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Hamdi LAADHARI
Hamdi LAADHARI

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🐍 The "Production-Ready" Miniconda Cheatsheet: From Homebrew to JupyterLab

#deeplearning #machinelearning #datascience #python

As I started my journey into AI and Data Science, I quickly realized that managing Python environments can be a total headache. Between broken dependencies and 'it works on my machine' errors, I was spending more time troubleshooting my setup than actually writing code.

I wanted a way to streamline my workflow and keep my machine clean, so I moved to a Miniconda + Homebrew setup on my Mac. To save myself (and hopefully you) from digging through endless documentation, I condensed the 'essential' commands into a single, high-density A4 infographic.

This cheatsheet skips the legacy bloat and focuses on a production-first workflow:

  • Clean Installation: Setting up via Homebrew for macOS.

  • Environment Hygiene: Creating, cloning, and exporting environments so your projects stay isolated.

  • Jupyter Integration: Properly registering kernels so your notebooks actually see your installed packages.

  • The Workflow: A step-by-step checklist for starting any new project the right way.

Whether you are a fellow student trying to organize your labs or a developer looking for a 'no-nonsense' reference, I hope this helps you spend less time in the terminal and more time building."

🐍 Miniconda Cheatsheet

📦 Installation (macOS via Homebrew) 

# Install Miniconda
brew install miniconda

# Init conda for zsh
conda init zsh

# Reload shell
source ~/.zshrc

# Verify
conda --version
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🌍 Environment Management 

# List all environments
conda env list

# Create a new environment
conda create --name myenv python=3.11

# Activate an environment
conda activate myenv

# Deactivate current environment
conda deactivate

# Delete an environment
conda env remove --name myenv

# Clone an environment
conda create --name newenv --clone myenv

# Export environment to file (for sharing/backup)
conda env export > environment.yml

# Recreate environment from file
conda env create -f environment.yml

# Show info about current environment
conda info
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📚 Package Management 

# Install a package
conda install numpy

# Install a specific version
conda install numpy=1.26

# Install multiple packages at once
conda install numpy pandas matplotlib scikit-learn

# Install from conda-forge channel (wider package selection)
conda install -c conda-forge jupyterlab

# Install with pip — ONLY when package is not available on conda or conda-forge
# Always check first: conda search -c conda-forge packagename
pip install somepackage

# Update a package
conda update numpy

# Update all packages in active environment
conda update --all

# Remove a package
conda remove numpy

# List installed packages in active environment
conda list

# Search for a package
conda search numpy
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🚀 JupyterLab 

# Install JupyterLab
conda install -c conda-forge jupyterlab

# Launch JupyterLab
jupyter-lab

# Launch from a specific folder
jupyter-lab --notebook-dir=~/projects
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🔌 Kernel Management 

# List available kernels
jupyter kernelspec list

# Register current env as a Jupyter kernel
conda install -c conda-forge ipykernel
python -m ipykernel install --user --name myenv --display-name "Python (myenv)"

# Remove a kernel
jupyter kernelspec remove myenv
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🔧 Conda Maintenance 

# Update conda itself
conda update conda

# Clean unused packages and cache (frees disk space)
conda clean --all

# Show conda configuration
conda config --show

# Add conda-forge as default channel
conda config --add channels conda-forge
conda config --set channel_priority strict
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💡 Typical Project Workflow 

# 1. Create and activate a fresh environment
conda create --name myproject python=3.11
conda activate myproject

# 2. Install packages (always prefer conda over pip inside conda envs)
conda install -c conda-forge jupyterlab numpy pandas matplotlib scikit-learn ipykernel

# 3. Register as a Jupyter kernel
python -m ipykernel install --user --name myproject --display-name "Python (myproject)"

# 4. Launch JupyterLab
jupyter-lab

# 5. When done, deactivate
conda deactivate

# 6. Export environment for reproducibility
conda env export > environment.yml
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🗂️ Quick Reference

Task Command
Create env conda create --name myenv python=3.11
Activate env conda activate myenv
Deactivate env conda deactivate
Delete env conda env remove --name myenv
Install package conda install numpy
Remove package conda remove numpy
List packages conda list
List envs conda env list
Launch JupyterLab jupyter-lab
Export env conda env export > environment.yml
Update conda conda update conda
Clean cache conda clean --all

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