Coding¶

Environments¶

uv (Recommended)¶

uv is a fast Python package and project manager written in Rust. It replaces pip, pip-tools, pipx, poetry, pyenv, virtualenv, and more.

Installation:

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# or via Homebrew
brew install uv

Project setup for examples and demo as subfolder within this project:

# Initialize a new project with pyproject.toml
uv init

# Create virtual environment and install dependencies
uv sync

# Add a dependency
uv add numpy pandas torch

# Run a script
uv run python script.py

# Run Jupyter
uv run jupyter lab

Quick environment for existing projects:

# Create venv and install from pyproject.toml or requirements.txt
uv venv
source .venv/bin/activate
uv pip install -r requirements.txt

Each project folder contains a pyproject.toml for dependency management.

VSCode¶

Jupyter Notebook¶

• To select an environment, use the Python: Select Interpreter command from the Command Palette (⇧⌘P)
• Use Create: New Jupyter Notebook from command Palette
• Select a kernel using the kernel picker in the top right.
• Within a Python Notebook, it's possible to view, inspect, sort, and filter the variables within the current Jupyter session, using Variables in toolbar.
• We can offload intensive computation in a Jupyter Notebook to other computers by connecting to a remote Jupyter server. Use server URL with security token.

Run Kaggle image¶

As an alternate Kaggle has a more complete docker image to start with.

# CPU based
docker run --rm -v $(pwd):/home -it gcr.io/kaggle-images/python /bin/bash
# GPU based
docker run -v $(pwd):/home --runtime nvidia --rm -it gcr.io/kaggle-gpu-images/python /bin/bash

Important Python Libraries¶

numpy¶

• Array computing in Python. Numpy official quickstar.t

• NumPy dimensions are called axes.

  import numpy as np
  a = np.array([2, 3, 4])
  b = np.ones((2, 3, 4), dtype=np.int16)
  c = np.zeros((3, 4))
  

• Create a sequence of number: np.arange(10, 30, 5)

• Matrix product: using .dot or @

  A = np.array([[1, 1], [0, 1]])
  B = np.array([[2, 0], [3, 4]])
  A @ B
  A.dot(B)
  

scipy¶

SciPy is a collection of mathematical algorithms and convenience functions built on top of NumPy. See product documentation.

• Get a normal distribution function: use the probability density function (pdf)

  from scipy.stats import norm
  x = np.arange(-3, 3, 0.01)
  y=norm.pdf(x)
  

MatPlotLib¶

Persent figure among multiple axes, from the data for human analysis.

• Classic import

  import matplotlib.pyplot as plt
  import numpy as np
  
  import matplotlib as mpl
  

• See Notebook

Seaborn¶

Seaborn provides a high-level interface for drawing attractive and informative statistical graphics. Based on top of MatPlotLib and integrated with Pandas.

See the introduction for different examples

import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme()
sns.relplot(
    data=masses_data,
    x="age", y="shape", 
    hue="density", size="density"
)
plt.show()

PyTorch¶

Via conda or pip, install pytorch torchvision torchaudio.

Example of getting started code in deep-neural-net folder.

Summary of the library and deeper studies

Code Samples¶

Code is organized in three main folders: examples/ for library-specific samples, e2e-demos/ for end-to-end applications, and notebooks/ for Jupyter notebooks.

Machine Learning Classifiers¶

Located in examples/ml-python/classifiers/:

PyTorch Deep Learning¶

Located in examples/pytorch/:

LangChain and LLM Integration¶

Located in examples/llm-langchain/:

RAG Implementations¶

Located in examples/llm-langchain/rag/:

LangGraph Agent Patterns¶

Located in examples/llm-langchain/langgraph/:

Ollama Local LLM¶

Located in examples/llm-ollama/:

End-to-End Demos¶

Located in e2e-demos/:

UI Frameworks¶

Located in techno/:

Jupyter Notebooks¶

Located in notebooks/: