( If you don’t have a nvidia GPU on your machine, this tutorial is not for you.)įirst, detect the model of your nvidia graphic card and the recommended driver. It enables you to get started right away without worrying about building custom integrations. The nvidia CUDA toolkit includes GPU-accelerated libraries, a C and C++ compiler and runtime, and optimization and debugging tools. NVIDIA GPUs are the best supported in terms of machine learning libraries and integration with common frameworks, such as PyTorch or TensorFlow.
Discovering and installing nvidia drivers.
In this article, I will cover the setup steps for the right CUDA/cuDNN installation and optimization, being the topics organized as the following: neural networks for image and video detection/segmentation), making the optimization of GPU usage necessary for sustaining the whole training process without crashing. If you work as a Data Scientist for one of those, you may have faced the need of setting up an Ubuntu server with the CUDA/cuDNN and TensorFlow stack for serving training/prediction jobs and pipelines.Īlso, one of the biggest burdens when dealing with AI is running out of memory while training heavy models (e.g.
Usually, companies use cloud-based servers for Machine Learning applications such as Microsoft Azure and Google Cloud Platform, mostly because of their readily available and easy to use services, which are built for a wide range of problems and offer low-code solutions for daily ML operations.Īlthough the majority of companies can deliver value quickly and inexpensively through these tools, there are businesses that rely mainly on their ML capacities as their core product or strategy and most frequently invest lots of money and time on their AI infrastructure and code. How to setup and optimize CUDA and TensorFlow on Ubuntu 20.04 - 2022 Introduction
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