Step-by-Step Guide: Cloud-GPU Jupyter Notebook Setup for Model Training & Interactive EDA

Interactive Environment Guide: Step-by-step walkthrough to configure JupyterLab with GPU acceleration, interactive ipywidgets visualizations, and reproducible notebook pipelines.

Step 1: Set Up virtualenv & JupyterLab Extension Suite

python -m venv .venv
source .venv/bin/activate
pip install jupyterlab ipywidgets matplotlib seaborn pandas torch
jupyter labextension install @jupyter-widgets/jupyterlab-manager

Step 2: Enable Interactive Data Visualizations

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Enable inline high-DPI plotting
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
sns.set_theme(style="darkgrid")

Step 3: Monitor GPU Memory Inside Notebook Cells

import torch
if torch.cuda.is_available():
    print(f"Active GPU: {torch.cuda.get_device_name(0)}")
    print(f"Allocated VRAM: {torch.cuda.memory_allocated(0) / 1e9:.2f} GB")