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google-colab-guide

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Run and manage Google Colab notebooks for Python and ML research

skills/tools/code-exec/google-colab-guide/SKILL.md

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# Google Colab Guide

Run Python code, train machine learning models, and perform data analysis using Google Colab's free cloud-hosted Jupyter notebooks with GPU and TPU access. This skill covers setup, resource management, persistent storage, and best practices for reproducible research computing.

## Overview

Google Colab (Colaboratory) provides free access to GPU-accelerated Jupyter notebooks running on Google's cloud infrastructure. For academic researchers, Colab eliminates the barrier of expensive hardware for machine learning experiments, large-scale data processing, and computationally intensive statistical analyses. The free tier includes NVIDIA T4 GPUs, and paid tiers (Colab Pro, Pro+) offer A100 GPUs and extended runtime.

Colab notebooks run in ephemeral virtual machines that are recycled after inactivity or maximum runtime. This creates unique challenges for research: managing persistent data, saving checkpoints, reproducing results, and working with large datasets. This skill addresses these challenges with proven patterns used by ML researchers worldwide.

Colab integrates natively with Google Drive for storage, GitHub for version control, and supports the full Python scientific computing ecosystem (NumPy, pandas, scikit-learn, PyTorch, TensorFlow, JAX). Each notebook runs in an isolated environment with root access, allowing installation of any Linux package or Python library.

## Getting Started
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