Build an LLM from Scratch 1: Set up your code environment

Sebastian Raschka · Beginner ·🧠 Large Language Models ·1y ago

Key Takeaways

Setting up a Python environment for building LLMs from scratch using uv, covering installation, virtual environment creation, and package installation

Original Description

Links to the book: - https://amzn.to/4fqvn0D (Amazon) - https://mng.bz/M96o (Manning) Link to the GitHub repository: https://github.com/rasbt/LLMs-from-scratch This is a supplementary video explaining how to set up a Python environment using uv. 00:00 Introduction 01:33 Setup info on GitHub 03:30 Optional setup preferences and uv 05:00 uv pip vs uv add syntax 05:35 1) Installing Python 09:05 2) Setting up uv 10:12 3) Creating a virtual environment 14:03 4) Installing packages 16:34 5) pip install fallback 16:57 6) If nothing works: Google Colab :) 19:07 7) uv run to run Jupyter Lab locally In particular, we are using `uv pip`, which is explained in this document: https://github.com/rasbt/LLMs-from-scratch/blob/main/setup/01_optional-python-setup-preferences/README.md Alternatively, the native `uv add` syntax (mentioned but not explicitly covered in this video) is described here: https://github.com/rasbt/LLMs-from-scratch/blob/main/setup/01_optional-python-setup-preferences/native-uv.md
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1 Intro to Deep Learning -- L06.5 Cloud Computing [Stat453, SS20]
Intro to Deep Learning -- L06.5 Cloud Computing [Stat453, SS20]
Sebastian Raschka
 2 Intro to Deep Learning -- L09 Regularization [Stat453, SS20]
Intro to Deep Learning -- L09 Regularization [Stat453, SS20]
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 3 Intro to Deep Learning -- L10 Input and Weight Normalization Part 1/2 [Stat453, SS20]
Intro to Deep Learning -- L10 Input and Weight Normalization Part 1/2 [Stat453, SS20]
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 4 Intro to Deep Learning -- L10 Input and Weight Normalization Part 2/2 [Stat453, SS20]
Intro to Deep Learning -- L10 Input and Weight Normalization Part 2/2 [Stat453, SS20]
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 5 Intro to Deep Learning -- L11 Common Optimization Algorithms [Stat453, SS20]
Intro to Deep Learning -- L11 Common Optimization Algorithms [Stat453, SS20]
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 6 Intro to Deep Learning -- L12 Intro to Convolutional Neural Networks (Part 1) [Stat453, SS20]
Intro to Deep Learning -- L12 Intro to Convolutional Neural Networks (Part 1) [Stat453, SS20]
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 7 Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 1/2 [Stat453, SS20]
Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 1/2 [Stat453, SS20]
Sebastian Raschka
 8 Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 2/2 [Stat453, SS20]
Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 2/2 [Stat453, SS20]
Sebastian Raschka
 9 Intro to Deep Learning -- L14 Intro to Recurrent Neural Networks [Stat453, SS20]
Intro to Deep Learning -- L14 Intro to Recurrent Neural Networks [Stat453, SS20]
Sebastian Raschka
 10 Intro to Deep Learning -- L15 Autoencoders [Stat453, SS20]
Intro to Deep Learning -- L15 Autoencoders [Stat453, SS20]
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 11 Intro to Deep Learning -- L16 Generative Adversarial Networks [Stat453, SS20]
Intro to Deep Learning -- L16 Generative Adversarial Networks [Stat453, SS20]
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 12 Intro to Deep Learning -- Student Presentations, Day 1 [Stat453, SS20]
Intro to Deep Learning -- Student Presentations, Day 1 [Stat453, SS20]
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 13 1.2 What is Machine Learning (L01: What is Machine Learning)
1.2 What is Machine Learning (L01: What is Machine Learning)
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 14 1.3 Categories of Machine Learning (L01: What is Machine Learning)
1.3 Categories of Machine Learning (L01: What is Machine Learning)
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 15 1.4 Notation (L01: What is Machine Learning)
1.4 Notation (L01: What is Machine Learning)
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 16 1.1 Course overview (L01: What is Machine Learning)
1.1 Course overview (L01: What is Machine Learning)
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 17 1.5 ML application (L01: What is Machine Learning)
1.5 ML application (L01: What is Machine Learning)
