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--- | ||
title: Data scientists | ||
description: Get started with Coder as a data scientist. | ||
--- | ||
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This article will walk you through the process of getting started with a Coder | ||
workspace capable of supporting data science projects. You'll learn how to: | ||
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- Connect Coder to your Git provider (this example assumes that you're using | ||
GitHub, but Coder supports GitLab and Bitbucket as well)); | ||
- Create a workspace with Jupyter Notebook and other data science packages | ||
present; | ||
- Add a sample project to your workspace, specifically | ||
[one as a Jupyter Notebook using IMDB movie data](https://github.com/khorne3/data-science-imdb-sample); | ||
- Create a dev URL and preview changes to your project. | ||
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## Prerequisites | ||
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This guide assumes that you have a Coder deployment available to you and that | ||
you have the credentials needed to access the deployment. | ||
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## Step 1: Log in and connect Coder to your Git provider | ||
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In this step, you'll log into Coder, then link your Coder account with your Git | ||
provider. This will allow you to do things like pull repositories and push | ||
changes. | ||
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1. Navigate to the Coder deployment using the URL provided to you by your site | ||
manager, and log in. | ||
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1. Click on your avatar in the top-right, and select **Account**. | ||
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 | ||
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1. Provide Coder with your SSH key to connect and authenticate to GitHub. | ||
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If your site manager has configured OAuth, go to **Linked Accounts** and | ||
follow the on-screen instructions to link your GitHub account. | ||
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 | ||
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If your site manager has _not_ configured OAuth or you are using a Git | ||
provider that Coder does not support, go to **SSH keys**. Copy your public | ||
SSH key and | ||
[provide it to GitHub](https://docs.github.com/en/authentication/connecting-to-github-with-ssh/adding-a-new-ssh-key-to-your-github-account). | ||
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 | ||
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## Step 2: Import an image | ||
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At this point, you'll import your image, which you can think of as a template | ||
for your workspace. This template contains the language version, tooling, and | ||
dependencies you need to work on the project. In this case, the image also | ||
contains a `configure` script that will clone the data science project from | ||
GitHub to your workspace. | ||
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To import an image: | ||
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1. In the top navigation bar, click **Images**. Then, click on **Import Image**. | ||
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1. Leave the default registry (which is **dockerhub**) selected. | ||
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1. Under **repository**, provide **kmhcdr/python**. Provide **latest** as the | ||
**tag**. Optionally, you can provide a **description** of the image | ||
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1. Specify the minimum amount of resources (cores, memory, and disk space) the | ||
workspace should have when using this image. For this project, we recommend 4 | ||
cores, 8 GB memory, and 10 GB disk space as a starting point. | ||
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1. Click **Import Image**. | ||
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 | ||
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## Step 3: Create your workspace | ||
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You will now create the workspace where you'll work on your development project. | ||
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1. Return to **Workspaces** using the top navigation bar. | ||
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1. Click **New workspace** to launch the workspace-creation dialog. | ||
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1. Provide a **Workspace Name**. | ||
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1. In the **Image** section, select the **kmhcdr/python** image you just | ||
imported. | ||
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1. Under **Workspace providers**, leave the default option (which is | ||
**built-in**) selected. | ||
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1. Expand the **Advanced** section. If the **Run as a container-based virtual | ||
machine** option is selected, _unselect_ the box. Leave the **CPU**, | ||
**Memory**, **Disk**, and **GPU** allocations as-is. | ||
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1. Scroll to the bottom, and click **Create workspace**. The dialog will close, | ||
allowing you to see the main workspace page. You can track the workspace | ||
build process using the **Build log** on the right-hand side. | ||
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Due to the number of packages present in the image, this might take few | ||
minutes. | ||
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 | ||
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Once your workspace is ready for use, you'll see a chip that says **Running** | ||
next to the name of your workspace. | ||
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## Step 4: Open up the sample project | ||
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At this point, you're ready to open up Jupyter to access your notebook. | ||
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1. Under **Browser applications**, click **Jupyter** to open the IDE in a new | ||
browser tab. | ||
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1. Under **Files**, click to open the **data-science-imdb-sample** project. | ||
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1. Click **Data Science Workflow.ipynb** to launch the notebook. | ||
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You're now ready to proceed with work on the project. | ||
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 |
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