Learning

Paddle Paddle Framework IPU Edition Prediction Example-Documentation-PaddlePaddle Deep Learning Platform

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Paddle framework IPU version supports paddle native inference library (Paddle Inference), which is suitable for cloud inference. C++ prediction example first step: Compile C++ prediction library from source code The current Paddle IPU version only supports the C++ prediction library provided through source code compilation. For compilation environment preparation, please refer to the Paddle Framework […]

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Daily Blog | Construction and Practice of vivo Internet Machine Learning Platform

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With the expansion of recommendation scenarios such as advertising and content, the algorithm model is also constantly evolving and iterating. With the continuous growth of business, model training and output urgently need platform management. The main business scenarios of the vivo Internet machine learning platform include game distribution, stores, malls, content distribution, etc. This article

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Daily blog | The underlying query principle of MyBatis for source code learning

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This article starts with a bug of a low version of MyBatis (versions before 3.4.5), analyzes a complete query process of MyBatis, and interprets a query process of MyBatis in detail from the parsing of the configuration file to the complete execution process of a query. Learn more about MyBatis’s one query process. #Daily #blog

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Recommended by Gitee | iFLearner, a powerful and lightweight federated learning framework

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iFLearner is a powerful and lightweight federated learning framework that provides a computing framework based on data privacy security protection, mainly for federated modeling in deep learning scenarios. Its security bottom layer supports various encryption technologies such as homomorphic encryption, secret sharing, and differential privacy. The algorithm layer supports various deep learning network models, and

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MegEngine is a fast, scalable, easy-to-use deep learning framework that supports automatic derivation. It has three core advantages: integrated training and inference, ultra-low hardware thresholds, and efficient inference on all platforms. It can help enterprises and developers to greatly save products from experimentation. The process from room prototype to industrial deployment can truly achieve hour-level transformation capabilities. As the core component of Brain++, Megvii’s next-generation AI productivity platform, MegEngine was officially open sourced to global developers in March 2020.

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MegEngine is a fast, scalable, and easy-to-use deep learning framework with three key features: Integration of training and reasoning: the same core for training and reasoning, model structure, quantization, pre- and post-processing, dynamic shape and even derivation can be put into the model for reasoning, and the training and reasoning can be easily aligned to

MegEngine is a fast, scalable, easy-to-use deep learning framework that supports automatic derivation. It has three core advantages: integrated training and inference, ultra-low hardware thresholds, and efficient inference on all platforms. It can help enterprises and developers to greatly save products from experimentation. The process from room prototype to industrial deployment can truly achieve hour-level transformation capabilities. As the core component of Brain++, Megvii’s next-generation AI productivity platform, MegEngine was officially open sourced to global developers in March 2020. Read More »

RMS releases GNU C language learning manual – News Fast Delivery

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GNU founder Richard Stallman (RMS) has been writing a GNU C language study manual for some time in the past, and now he has released an announcement to officially disclose this result – GNU C Language Intro and Reference Manual (GNU C Language Intro and Reference Manual (GNU C Language Intro and Reference Manual) Manual),

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Alibaba Cloud Machine Learning PAI Open Source Chinese NLP Algorithm Framework EasyNLP, Helping NLP Large Model Landing

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Author: Presence, Cen Ming, Xiong Xi A guide As BERT, Megatron, GPT-3 and other pre-training models have achieved remarkable results in the field of NLP, more and more teams are devoted to ultra-large-scale training, which makes the scale of training models develop from 100 million to 100 billion or even trillions scale. However, there are

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Deep Learning Compiler BladeDISC

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BladeDISC is a deep learning compiler independently developed and open sourced by Alibaba Group, which aims to provide users with general, transparent, and easy-to-use deep learning performance optimization capabilities. BladeDISC supports mainstream machine learning frameworks, such as TensorFlow, PyTorch, and mainstream hardware, such as GPGPU, CPU, etc. At the architectural level, BladeDISC fundamentally solves the

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Alibaba’s BladeDISC Deep Learning Compiler Officially Open Source

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Author: Zhu Kai – Machine Learning PAI Team With the continuous development of deep learning, the structure of AI models is evolving rapidly, and the underlying computing hardware technologies are emerging in an endless stream. For the majority of developers, it is not only necessary to consider how to effectively utilize computing power in complex

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vivo front-end intelligent practice: the application of machine learning in automatic web page layout – vivo Internet Technology – News Fast Delivery

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Author: vivo Internet front-end team – Su Ning Using the machine learning model based on the self-attention mechanism to design the layout of the design draft can be combined with the context of the dom node to obtain a reasonable solution. 1. Background The traditional craft of cutting diagrams as a front-end is a task

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