CUDA

Speed Up Training by Up to 100 Times! Open Source Differentiable Logic Gate Networks Based on PyTorch

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Speed Up Training by Up to 100 Times! Open Source Differentiable Logic Gate Networks Based on PyTorch

Click the “Little White Learns Vision” above, and choose to add “Bookmark” or “Pin“ Important content delivered first Editor’s Recommendation This article explores logic gate networks aimed at machine learning tasks through learning combinations of logic gates. These networks consist of logic gates such as AND and XOR. To achieve effective training, this article proposes … Read more

How to Install PyTorch

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How to Install PyTorch

1. Download and install Visual Studio Code choose the appropriate version to install 2. Download and install conda and configure the environment Download method one: official website download Download method two: Tsinghua mirror installation 3. conda environment configuration Open advanced system settings on your computer and click on system environment variables: Find path and then … Read more

Discussing 12 Pitfalls I Encountered in PyTorch

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Discussing 12 Pitfalls I Encountered in PyTorch

Author | hyk_1996 Source: CSDN Blog Compiled by: Da Bai 1. Difference in Effects of nn.Module.cuda() and Tensor.cuda() Both the cuda() function can achieve memory migration from CPU to GPU for models and data, but their effects differ. For nn.Module: model = model.cuda() model.cuda() The above two lines achieve the same effect, which is memory … Read more

New PyTorch API: Implementing Different Attention Variants with Just a Few Lines of Code!

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New PyTorch API: Implementing Different Attention Variants with Just a Few Lines of Code!

Click on the above“Beginner’s Guide to Vision” to choose to addto favorites or “pin” Important information delivered promptly Reprinted from: Machine Heart | Edited by: Chen Chen Try a new attention pattern with FlexAttention. In theory, the attention mechanism is everything you need. However, in practice, we also need to optimize implementations of attention mechanisms … Read more

Minimal Implementation of Elastic Training in Pytorch

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Minimal Implementation of Elastic Training in Pytorch

Click the above “Getting Started with Vision” to add a Star or “Pin” Important content delivered immediately Scan the QR code below to join the cutting-edge academic paper exchange group!You can get the latest top conference/journal paper idea interpretations and the interpretation PDFs and materials from beginner to advanced in CV, as well as the … Read more

Understanding PyTorch Memory Management Mechanism

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Understanding PyTorch Memory Management Mechanism

Author丨Mia Luo @ Zhihu (Authorized Reprint) Source丨https://zhuanlan.zhihu.com/p/486360176Editor丨Xiaoshutong, Jizhi Shutong 1. Background Introduction The analysis of the PyTorch memory management mechanism mainly aims to reduce the impact of “memory fragmentation”. A simple example is: As shown in the figure above, suppose we want to allocate 800MB of memory. Although the total free memory is 1000MB, the … Read more

Common Issues When Setting Up PyTorch Environment

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Common Issues When Setting Up PyTorch Environment

1 Issues 1. Always stuck on solving environment and can’t get out. Network solutions include: configuring Tsinghua source, updating conda, etc., but nothing worked.2. After downloading, there are 3 things that need to be done, the last one starting with exe (maybe), always reports an error. Network solutions include: opening prompt with administrator privileges, etc., … Read more

Settings for Reproducible Experiments in PyTorch

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Click on the above “Beginner’s Guide to Vision” to choose to add “Star” or “Pin“ Important content delivered promptly Author: Alxander@Zhihu (authorized) Source: https://zhuanlan.zhihu.com/p/448284000 Editor: Jishi Platform Jishi Guide During the training process in deep learning, due to random initialization and the randomness of sample reading, repeated experimental results may differ, with some variations being … Read more

Understanding PyTorch Memory Management Mechanism

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Understanding PyTorch Memory Management Mechanism

Author丨Mialo@Zhihu Source丨https://zhuanlan.zhihu.com/p/486360176 1. Background Introduction Analyzing the PyTorch memory management mechanism primarily aims to reduce the impact of “memory fragmentation”. A simple example is as follows: As shown in the figure above, suppose we want to allocate 800MB of memory. Although the total free memory is 1000MB, the free memory shown in the upper figure … Read more