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Paths/MLOps Engineer/Python / Environments & Dependencies

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Pin the Project Dependencies

Set up ~/ml-project with a pinned dependency file, a data directory and a dataset manifest — the small ritual that makes every future experiment reproducible. Every command is explained in the walkthrough.

Validator

⏱ 0:00

IDLE — checks run live as you type commands; submit when done

01Requirements

live checks
  • ~/ml-project/data directory exists○
  • requirements.txt pins scikit-learn 1.5.0○
  • data/datasets.txt lists train.csv and test.csv○

02Walkthrough

8 steps
  1. 01

    Create the project directory

    mkdir makes a new directory. The ~ is shorthand for your home directory (/home/learner), so this creates /home/learner/ml-project.

    mkdir ~/ml-project
  2. 02

    Move into it

    cd changes your current directory. Everything you create next lands inside ~/ml-project.

    cd ~/ml-project
  3. 03

    Add a data directory

    Keeping data separate from code is the first habit of reproducible ML — experiments change, data locations should not.

    mkdir data
  4. 04

    Pin the dependencies

    echo prints text and the > redirect writes it into a file. Pinning the exact version (scikit-learn==1.5.0) means this project trains identically next month.

    echo scikit-learn==1.5.0 > requirements.txt
  5. 05

    Verify the pin

    cat prints a file's contents. You should see the exact pinned version.

    cat requirements.txt
  6. 06

    Write the dataset manifest

    A one-line manifest naming the train and test files. Six months from now this file answers "which data did we use?".

    echo train.csv, test.csv > data/datasets.txt
  7. 07

    Inspect the data directory

    ls lists a directory's contents. datasets.txt should be there.

    ls data
  8. 08

    Submit for validation

    The validator checks that the data directory, the pinned requirements file and the dataset manifest all exist as you built them.

03Stuck?

0 of 3 revealed

Take a hint without losing the lab — each one points at the thing to look at, not at the answer.

04AI Tutor

guides, never solves

Tell me what you're stuck on — I'll nudge, not solve. Which requirement are you looking at?

04Notebook

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learner@lab — ubuntu:24.04

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