Education Playground Setup Guide

Education Playground Setup Guide#

Complete installation instructions for getting started with Education Playground.

Table of Contents#

Prerequisites#

Required#

  • Python 3.10 or higher (Download)

  • Git (Download)

  • 4GB+ RAM (8GB+ recommended for deep learning)

  • 5GB+ free disk space

Installation Methods#

Method 2: Using Conda#

Great if you already use Anaconda/Miniconda.

Step 1: Clone Repository#

git clone https://github.com/mykolas-perevicius/Education_Playground.git
cd Education_Playground

Step 2: Create Conda Environment#

conda create -n education-playground python=3.11
conda activate education-playground

Step 3: Install Dependencies#

# Install most packages via conda (faster, pre-compiled)
conda install jupyter numpy pandas matplotlib scikit-learn

# Install remaining via pip
pip install -r requirements.txt

Step 4: Start Jupyter#

jupyter notebook

Method 3: Using Docker#

For advanced users who want a containerized environment.

Step 1: Create Dockerfile#

FROM python:3.11-slim

WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    git \
    && rm -rf /var/lib/apt/lists/*

# Copy requirements
COPY requirements.txt .

# Install Python packages
RUN pip install --no-cache-dir -r requirements.txt

# Download NLTK data
RUN python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords'); nltk.download('wordnet')"

# Copy project files
COPY . .

EXPOSE 8888

CMD ["jupyter", "notebook", "--ip=0.0.0.0", "--port=8888", "--no-browser", "--allow-root"]

Step 2: Build and Run#

docker build -t education-playground .
docker run -p 8888:8888 -v $(pwd):/app education-playground

Verification#

Test your installation by running this in Python or a Jupyter cell:

# Test imports
import sys
print(f"Python version: {sys.version}")

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn
import tensorflow as tf

print(f"NumPy: {np.__version__}")
print(f"Pandas: {pd.__version__}")
print(f"Scikit-learn: {sklearn.__version__}")
print(f"TensorFlow: {tf.__version__}")

print("\n✓ All core packages installed successfully!")

Run the Calibration Test#

jupyter notebook 00_calibration_test.ipynb

This will help you determine which level to start with.

IDE Setup#

JupyterLab#

For a browser-based IDE with advanced features:

pip install jupyterlab
jupyter lab

Benefits:

  • Multi-tab interface

  • Built-in terminal

  • File browser

  • Extension ecosystem

PyCharm#

  1. Open Education_Playground as a project

  2. Configure Python interpreter: File > Settings > Project > Python Interpreter

  3. Install Jupyter support: Settings > Plugins > Jupyter

  4. Open .ipynb files directly in PyCharm

Troubleshooting#

Issue: jupyter: command not found#

Solution: Your virtual environment isn’t activated or Jupyter isn’t installed.

# Reactivate virtual environment
source venv/bin/activate  # macOS/Linux
venv\Scripts\activate     # Windows

# Reinstall Jupyter
pip install jupyter

Issue: TensorFlow installation fails#

Solution: TensorFlow has specific requirements.

On Apple Silicon (M1/M2/M3 Macs):

pip install tensorflow-macos
pip install tensorflow-metal  # For GPU acceleration

On Windows with GPU:

pip install tensorflow[and-cuda]

CPU-only (any platform):

pip install tensorflow-cpu

Issue: NLTK data not found#

Solution: Download required NLTK datasets:

import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('averaged_perceptron_tagger')

Issue: Kernel keeps dying#

Possible causes:

  • Insufficient RAM (need 4GB+, 8GB+ for deep learning)

  • Memory leak in code

  • Outdated packages

Solutions:

# Update packages
pip install --upgrade jupyter ipykernel

# Restart kernel: Kernel > Restart in Jupyter

# Clear outputs before running: Cell > All Output > Clear

Issue: Import errors after installation#

Solution: Wrong Python interpreter selected.

# Check which Python you're using
which python   # macOS/Linux
where python   # Windows

# Should point to your venv/conda environment
# If not, reactivate your environment

Issue: Slow pip installation#

Solution: Use pip’s cache and parallel downloads:

pip install -r requirements.txt --cache-dir ~/.cache/pip

Or switch to conda for faster binary installs:

conda install --file requirements.txt

Platform-Specific Notes#

macOS#

  • Use python3 instead of python if you have system Python 2.x

  • May need Xcode Command Line Tools: xcode-select --install

  • For M1/M2/M3 chips, some packages have ARM-specific versions

Windows#

  • Enable long path support (for deep learning models): Run as Administrator

    reg add HKLM\SYSTEM\CurrentControlSet\Control\FileSystem /v LongPathsEnabled /t REG_DWORD /d 1

  • Use PowerShell or Windows Terminal (better than CMD)

  • If permission errors, run terminal as Administrator

Linux#

  • May need build tools: sudo apt-get install python3-dev build-essential

  • For GPU support: Install CUDA toolkit and cuDNN

  • Check Python version: Many distros ship with older versions

Next Steps#

  1. Take the calibration test: 00_calibration_test.ipynb

  2. Navigate to your level: easy/, medium/, or hard/

  3. Read the README: Comprehensive course overview

  4. Check out resources: RESOURCES.md for external learning materials

  5. Review cheatsheets: Quick references as you learn

Getting Help#

Optional: GPU Setup for Deep Learning#

NVIDIA GPU (CUDA)#

Check GPU availability:

import tensorflow as tf
print(f"GPUs available: {tf.config.list_physical_devices('GPU')}")

import torch
print(f"CUDA available: {torch.cuda.is_available()}")

Install CUDA support:

  1. Install NVIDIA CUDA Toolkit

  2. Install cuDNN

  3. Install GPU-enabled versions:

# TensorFlow with GPU
pip install tensorflow[and-cuda]

# PyTorch with GPU (check https://pytorch.org for your CUDA version)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Apple Silicon (M1/M2/M3)#

pip install tensorflow-macos tensorflow-metal
# PyTorch automatically supports MPS (Metal Performance Shaders)

Happy Learning! You’re all set to start your programming journey. 🚀

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