Commit Graph

18 Commits

Author SHA1 Message Date
Vijay Janapa Reddi
9826ed0118 Deprecate AUTO TESTING: Remove run_module_tests_auto from all _dev.py modules. Standardize on full-module test execution for reliable, context-aware testing. 2025-07-20 13:28:10 -04:00
Vijay Janapa Reddi
ba2512e4e5 Update test function names from test_integration_* to test_module_* for clearer cross-module testing semantics 2025-07-20 13:03:52 -04:00
Vijay Janapa Reddi
03fe3d3973 Renames integration test function
Updates the integration test function name for clarity
and consistency within the codebase.
2025-07-20 12:59:51 -04:00
Vijay Janapa Reddi
e9a37652b0 Clean up formatting in layers module 2025-07-20 12:54:57 -04:00
Vijay Janapa Reddi
30b8bc3b59 Fix test function calls - remove __main__ wrapper to ensure tests run during automation 2025-07-20 12:51:47 -04:00
Vijay Janapa Reddi
943c0616cc Simplify plot handling - remove _should_show_plots functions and plot guards 2025-07-20 12:47:14 -04:00
Vijay Janapa Reddi
aa4eb0f809 Removes development headers from notebooks
Removes redundant "DEVELOPMENT" headers from several notebook files.

These headers are no longer necessary and declutter the notebook content, improving readability and focus on the core content and testing sections.
2025-07-20 12:39:21 -04:00
Vijay Janapa Reddi
ecf844fd2c Standardize section headers for 04_layers module 2025-07-20 12:25:54 -04:00
Vijay Janapa Reddi
edf7caf8b5 🧪 Fix test function name mismatches in 04_layers module
- Fixed test_matrix_multiplication() → test_unit_matrix_multiplication()
- Fixed test_dense_layer() → test_unit_dense_layer()
- Fixed test_layer_activation() → test_unit_layer_activation()

Ensures correct function names are called to match their definitions.
2025-07-20 10:22:11 -04:00
Vijay Janapa Reddi
e9371e2182 Add structural organization headers to 04_layers module
- Added ## 🔧 DEVELOPMENT section before Step 1 where development begins
- Added ## 🤖 AUTO TESTING section before nbgrader block
- Updated to ## 🎯 MODULE SUMMARY: Neural Network Layers

Improves notebook organization without changing any code logic or content.
2025-07-20 09:56:48 -04:00
Vijay Janapa Reddi
7582409690 🧹 Remove backup files - Clean repository maintenance
- Delete 8 *_backup.py files from modules/source directories
- Remove tito/commands/test.py.backup file
- Eliminates obsolete backup files from version control
- Keeps repository clean and focused on current implementations
- Reduces repository size and improves maintainability

Removed files:
- modules/source/02_tensor/tensor_dev_backup.py
- modules/source/03_activations/activations_dev_backup.py
- modules/source/04_layers/layers_dev_backup.py
- modules/source/05_dense/dense_dev_backup.py
- modules/source/06_spatial/spatial_dev_backup.py
- modules/source/08_dataloader/dataloader_dev_backup.py
- modules/source/09_autograd/autograd_dev_backup.py
- modules/source/13_kernels/kernels_dev_backup.py
- tito/commands/test.py.backup
2025-07-20 08:42:59 -04:00
Vijay Janapa Reddi
cec401af65 🧹 Remove Jupyter notebooks from modules/source - Python-first workflow
- Delete all 15 .ipynb files from modules/source directories
- Align with TinyTorch's Python-first development philosophy
- .py files are the source of truth, .ipynb files are temporary outputs
- Prevents version control conflicts with notebook metadata
- Students work directly with .py files using Jupytext format
- Notebooks can be regenerated when needed via 'tito nbdev generate'

