hands-on_machine_learning_oreilly

Exercises and examples from O'Reilly's "Hands-on Machine Learning with Scikit-Learn, Keras & TernsorFlow" by A. Geron (3rd ed.)

Description

This repository is an accompanying journal on my path of going through examples and exercises from "Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow" book from o'Reilly. It follows book's chapter division, partially for ease of navigation, but also due to the fact that different chapters may require different environment set-up.

Runbook

For Python modules

Please follow these steps to set-up and activate a local virtual environment:

1. Make sure you're at the root of the project

2. Set-up the virtual environment

   python -m venv shared_venv/

3. Activate your virtual environment (example below is for macOS/Linux)

   source shared_venv/bin/activate

4. Install required dependencies:

   pip install -r requirements.txt

5. [optional, for Visual Studio Code] If you want to run/edit the project from Visual Studio Code:

   1. Make sure you have Python extension for Visual Studio Code installed
   2. Open command pallete (Ctr + Shift + P on Linux) and select Python: Select Interpreter
   3. Pick the option containing shared_venv

To run a Python file, run a main module like this:

python3 -m chapter_03.ex03.main

To run tests for a certain chapter:

# change chapter folder accordingly
cd chapter_02/
python -m pytest

Docker setup

For Jupyter Notebook examples I've used initial containerized environment with a small fix. Simply navigate to docker folder and just run: docker compose up.

This setup is based on Dockerfile.gpu.

Build image

This should be ideally done only once:

cd docker/
# build docker compose image from Dockerfile.gpu
# Note that this may take a while (12 minutes on my machine)
docker compose build

Run

# click on one of the produced links to start adding/editing Jupyter Notebook entries
docker compose up
...
handson-ml3  |     To access the server, open this file in a browser:
handson-ml3  |         file:///home/devel/.local/share/jupyter/runtime/jpserver-1-open.html
handson-ml3  |     Or copy and paste one of these URLs:
handson-ml3  |         http://62903a64ecf6:8888/lab?token=87cbe0058148e33d6702d94d243525acb18f7577ff00ca5d
handson-ml3  |         http://127.0.0.1:8888/lab?token=87cbe0058148e33d6702d94d243525acb18f7577ff00ca5d
handson-ml3  | [I 2024-06-24 23:49:32.937 ServerApp] Skipped non-installed server(s): bash-language-server, dockerfile-language-server-nodejs, javascript-typescript-langserver, jedi-language-server, julia-language-server, pyright, python-language-server, python-lsp-server, r-languageserver, sql-language-server, texlab, typescript-language-server, unified-language-server, vscode-css-languageserver-bin, vscode-html-languageserver-bin, vscode-json-languageserver-bin, yaml-language-server

⚠️ Should the above docker container be automatically restarted by the Docker engine on your laptop, just look for the last logs for the running container, e.g.:

docker logs --tail 15 handson-ml3
    To access the server, open this file in a browser:
        file:///home/devel/.local/share/jupyter/runtime/jpserver-1-open.html
    Or copy and paste one of these URLs:
        http://62903a64ecf6:8888/lab?token=b99d270fee1d46985f3f58abb29dabf913672ff971da673c
        http://127.0.0.1:8888/lab?token=b99d270fee1d46985f3f58abb29dabf913672ff971da673c
[I 2024-08-19 20:10:40.091 ServerApp] Skipped non-installed server(s): bash-language-server, dockerfile-language-server-nodejs, javascript-typescript-langserver, jedi-language-server, julia-language-server, pyright, python-language-server, python-lsp-server, r-languageserver, sql-language-server, texlab, typescript-language-server, unified-language-server, vscode-css-languageserver-bin, vscode-html-languageserver-bin, vscode-json-languageserver-bin, yaml-language-server
[I 2024-08-20 22:19:51.983 ServerApp] 302 GET / (@172.18.0.1) 0.41ms
[I 2024-08-20 22:19:51.985 LabApp] 302 GET /lab? (@172.18.0.1) 0.46ms
[I 2024-08-20 22:20:23.947 LabApp] 302 GET /lab?token=[secret] (@172.18.0.1) 0.52ms
[W 2024-08-20 22:22:28.435 LabApp] Could not determine jupyterlab build status without nodejs
[I 2024-08-20 22:22:29.358 ServerApp] Kernel started: b6936699-0de1-4051-a6a1-337ea2696f9c
[I 2024-08-20 22:22:30.748 ServerApp] Connecting to kernel b6936699-0de1-4051-a6a1-337ea2696f9c.
[I 2024-08-20 22:22:30.762 ServerApp] Connecting to kernel b6936699-0de1-4051-a6a1-337ea2696f9c.
[I 2024-08-20 22:22:30.776 ServerApp] Connecting to kernel b6936699-0de1-4051-a6a1-337ea2696f9c.