Next, you’ll need to install dependencies to support the addition of a new package repo (docker) that’s using HTTPS connectivity: $ sudo apt-get install apt-transport-https ca-certificates curl software-properties-commonĪdd Docker’s official GPG key: $ curl -fsSL | sudo apt-key add -Īdd Docker’s official apt repo $ sudo add-apt-repository "deb $(lsb_release -cs) stable" Next, use “ apt-get upgrade” to fetch new versions of packages existing on the machine $ sudo apt-get upgrade This won’t install anything, but simply download the package lists with their latest versions. This component is crucial for rapid experimentation.īegin by updating the apt index and lists. Additionally, it allows you to scale to the cloud and other servers without rebuilding your environment from scratch every time. First, it allows you to track your environment and your model dependencies. What exactly is a container? Containers allow data scientists and developers to wrap up an environment with all of the parts it needs – such as libraries and other dependencies – and ship it all out in one package.ĭocker is an important component when building machine learning models. PrerequisitesĪ computer/server with GPU and Ubuntu 16.04 installed Step 1 – Install Dockerĭocker is a tool designed to make it easier to create, deploy, and run applications by using containers. If you’re working on Deep Learning applications or on any computation that can benefit from GPUs – you’ll probably need this tool. NVIDIA-Docker is a tool created by Nvidia to enable support for GPU devices in the containers. It enables data scientists to build environments once – and ship their training/deployment quickly and easily. Docker was popularly adopted by data scientists and machine learning developers since its inception in 2013. Docker is a tool designed to make it easier to create, deploy, and run applications by using containers. This tutorial will help you set up Docker and Nvidia-Docker 2 on Ubuntu 18.04.
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