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 18 1.6 ML motivation (L01: What is Machine Learning)
1.6 ML motivation (L01: What is Machine Learning)
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 19 2.1 Introduction to NN (L02: Nearest Neighbor Methods)
2.1 Introduction to NN (L02: Nearest Neighbor Methods)
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 20 2.2 Nearest neighbor decision boundary (L02: Nearest Neighbor Methods)
2.2 Nearest neighbor decision boundary (L02: Nearest Neighbor Methods)
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 21 2.3 K-nearest neighbors (L02: Nearest Neighbor Methods)
2.3 K-nearest neighbors (L02: Nearest Neighbor Methods)
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 22 2.4 Big O of K-nearest neighbors (L02: Nearest Neighbor Methods)
2.4 Big O of K-nearest neighbors (L02: Nearest Neighbor Methods)
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 23 2.5 Improving k-nearest neighbors (L02: Nearest Neighbor Methods)
2.5 Improving k-nearest neighbors (L02: Nearest Neighbor Methods)
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 24 2.6 K-nearest neighbors in Python (L02: Nearest Neighbor Methods)
2.6 K-nearest neighbors in Python (L02: Nearest Neighbor Methods)
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 25 3.1 (Optional) Python overview
3.1 (Optional) Python overview
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 26 3.2 (Optional) Python setup
3.2 (Optional) Python setup
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 27 3.3 (Optional) Running Python code
3.3 (Optional) Running Python code
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 28 4.1 Intro to NumPy (L04: Scientific Computing in Python)
4.1 Intro to NumPy (L04: Scientific Computing in Python)
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 29 4.2 NumPy Array Construction and Indexing (L04: Scientific Computing in Python)
4.2 NumPy Array Construction and Indexing (L04: Scientific Computing in Python)
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 30 4.4 NumPy Broadcasting (L04: Scientific Computing in Python)
4.4 NumPy Broadcasting (L04: Scientific Computing in Python)
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 31 4.5 NumPy Advanced Indexing -- Memory Views and Copies (L04: Scientific Computing in Python)
4.5 NumPy Advanced Indexing -- Memory Views and Copies (L04: Scientific Computing in Python)
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 32 4.3 NumPy Array Math and Universal Functions (L04: Scientific Computing in Python)
4.3 NumPy Array Math and Universal Functions (L04: Scientific Computing in Python)
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 33 4.7 Reshaping NumPy Arrays (L04: Scientific Computing in Python)
4.7 Reshaping NumPy Arrays (L04: Scientific Computing in Python)
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 34 4.6 NumPy Random Number Generators (L04: Scientific Computing in Python)
4.6 NumPy Random Number Generators (L04: Scientific Computing in Python)
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 35 4.8 NumPy Comparison Operators and Masks (L04: Scientific Computing in Python)
4.8 NumPy Comparison Operators and Masks (L04: Scientific Computing in Python)
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 36 4.9 NumPy Linear Algebra Basics (L04: Scientific Computing in Python)
4.9 NumPy Linear Algebra Basics (L04: Scientific Computing in Python)
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 37 4.10 Matplotlib (L04: Scientific Computing in Python)
4.10 Matplotlib (L04: Scientific Computing in Python)
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 38 5.1 Reading a Dataset from a Tabular Text File (L05: Machine Learning with Scikit-Learn)
5.1 Reading a Dataset from a Tabular Text File (L05: Machine Learning with Scikit-Learn)
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 39 5.2 Basic data handling (L05: Machine Learning with Scikit-Learn)
5.2 Basic data handling (L05: Machine Learning with Scikit-Learn)
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 40 5.3 Object Oriented Programming & Python Classes (L05: Machine Learning with Scikit-Learn)
5.3 Object Oriented Programming & Python Classes (L05: Machine Learning with Scikit-Learn)
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 41 5.4 Intro to Scikit-learn (L05: Machine Learning with Scikit-Learn)
5.4 Intro to Scikit-learn (L05: Machine Learning with Scikit-Learn)
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 42 5.5 Scikit-learn Transformer API (L05: Machine Learning with Scikit-Learn)
