Abhigyan Chakraborty

Setting Up VS Code

Phase 1: The Local Workbench — Part 4

(Part 4 of series Blueprint for a Modern Research Computing Environment)

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Quick Summary

This article installs VS Code, connects it to your Ubuntu environment (via Remote WSL on Windows, or natively on Linux), and wires up the Python and Jupyter extensions. You’ll then select env_project1 as your interpreter and kernel, and run both first_script.py and your notebook from Part 3 directly inside VS Code — one place for terminal, Git, debugger, scripts, and notebooks.

Objective

In Part 3, we ran Python code in both a .py script and a .ipynb notebook using JupyterLab. In this part, we’ll set up VS Code — a single environment where you can work with both file types, alongside your terminal, Git, and debugger.

By the end, you’ll have:

Content

💡 Getting Unstuck (Expand for AI Troubleshooting Prompts)

AI tools (like Claude, Grok, Gemini, or ChatGPT) give the best troubleshooting advice when they have the exact text of the tutorial. To ensure the AI understands what you are trying to build, we will give it the actual file.

1. Download the tutorial file

2. Upload it to your AI

3. Ask for Help Copy and paste this exact prompt into the chat along with your file:

I have attached the markdown file for the tutorial I am following. Please read it so you understand the specific environment I am trying to build.

I need help with the following:

Step [X]: [paste exact step text from the blog]

Command I ran: [paste exact command]

What happened: [paste full output/error — if the terminal was truly blank, say so explicitly]

Please help me troubleshoot and fix this error. You can use your general knowledge to solve the problem, but your solution MUST align with the architecture and tools taught in the attached file. Do not suggest alternative setups that contradict the tutorial. Once fixed, tell me what to do next in the article.

To go deeper on a step before you run a command:

I have attached the Part4.md file. Look at Step [X] and explain exactly what the command does and why we are doing it before I run it.

Think of this series as the roadmap and your AI assistant as your learning companion.

Prerequisites

Step 1 — Installing VS Code

Windows (WSL) Users

Go to https://code.visualstudio.com/ and click Download for Windows.

Run the downloaded file — it will be named something like VSCodeUserSetup-x64-1.xx.x.exe.

The installer will walk you through these screens:

  1. License Agreement — Select I accept the agreement → click Next
  2. Select Destination Location — leave the default path as is → click Next
  3. Select Start Menu Folder — leave as is → click Next
  4. Select Additional Tasks — this is the important screen. You will see a list of checkboxes. Make sure these two are checked:
    • ✅ Add to PATH — allows you to type code . in the terminal to open VS Code from any folder
    • ✅ Open with Code (under the File Explorer options) — lets you right-click any folder in Windows Explorer and open it in VS Code
    • Leave everything else as is → click Next
  5. Ready to Install — click Install
  6. Finish — leave Launch Visual Studio Code checked → click Finish

Now open your Ubuntu terminal and verify:

code --version

You should see a version number. If you get command not found, close the terminal completely, reopen it, and try again.

Native Linux Users

Open your terminal and run these commands one by one:

sudo apt update

sudo apt install wget gpg

wget -qO- [https://packages.microsoft.com/keys/microsoft.asc](https://packages.microsoft.com/keys/microsoft.asc) | gpg --dearmor > packages.microsoft.gpg

sudo install -D -o root -g root -m 644 packages.microsoft.gpg /etc/apt/keyrings/packages.microsoft.gpg

echo "deb [arch=amd64,arm64,armhf signed-by=/etc/apt/keyrings/packages.microsoft.gpg] [https://packages.microsoft.com/repos/code](https://packages.microsoft.com/repos/code) stable main" | sudo tee /etc/apt/sources.list.d/vscode.list > /dev/null

sudo apt update

sudo apt install code

Verify:

code --version

You should see a version number printed.

Step 2 — Installing the Remote WSL Extension

Windows (WSL) users only. Native Linux users skip to Step 3.

The Remote WSL extension allows VS Code running on Windows to connect to your Ubuntu environment. Without it, VS Code won’t see your Linux files, Conda environments, or packages.

  1. Open VS Code
  2. Press Ctrl+Shift+X to open the Extensions panel
  3. Search for Remote - WSL
  4. Click Install

Step 3 — Installing Python and Jupyter Extensions

Open VS Code. Press Ctrl+Shift+X to open the Extensions panel.

Search for and install these two extensions:

These give VS Code the ability to:

Step 4 — Opening Your Project in VS Code

Activate your environment and navigate to your project folder in the terminal:

conda activate env_project1

cd ~/project_1

Open VS Code from the terminal:

code .

WSL users: VS Code will open on Windows and automatically connect to your Ubuntu environment via the Remote WSL extension. You’ll see a green indicator in the bottom-left corner of VS Code showing WSL: Ubuntu.

Native Linux users: VS Code opens locally with ~/project_1 as the workspace.

From this point, everything is identical for both.

Step 5 — Selecting a Conda Interpreter for .py Files

VS Code needs to know which Python environment to use when running .py files.

  1. Press Ctrl+Shift+P to open the Command Palette
  2. Type Python: Select Interpreter and press Enter
  3. You’ll see a list of available Python environments
  4. Select Python (env_project1)

VS Code will now use env_project1 when running any .py file in this project.

Step 6 — Running first_script.py

In the VS Code file browser on the left, click on first_script.py to open it.

To run it:

The output will appear in the Terminal panel at the bottom:

Array: [1 2 3 4 5]
Mean: 3.0
Sum: 15

Same output as Part 3 — but now running directly inside VS Code.

Step 7 — Selecting a Jupyter Kernel for .ipynb Files

In the VS Code file browser on the left, click on your .ipynb notebook to open it.

In the top-right corner of the notebook, you’ll see a kernel selector showing either a kernel name or Select Kernel. Click it.

A dropdown will appear at the top of the screen. Select Python (env_project1).

This connects your notebook to the same Conda environment you’ve been working in.

Step 8 — Running Your Notebook in VS Code

With your notebook open and env_project1 selected as the kernel, you’ll see the same cells you wrote in Part 3.

Key notebook controls in VS Code:

Action How
Run a cell Shift+Enter or click ▷ next to the cell
Run all cells Click Run All in the top toolbar
Clear output of a cell Right-click the cell → Clear Cell Output
Clear all outputs Click Clear All Outputs in the top toolbar
Restart kernel Click Restart in the top toolbar
Restart and run all Click Restart → Restart and Run All Cells

Run your notebook. Expected output in the first cell:

Array: [1 2 3 4 5]
Mean: 3.0
Sum: 15

Same result as Part 3 — the environment, the code, and the output are identical. The difference is you now have your terminal, file browser, Git, and debugger all in the same window.

What’s Next

What You’ve Done:

Next: Part 5 — Linux Essentials for HPC and Remote Servers | Previous: Part 3 — Managing Projects with Conda Environments

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