 license.
About
PArallel Distributed Deep LEarning （『飞桨』核心框架，高性能单机、分布式训练和跨平台部署）
www.paddlepaddle.org/
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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PaddlePaddle
English | 简体中文
Build StatusDocumentation StatusDocumentation StatusReleaseLicense
Welcome to the PaddlePaddle GitHub.
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.
Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our release announcement to track the latest feature of PaddlePaddle.
Latest PaddlePaddle Release: Fluid 1.5.1
Install Latest Stable Release:
# Linux CPU
pip install paddlepaddle
# Linux GPU cuda10cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.5.1.post87
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu==1.5.1.post97
# For installation on other platform, refer to http://paddlepaddle.org/

Now our developers could acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you would obtain 12 hours to train models online per day. If you could insist on that for five consecutive days, then you would own extra 48 hours. Click here to start.
Features
  • Flexibility
    PaddlePaddle supports a wide range of neural network architectures and
optimization algorithms. It is easy to configure complex models such as
neural machine translation model with attention mechanism or complex memory
connection.
  • Efficiency
    In order to unleash the power of heterogeneous computing resource,
optimization occurs at different levels of PaddlePaddle, including
computing, memory, architecture and communication. The following are some
examples:
    • Optimized math operations through SSE/AVX intrinsics, BLAS libraries
(e.g. MKL, OpenBLAS, cuBLAS) or customized CPU/GPU kernels.
    • Optimized CNN networks through MKL-DNN library.
    • Highly optimized recurrent networks which can handle variable-length
sequence without padding.
    • Optimized local and distributed training for models with high dimensional
sparse data.
  • Scalability
    With PaddlePaddle, it is easy to use many CPUs/GPUs and machines to speed
up your training. PaddlePaddle can achieve high throughput and performance
via optimized communication.
  • Connected to Products
    In addition, PaddlePaddle is also designed to be easily deployable. At Baidu,
PaddlePaddle has been deployed into products and services with a vast number
of users, including ad click-through rate (CTR) prediction, large-scale image
classification, optical character recognition(OCR), search ranking, computer
virus detection, recommendation, etc. It is widely utilized in products at
Baidu and it has achieved a significant impact. We hope you can also explore
the capability of PaddlePaddle to make an impact on your product.
Installation
It is recommended to read this doc on our website.
Documentation
We provide English and
Chinese documentation.
Communication
  • Github Issues: bug reports, feature requests, install issues, usage issues, etc.
  • QQ discussion group: 432676488 (PaddlePaddle).
  • Forums: discuss implementations, research, etc.
Copyright and License
PaddlePaddle is provided under the Apache-2.0 license.
About
PArallel Distributed Deep LEarning （『飞桨』核心框架，高性能单机、分布式训练和跨平台部署）
www.paddlepaddle.org/
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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PaddlePaddle
English | 简体中文
Build StatusDocumentation StatusDocumentation StatusReleaseLicense
Welcome to the PaddlePaddle GitHub.
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.
Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our release announcement to track the latest feature of PaddlePaddle.
Latest PaddlePaddle Release: Fluid 1.5.1
Install Latest Stable Release:
# Linux CPU
pip install paddlepaddle
# Linux GPU cuda10cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.5.1.post87
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu==1.5.1.post97
# For installation on other platform, refer to http://paddlepaddle.org/

