Jupyter Notebooks

Set up Jupyter Notebooks for deep learning. Install JupyterLab, configure remote access, troubleshoot kernel issues, and optimize for GPU workflows with PyTorch and TensorFlow.

Jupyter Notebook Setup

Jupyter Notebooks are essential for interactive deep learning development and experimentation.

Installation

# Activate your environment
source ml-env/bin/activate

# Install Jupyter
pip install jupyter notebook jupyterlab

# Launch Jupyter
jupyter notebook
# or
jupyter lab

# Activate your environment
conda activate ml-env

# Install Jupyter
conda install jupyter notebook jupyterlab

# Launch Jupyter
jupyter notebook

Adding Environment as Jupyter Kernel

To use your Python environment in Jupyter notebooks:

# Activate your environment
source ml-env/bin/activate

# Install ipykernel
pip install ipykernel

# Add environment as kernel
python -m ipykernel install --user --name ml-env --display-name "Python 3.11 (ML)"

# Verify it's added
jupyter kernelspec list

# Activate your environment
conda activate ml-env

# Install ipykernel
conda install ipykernel

# Add environment as kernel with descriptive name
python -m ipykernel install --user --name ml-env --display-name "Python 3.11 (ML - Conda)"

# Verify it's added
jupyter kernelspec list

Recommended naming convention:

# Include Python version and environment purpose
python -m ipykernel install --user --name tfmain --display-name "Python 3.10 (TensorFlow)"
python -m ipykernel install --user --name pytorch-env --display-name "Python 3.11 (PyTorch)"

Managing Kernels

List All Kernels

jupyter kernelspec list

Output example:

Available kernels:
  ml-env       /home/user/.local/share/jupyter/kernels/ml-env
  python3      /usr/share/jupyter/kernels/python3
  pytorch-env  /home/user/.local/share/jupyter/kernels/pytorch-env

Remove Unused Kernel

# Remove a kernel
jupyter kernelspec uninstall unwanted-kernel

# Example
jupyter kernelspec uninstall old-tf-env

Update Kernel Display Name

# Remove old kernel
jupyter kernelspec uninstall old-name

# Re-add with new display name
python -m ipykernel install --user --name myenv --display-name "New Display Name"

Jupyter Notebook Tips

Timing Cell Execution

%%time
# Your code here
model.train()

Shows total execution time for the cell.

%%timeit
# Your code here - runs multiple times for accurate measurement
result = model(data)

Runs code multiple times and provides statistics.

# Install extension for persistent timing
pip install jupyter_contrib_nbextensions
jupyter contrib nbextension install --user
jupyter nbextension enable execute_time/ExecuteTime

# Restart Jupyter to see execution times in cells automatically

Shows execution time for every cell automatically.

GPU Monitoring in Notebooks

# Check GPU availability
import torch
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"GPU: {torch.cuda.get_device_name(0)}")
print(f"GPU count: {torch.cuda.device_count()}")

# Monitor GPU memory
print(f"Memory allocated: {torch.cuda.memory_allocated(0) / 1e9:.2f} GB")
print(f"Memory reserved: {torch.cuda.memory_reserved(0) / 1e9:.2f} GB")

# Check GPU availability
import tensorflow as tf
gpus = tf.config.list_physical_devices('GPU')
print(f"GPUs available: {len(gpus)}")
for gpu in gpus:
    print(f"  - {gpu}")

# Get GPU memory info
gpu_devices = tf.config.experimental.list_physical_devices('GPU')
for device in gpu_devices:
    tf.config.experimental.set_memory_growth(device, True)

# Monitor all GPUs in real-time
!nvidia-smi

# Watch GPU usage continuously
!watch -n 1 nvidia-smi  # Updates every second

Auto-Reload Modules

# Add to first cell - automatically reload changed modules
%load_ext autoreload
%autoreload 2

Better Output Display

# Display all outputs (not just last one)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

Common Issues

Issue: Empty Browser Page on Jupyter Start

Problem: Jupyter launches but shows blank page

Solution:

# Hard refresh browser
Ctrl + Shift + R  # Windows/Linux
Cmd + Shift + R   # Mac

# Or clear browser cache and restart Jupyter
jupyter notebook --no-browser
# Then manually open the URL shown

Issue: Kernel Not Found / “Dead Kernel”

