Vijay Janapa Reddi
6c48010c13
✨ Add structural organization headers to 06_spatial module
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- Added ## 🔧 DEVELOPMENT section before Step 1 where development begins
- Added ## 🤖 AUTO TESTING section before auto testing block
- Updated to ## 🎯 MODULE SUMMARY: Convolutional Networks
Improves notebook organization without changing any code logic or content.
2025-07-20 09:59:37 -04:00
Vijay Janapa Reddi
35bf079749
🧹 Remove backup files - Clean repository maintenance
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- 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
771ed98a80
🧹 Remove Jupyter notebooks from modules/source - Python-first workflow
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- 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
dfad756278
🧠 Core ML: Standardize test naming in neural network building blocks
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- 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
d4d6277604
🔧 Complete module restructuring and integration fixes
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📦 Module File Organization:
- Renamed networks_dev.py → dense_dev.py in 05_dense module
- Renamed cnn_dev.py → spatial_dev.py in 06_spatial module
- Added new 07_attention module with attention_dev.py
- Updated module.yaml files to reference correct filenames
- Updated #| default_exp directives for proper package exports
🔄 Core Package Updates:
- Added tinytorch.core.dense (Sequential, MLP architectures)
- Added tinytorch.core.spatial (Conv2D, pooling operations)
- Added tinytorch.core.attention (self-attention mechanisms)
- Updated all core modules with latest implementations
- Fixed tensor assignment issues in compression module
🧪 Test Integration Fixes:
- Updated integration tests to use correct module imports
- Fixed tensor activation tests for new module structure
- Ensured compatibility with renamed components
- Maintained 100% individual module test success rate
Result: Complete 14-module TinyTorch framework with proper organization,
working integrations, and comprehensive test coverage ready for production use.
2025-07-18 02:10:49 -04:00
Vijay Janapa Reddi
442e860d5f
Fix module file naming and tensor assignment issues
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- Updated module.yaml files for 05_dense and 06_spatial to reference correct dev file names
- Fixed #| default_exp directives in dense_dev.py and spatial_dev.py to export to correct module names
- Fixed tensor assignment issues in 12_compression module by creating new Tensor objects instead of trying to assign to .data property
- Removed missing function imports from autograd integration test
- All individual module tests now pass (01_setup through 14_benchmarking)
- Generated correct module files: dense.py, spatial.py, attention.py
2025-07-18 01:56:07 -04:00
Vijay Janapa Reddi
59d58718f9
refactor: Implement learner-focused module progression with better naming
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✅ Renamed modules for clearer pedagogical flow:
- 05_networks → 05_dense (multi-layer dense/fully connected networks)
- 06_cnn → 06_spatial (convolutional networks for spatial patterns)
- 06_attention → 07_attention (attention mechanisms for sequences)
✅ Shifted remaining modules down by 1:
- 07_dataloader → 08_dataloader
- 08_autograd → 09_autograd
- 09_optimizers → 10_optimizers
- 10_training → 11_training
- 11_compression → 12_compression
- 12_kernels → 13_kernels
- 13_benchmarking → 14_benchmarking
- 14_mlops → 15_mlops
- 15_capstone → 16_capstone
✅ Updated module metadata (module.yaml files):
- Updated names, descriptions, dependencies
- Fixed prerequisite chains and enables relationships
- Updated export paths to match new names
New learner progression:
Foundation → Individual Layers → Dense Networks → Spatial Networks → Attention Networks → Training Pipeline
Perfect pedagogical flow: Build one layer → Stack dense layers → Add spatial patterns → Add attention mechanisms → Learn to train them all.
2025-07-18 00:12:50 -04:00