Now our developers could acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you would obtain 12 hours to train models online per day. If you could insist on that for five consecutive days, then you would own extra 48 hours. Click here to start.
Features
  • Flexibility
    PaddlePaddle supports a wide range of neural network architectures and
optimization algorithms. It is easy to configure complex models such as
neural machine translation model with attention mechanism or complex memory
connection.
  • Efficiency
    In order to unleash the power of heterogeneous computing resource,
optimization occurs at different levels of PaddlePaddle, including
computing, memory, architecture and communication. The following are some
examples:
    • Optimized math operations through SSE/AVX intrinsics, BLAS libraries
(e.g. MKL, OpenBLAS, cuBLAS) or customized CPU/GPU kernels.
    • Optimized CNN networks through MKL-DNN library.
    • Highly optimized recurrent networks which can handle variable-length
sequence without padding.
    • Optimized local and distributed training for models with high dimensional
sparse data.
  • Scalability
    With PaddlePaddle, it is easy to use many CPUs/GPUs and machines to speed
up your training. PaddlePaddle can achieve high throughput and performance
via optimized communication.
  • Connected to Products
    In addition, PaddlePaddle is also designed to be easily deployable. At Baidu,
PaddlePaddle has been deployed into products and services with a vast number
of users, including ad click-through rate (CTR) prediction, large-scale image
classification, optical character recognition(OCR), search ranking, computer
virus detection, recommendation, etc. It is widely utilized in products at
Baidu and it has achieved a significant impact. We hope you can also explore
the capability of PaddlePaddle to make an impact on your product.
Installation
It is recommended to read this doc on our website.
Documentation
We provide English and
Chinese documentation.
Communication
  • Github Issues: bug reports, feature requests, install issues, usage issues, etc.
  • QQ discussion group: 432676488 (PaddlePaddle).
  • Forums: discuss implementations, research, etc.
Copyright and License
PaddlePaddle is provided under the Apache-2.0 license.
About
PArallel Distributed Deep LEarning （『飞桨』核心框架，高性能单机、分布式训练和跨平台部署）
www.paddlepaddle.org/
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
PaddlePaddle
English | 简体中文
Build StatusDocumentation StatusDocumentation StatusReleaseLicense
Welcome to the PaddlePaddle GitHub.
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.
Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our release announcement to track the latest feature of PaddlePaddle.
Latest PaddlePaddle Release: Fluid 1.5.1
Install Latest Stable Release:
# Linux CPU
pip install paddlepaddle
# Linux GPU cuda10cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.5.1.post87
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu==1.5.1.post97
# For installation on other platform, refer to http://paddlepaddle.org/

Now our developers could acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you would obtain 12 hours to train models online per day. If you could insist on that for five consecutive days, then you would own extra 48 hours. Click here to start.
Features
  • Flexibility
    PaddlePaddle supports a wide range of neural network architectures and
optimization algorithms. It is easy to configure complex models such as
neural machine translation model with attention mechanism or complex memory
connection.
  • Efficiency
    In order to unleash the power of heterogeneous computing resource,
optimization occurs at different levels of PaddlePaddle, including
computing, memory, architecture and communication. The following are some
examples:
    • Optimized math operations through SSE/AVX intrinsics, BLAS libraries
(e.g. MKL, OpenBLAS, cuBLAS) or customized CPU/GPU kernels.
    • Optimized CNN networks through MKL-DNN library.
    • Highly optimized recurrent networks which can handle variable-length
sequence without padding.
    • Optimized local and distributed training for models with high dimensional
sparse data.
  • Scalability
    With PaddlePaddle, it is easy to use many CPUs/GPUs and machines to speed
up your training. PaddlePaddle can achieve high throughput and performance
via optimized communication.
  • Connected to Products
    In addition, PaddlePaddle is also designed to be easily deployable. At Baidu,
PaddlePaddle has been deployed into products and services with a vast number
of users, including ad click-through rate (CTR) prediction, large-scale image
classification, optical character recognition(OCR), search ranking, computer
virus detection, recommendation, etc. It is widely utilized in products at
Baidu and it has achieved a significant impact. We hope you can also explore
the capability of PaddlePaddle to make an impact on your product.
Installation
It is recommended to read this doc on our website.
Documentation
We provide English and
Chinese documentation.
Communication
  • Github Issues: bug reports, feature requests, install issues, usage issues, etc.
  • QQ discussion group: 432676488 (PaddlePaddle).
  • Forums: discuss implementations, research, etc.
Copyright and License
PaddlePaddle is provided under the Apache-2.0 license.
About
PArallel Distributed Deep LEarning （『飞桨』核心框架，高性能单机、分布式训练和跨平台部署）
www.paddlepaddle.org/
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages