CUDA

RTX 3090 Deep Learning Environment Setup Guide: Pytorch, TensorFlow, Keras

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RTX 3090 Deep Learning Environment Setup Guide: Pytorch, TensorFlow, Keras

For academic sharing only, does not represent the stance of this public account. Contact for removal if infringing.Reprinted from:Author丨Yukyin@ZhihuSource丨https://zhuanlan.zhihu.com/p/279401802Editor丨Extreme City Platform I recently got a 3090 and found that the various environment configurations written online are quite chaotic and slow. So I tested the fastest 3090 configuration environment myself, feel free to add more! Basic … Read more

Teaching You to Implement PyTorch Operators with CUDA

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Introduction CUDA (Compute Unified Device Architecture) is a general parallel computing architecture launched by NVIDIA, enabling GPUs to solve complex computational problems. Developers can use C language to write programs for the CUDA architecture, which can run at ultra-high performance on CUDA-supported processors. Editor | Heart of Autonomous Driving Author | Yuppie@Zhihu Link | https://zhuanlan.zhihu.com/p/595851188 … Read more

Get Your GPU Ready for Deep Learning (With Code)

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Get Your GPU Ready for Deep Learning (With Code)

Author: Saurabh Bodhe Translator: Chen Zhendong Proofreader: Che Qianzi This article is approximately 1000 words, suggested reading time is 5 minutes. This article discusses a tutorial on setting up a GPU-based TensorFlow platform using NVIDIA’s official tools. “Building Deep Learning on Google Cloud Platform”I know that building a high-end deep learning system based on GPU … Read more

Understanding PyTorch Memory Management Mechanism

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Understanding PyTorch Memory Management Mechanism

Source: Data STUDIO This article is approximately 3600 words, recommended reading time is 9 minutes. Why does the error message indicate enough memory, yet still encounter OOM? What is the multi-level allocation mechanism of memory? Why is it designed this way? Analyzing the PyTorch Memory Management Mechanism Mainly Aims to Reduce “Memory Fragmentation” Impact. A … Read more

Ultimate Guide to Installing PyTorch 2.2 with GPU

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Ultimate Guide to Installing PyTorch 2.2 with GPU

1. Deep Learning Workstation 1.1 Configuration First, here is the computer configuration diagram: Upgraded the configuration using the company’s office computer. Except for the graphics card and power supply, other hardware is original from the company computer. 1.2 Graphics Card If you have the budget, go directly for RTX4090, but cannot reuse the company computer … Read more

Key Points for Local Deployment of Large Model Applications

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Key Points for Local Deployment of Large Model Applications

—— Taking Ollama + OpenWebUI deployment in Windows 11 as an example 1.System Requirements Operating System: Windows 11 Memory Requirement: 16GB or more Hardware Requirement: At least 4GB VRAM Nvidia graphics card 2.Installation of Graphics Driver and CUDA ①Graphics Driver: Download and install from the official NVIDIA website ②CUDA Toolkit: This is the key program … Read more

Compiling and Installing GPU-Supported TensorFlow 1.8.0 from Source on Ubuntu 18.04

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Compiling and Installing GPU-Supported TensorFlow 1.8.0 from Source on Ubuntu 18.04

When I first used Linux, the first thing I wanted to do was install the GPU version of TensorFlow. I found a great guide, but even considering that, I spent over 40 hours on the installation. In this article, I want to save you time and share my experience. Below you will find an updated … Read more

Deep Learning Environment Setup Guide for RTX 3090: Pytorch, TensorFlow, Keras

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Deep Learning Environment Setup Guide for RTX 3090: Pytorch, TensorFlow, Keras

Click on the above“Beginner Learning Vision” to add it to your favorites or “pin” Important content delivered to you first Author丨Yukyin@Zhihu Source丨https://zhuanlan.zhihu.com/p/279401802 This article is for academic sharing only. If there is any infringement, please contact us to delete the article. Introduction This article introduces the detailed process and code for setting up a deep … Read more

Step-by-Step Guide to Install TensorFlow GPU Version

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Step-by-Step Guide to Install TensorFlow GPU Version

Introduction The main difference between the CPU version and the GPU version is the running speed; the GPU version runs faster. Therefore, if your computer’s graphics card supports CUDA, it is recommended to install the GPU version. The CPU version requires no additional preparation and can generally be installed on any computer without needing a … Read more