paddlepaddle/paddle Docker 镜像 - 轩辕镜像
paddlepaddle/paddle并行分布式深度学习是一种通过将深度学习任务按数据并行、模型并行或混合并行等方式分解，并分配到多台计算机或计算节点（如CPU、GPU集群）上协同执行的技术，旨在加速大规模数据集和复杂模型的训练过程，通过并行计算提升效率、缩短训练时间，广泛应用于图像识别、自然语言处理、推荐系统、自动驾驶等领域，是应对海量数据和高复杂度模型训练挑战、推动人工智能技术规模化落地的关键支撑技术之一。
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PaddlePaddle
 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.
Features
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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.
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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.
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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.
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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.
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Deep Learning 101
You might want to start from this online interactive book that can run in a Jupyter Notebook.
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Distributed Training
You can run distributed training jobs on MPI clusters.
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Python API
Our new API enables much shorter programs.
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How to Contribute
We appreciate your contributions!
Ask Questions
You are welcome to submit questions and bug reports as Github Issues.
Copyright and License
PaddlePaddle is provided under the Apache-2.0 license.
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