From the course: Docker for Data Scientists
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Creating a common development environment
From the course: Docker for Data Scientists
Creating a common development environment
- [Instructor] Often, when working in a data science or a machine learning team, you want to standardize your environment. Now it's common for data science teams to use Anaconda, so let's start off with Miniconda which is a slimmed down version of Conda and let's say our dev environment requires the following versions for pandas and Seaborn. So I'm going to build my image, so docker build -t, and I'm going to call this dev-env add dot. And this'll take a couple of minutes for it to download all for the different packages required. So let me go ahead and clear my screen. Now, it's common for teams to work from a shared drive or a common network drive. So for the sake of this example, I'm going to say that my C drive is my shared drive. Now, Windows has a hard time dealing with spaces in the path so we need to enclose the path with a double quote. So in this example you're going to be using Docker's Volumes, so docker container run - it -v for volumes. So I then have quotes because my…
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