August 2021 Summaries

This post explains how to install TensorFlow and PyTorch along with their dependencies on an NVIDIA RTX 3090, 3080, or 3070 GPU using Lambda Stack, a freely available Ubuntu 20.04 APT package. The installation process is completed in under 2 minutes and includes the necessary dependencies such as CUDA v11.1 and cuDNN v8.2.1. The system requirements include an NVIDIA GPU and Ubuntu 20.04 LTS (Focal Fossa). The post also provides instructions on how to manage the TensorFlow and PyTorch installation, including upgrading to new versions. Additionally, Lambda Labs offers customizable GPU workstations, servers, or cloud instances for those interested in deep learning applications.

The text discusses a comparison of the RTX A6000 and RTX 3090 GPUs in terms of their performance for deep learning tasks, specifically PyTorch training. The benchmarks show that a single RTX A6000 is faster than an RTX 3090 when training image models with mixed precision, but slightly slower when using 32-bit precision. However, when training language models, the RTX A6000 consistently outperforms the RTX 3090 by at least 1.3x. The performance difference is attributed to the extra memory on the RTX A6000, which helps alleviate memory bottlenecks in language model training. Additionally, the text highlights that using multiple GPUs can significantly improve training speed for both image and language models.