5.5 Scikit-learn Transformer API (L05: Machine Learning with Scikit-Learn)
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 43 5.6 Scikit-learn Pipelines (L05: Machine Learning with Scikit-Learn)
5.6 Scikit-learn Pipelines (L05: Machine Learning with Scikit-Learn)
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 44 6.1 Intro to Decision Trees (L06: Decision Trees)
6.1 Intro to Decision Trees (L06: Decision Trees)
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 45 6.2 Recursive algorithms & Big-O (L06: Decision Trees)
6.2 Recursive algorithms & Big-O (L06: Decision Trees)
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 46 6.3 Types of decision trees (L06: Decision Trees)
6.3 Types of decision trees (L06: Decision Trees)
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 47 6.5 Gini & Entropy versus misclassification error (L06: Decision Trees)
6.5 Gini & Entropy versus misclassification error (L06: Decision Trees)
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 48 6.6 Improvements & dealing with overfitting (L06: Decision Trees)
6.6 Improvements & dealing with overfitting (L06: Decision Trees)
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 49 6.7 Code Example Implementing Decision Trees in Scikit-Learn (L06: Decision Trees)
6.7 Code Example Implementing Decision Trees in Scikit-Learn (L06: Decision Trees)
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 50 7.1 Intro to ensemble methods (L07: Ensemble Methods)
7.1 Intro to ensemble methods (L07: Ensemble Methods)
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 51 7.2 Majority Voting (L07: Ensemble Methods)
7.2 Majority Voting (L07: Ensemble Methods)
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 52 7.3 Bagging (L07: Ensemble Methods)
7.3 Bagging (L07: Ensemble Methods)
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 53 7.4 Boosting and AdaBoost (L07: Ensemble Methods)
7.4 Boosting and AdaBoost (L07: Ensemble Methods)
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 54 7.5 Gradient Boosting (L07: Ensemble Methods)
7.5 Gradient Boosting (L07: Ensemble Methods)
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 55 7.6 Random Forests (L07: Ensemble Methods)
7.6 Random Forests (L07: Ensemble Methods)
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 56 7.7 Stacking (L07: Ensemble Methods)
7.7 Stacking (L07: Ensemble Methods)
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 57 8.1 Intro to overfitting and underfitting (L08: Model Evaluation Part 1)
8.1 Intro to overfitting and underfitting (L08: Model Evaluation Part 1)
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 58 8.2 Intuition behind bias and variance (L08: Model Evaluation Part 1)
8.2 Intuition behind bias and variance (L08: Model Evaluation Part 1)
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 59 8.3 Bias-Variance Decomposition of the Squared Error (L08: Model Evaluation Part 1)
8.3 Bias-Variance Decomposition of the Squared Error (L08: Model Evaluation Part 1)
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 60 8.4 Bias and Variance vs Overfitting and Underfitting (L08: Model Evaluation Part 1)
8.4 Bias and Variance vs Overfitting and Underfitting (L08: Model Evaluation Part 1)
Sebastian Raschka

This video explains how to set up a Python environment using uv for building LLMs from scratch, covering essential steps such as installing Python, setting up a virtual environment, and installing necessary packages. By following this tutorial, viewers can create a suitable environment for LLM development. The video also provides alternative solutions, such as using Google Colab, for cases where the primary setup method fails.

Key Takeaways
  1. Install Python
  2. Set up uv
  3. Create a virtual environment
  4. Install packages using uv pip or uv add syntax
  5. Use pip install as a fallback option
  6. Use Google Colab if other methods fail
  7. Run Jupyter Lab locally using uv run
💡 Using uv to manage the Python environment and packages simplifies the setup process and ensures consistency across different projects and collaborations.

Related Reads

Chapters (11)

Introduction
1:33 Setup info on GitHub
3:30 Optional setup preferences and uv
5:00 uv pip vs uv add syntax
5:35 1) Installing Python
9:05 2) Setting up uv
10:12 3) Creating a virtual environment
14:03 4) Installing packages
16:34 5) pip install fallback
16:57 6) If nothing works: Google Colab :)
19:07 7) uv run to run Jupyter Lab locally
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