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
Installing TensorFlow on Windows Installing Python First, you need to install Python on your Windows system. It is recommended to use the official Python distribution, which is Anaconda, as it comes with many scientific computing libraries, such as numpy and scipy, which are also used in TensorFlow. You can download and install Anaconda from the … Read more
Click the above “Beginner Learning Vision” to select Star or Pin. Important content delivered promptly This article is transferred from | Computer Vision Alliance Overview tf.distribute.Strategy is a TensorFlow API used to distribute training across multiple GPUs, multiple machines, or TPUs. With this API, you can distribute existing models and training code with minimal code … Read more
TensorFlow programs can specify the device for each operation using the tf.device function. This device can be a local CPU or GPU, or it can be a remote server. TensorFlow assigns a name to each available device, and the tf.device function can specify the device for executing operations using the device’s name. For example, the … Read more
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
Click the “Beginner’s Guide to Vision” above, and choose to add “Star” or “Top“ Heavy content delivered promptly Why Use Multi-GPU Parallel Training In simple terms, there are two reasons: the first is that the model cannot fit on a single GPU, but can run completely on two or more GPUs (like the early AlexNet). … Read more
Click on the above “Beginner Learning Visuals” to select “Star” or “Pin” Important content delivered to you first Selected from | Medium Author | Aakash N S Contributors | Panda This article is the fourth in this series and will introduce how to train deep neural networks using PyTorch on a GPU. In previous tutorials, … Read more
Source: Read Chip Technology This article is approximately 4800 words, and it is recommended to read it in 10 minutes. This article introduces 9 tips for training neural networks using Pytorch. Image Source: unsplash.com/@dulgier In fact, your model may still be at the level of the Stone Age. You might still be training with 32-bit … Read more
Click the above “Beginner’s Guide to Vision” to select “Star” or “Top” Heavyweight content delivered first-hand This article is sourced from | Visual Algorithms Don’t let your neural network end up like this Let’s face it, your model might still be stuck in the Stone Age. I bet you’re still using 32-bit precision or GASP … Read more
Author: z.defying Reprinted from: Datawhale Table of Contents: 1 Specify GPU ID 2 View Model Layer Output Details 3 Gradient Clipping 4 Expand Dimensions of a Single Image 5 One-Hot Encoding 6 Prevent Out of Memory When Validating Model 7 Learning Rate Decay 8 Freeze Parameters of Certain Layers 9 Use Different Learning Rates for … Read more