Problem: Notebook can’t connect to kernel

Solution:

# 1. Check if kernel exists
jupyter kernelspec list

# 2. Reinstall ipykernel in environment
source ml-env/bin/activate
pip install --force-reinstall ipykernel
python -m ipykernel install --user --name ml-env --display-name "Python (ML)"

# 3. Restart Jupyter

# 1. Check if kernel exists
jupyter kernelspec list

# 2. Reinstall ipykernel in environment
conda activate ml-env
conda install --force-reinstall ipykernel
python -m ipykernel install --user --name ml-env --display-name "Python (ML)"

# 3. Restart Jupyter

Issue: Wrong Python Environment

Problem: Notebook uses system Python instead of your environment

Solution:

# Verify kernel Python path
jupyter kernelspec list
cat ~/.local/share/jupyter/kernels/ml-env/kernel.json

# Should point to your environment, e.g.:
# "/home/user/ml-env/bin/python"

# If wrong, remove and re-add kernel
jupyter kernelspec uninstall ml-env
source ml-env/bin/activate
python -m ipykernel install --user --name ml-env

Issue: ImportError for Packages Installed in Environment

Problem: Package installed but import fails in notebook

Check you’re using the right kernel:

# In notebook cell
import sys
print(sys.executable)
# Should show: /home/user/ml-env/bin/python

# If wrong, change kernel:
# Kernel → Change Kernel → Select correct environment

Issue: Jupyter Lab Extensions Not Loading

# Rebuild JupyterLab
jupyter lab build

# Or clear cache and rebuild
jupyter lab clean
jupyter lab build

Issue: “Charset Normalizer” Error

Error:

AttributeError: partially initialized module 'charset_normalizer'
has no attribute 'md__mypyc' (most likely due to a circular import)

Fix:

pip install -U --force-reinstall charset-normalizer

JupyterLab vs Jupyter Notebook

Jupyter Notebook (Classic)

Simpler interface
Lighter weight
Better for single-notebook work

jupyter notebook

JupyterLab (Modern)

Multi-tab interface
Built-in terminal
File browser
Extension system

jupyter lab

Recommendation: Use JupyterLab for most work, Notebook for simplicity.

Remote Jupyter Access

SSH Tunneling

On remote machine:

jupyter notebook --no-browser --port=8888

On local machine:

# Create SSH tunnel
ssh -L 8888:localhost:8888 user@remote-server

# Open browser to http://localhost:8888

JupyterHub (Multi-user)

For team environments, consider JupyterHub:

Multi-user support
Resource management
Authentication

See JupyterHub docs for setup.

Productivity Extensions

Recommended Extensions

# Install extension manager
pip install jupyter_contrib_nbextensions
jupyter contrib nbextension install --user

# Enable useful extensions
jupyter nbextension enable execute_time/ExecuteTime
jupyter nbextension enable toc2/main  # Table of contents
jupyter nbextension enable varInspector/main  # Variable inspector
jupyter nbextension enable codefolding/main  # Code folding

JupyterLab Extensions

# GPU monitoring dashboard
pip install jupyterlab-nvdashboard
jupyter labextension install jupyterlab-nvdashboard

# Git integration
pip install jupyterlab-git

Best Practices

Notebook Organization

# Cell 1: Imports and setup
import torch
import numpy as np
%load_ext autoreload
%autoreload 2

# Cell 2: Configuration
BATCH_SIZE = 32
LEARNING_RATE = 1e-3
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'

# Cell 3+: Your work
...

Save Notebooks as Scripts

# Convert notebook to Python script
jupyter nbconvert --to script notebook.ipynb

# Generates notebook.py

Version Control

# Add to .gitignore
*.ipynb_checkpoints/
.ipynb_checkpoints

# Clean output before committing
jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace notebook.ipynb

Next Steps

Set up Python environments for isolated dependencies
GPU troubleshooting if GPU not detected
Monitor training from notebooks

:::tip[Kernel Best Practices] Create one kernel per project environment. Use descriptive names like “Python 3.11 (CV Project)” to easily identify which kernel to use. :::

Jupyter Notebooks