Removed files:
- All *_dev.ipynb files across modules 01-15
- Keeps repository clean and focused on source code
2025-07-20 08:41:26 -04:00
Vijay Janapa Reddi
fd6c15da48 🧠 Core ML: Standardize test naming in neural network building blocks
- Activations: test_integration_* → test_module_* (module dependency tests)
- Layers: test_matrix_multiplication → test_unit_matrix_multiplication
- Layers: test_dense_layer → test_unit_dense_layer
- Layers: test_layer_activation → test_unit_layer_activation
- Dense: test_integration_* → test_module_* (module dependency tests)
- Spatial: test_integration_* → test_module_* (module dependency tests)
- Attention: test_integration_* → test_module_* (module dependency tests)
- Establishes unit vs module test distinction for neural network components
2025-07-20 08:39:00 -04:00
Vijay Janapa Reddi
56a9baefa9 refactor: Implement YAML-based difficulty and time system
- Added educational metadata (difficulty, time_estimate) to all module.yaml files
- Updated convert_readmes.py to read from YAML instead of hardcoded mappings
- Standardized difficulty progression: 🥷
- Fixed path resolution for YAML reading in book build process
- Eliminated duplication: single source of truth for educational metadata
- Capstone gets special ninja treatment (🥷) as beyond-expert level
2025-07-16 11:48:09 -04:00
Vijay Janapa Reddi
be3f3503a1 Standardize all 14 module READMEs with consistent structure
 Complete standardization of all TinyTorch module READMEs:

📊 **Module Info**: Consistent difficulty, time, prerequisites, next steps
🎯 **Learning Objectives**: Clear, measurable, action-oriented outcomes
🧠 **Pedagogical Framework**: Build → Use → [Context-specific verb]
📚 **What You'll Build**: Concrete code examples and implementations
🚀 **Getting Started**: Prerequisites check + development workflow
🧪 **Testing**: Comprehensive test coverage + inline feedback
🎯 **Key Concepts**: Real-world applications + technical foundations
🎉 **Ready to Build**: Motivational + grid cards for all modules

 All 14 modules now follow identical structure:
- 01_setup: Foundation workflow mastery
- 02_tensor: Core data structures
- 03_activations: Neural network fundamentals
- 04_layers: Building blocks
- 05_networks: Architecture design
- 06_cnn: Computer vision foundations
- 07_dataloader: Data pipeline engineering
- 08_autograd: Automatic differentiation
- 09_optimizers: Learning algorithms
- 10_training: End-to-end orchestration
- 11_compression: Model optimization
- 12_kernels: Performance optimization
- 13_benchmarking: Systematic evaluation
- 14_mlops: Production deployment (capstone)

🎓 **Student Experience**: Predictable navigation, clear expectations, motivational flow
👨‍🏫 **Instructor Experience**: Professional consistency, easy maintenance, coherent course

This establishes the single source of truth that will automatically convert to
clean website chapters via book/convert_readmes.py
2025-07-16 01:44:49 -04:00
Vijay Janapa Reddi
412c90856f Standardize module headers - consistent 🔥 emoji and clean chapter titles
README Updates:
- All modules now use consistent '🔥 Module: [Name]' format
- Removed inconsistent emojis (🧠, 🚀, 📊, 🧱, 🏋️)
- Removed module numbers and descriptive subtitles
- Clean, consistent branding across all 14 modules

Converter Updates:
- Added header cleaning logic to strip module prefixes from chapter titles
- Chapters now show clean names: 'CNN', 'Tensor', 'Setup', etc.
- No emoji or module numbers in final website headers
- Maintains clean, professional appearance

Result: Consistent source files + clean website presentation
2025-07-16 01:18:07 -04:00
Vijay Janapa Reddi
af996a8b9b Generate notebook files from Python modules for direct access 2025-07-15 23:51:56 -04:00
Vijay Janapa Reddi
8afe207ce5 Renumber modules from 00-13 to 01-14 for natural numbering
 Rename all module directories: 00_setup → 01_setup, etc.
 Update convert_modules.py mappings for new directory names
 Update _toc.yml file paths and titles (1-14 instead of 0-13)
 Regenerate all overview pages with new numbering
 Fix all broken references in usage-paths and intro
 Update chapter references to use natural numbering

Benefits:
- More intuitive course progression starting from 1
- Matches academic course numbering conventions
- Eliminates confusion about 'Module 0' concept
- Cleaner mental model for students and instructors
- All references and links properly updated

Complete transformation: 14 modules now numbered 01-14
2025-07-15 18:51:36 -04:00