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…ndencies

This commit addresses outdated dependencies and improves dependency management:

Changes:

  • Update PyTorch from 1.6.0 (2020) to latest stable (2.0.0+)
  • Update torchvision from 0.7.0 (2020) to latest compatible version (0.15.0+)
  • Add minimum version constraints for devito (>=4.8.0)
  • Separate runtime and development dependencies:
    • requirements.txt: Core runtime deps (devito, torch)
    • requirements-dev.txt: Test-only deps (pytest, torchvision, numpy)
  • Update CI workflow to use modern PyTorch CPU builds
  • Update Dockerfile to use latest PyTorch versions
  • Update pip cache key to reflect new requirements structure

Benefits:

  • Removes 5+ year old security-outdated packages
  • Clarifies which dependencies are needed for runtime vs testing
  • Allows users to install Joey without test-only bloat
  • Maintains compatibility with modern Python ecosystems
  • No known security vulnerabilities (verified with pip-audit)

Torchvision moved to dev dependencies as it's only used in test_lenet.py for MNIST dataset loading.

…ndencies

This commit addresses outdated dependencies and improves dependency management:

Changes:
- Update PyTorch from 1.6.0 (2020) to latest stable (2.0.0+)
- Update torchvision from 0.7.0 (2020) to latest compatible version (0.15.0+)
- Add minimum version constraints for devito (>=4.8.0)
- Separate runtime and development dependencies:
  * requirements.txt: Core runtime deps (devito, torch)
  * requirements-dev.txt: Test-only deps (pytest, torchvision, numpy)
- Update CI workflow to use modern PyTorch CPU builds
- Update Dockerfile to use latest PyTorch versions
- Update pip cache key to reflect new requirements structure

Benefits:
- Removes 5+ year old security-outdated packages
- Clarifies which dependencies are needed for runtime vs testing
- Allows users to install Joey without test-only bloat
- Maintains compatibility with modern Python ecosystems
- No known security vulnerabilities (verified with pip-audit)

Torchvision moved to dev dependencies as it's only used in test_lenet.py
for MNIST dataset loading.
This notebook provides a detailed performance benchmark comparing Joey
and PyTorch implementations of LeNet-5 on the MNIST dataset.

Features:
- Complete LeNet-5 implementation in both Joey and PyTorch
- Performance benchmarks for:
  * Forward pass (with statistical analysis)
  * Backward pass (gradient computation)
  * Complete training loop
- Visualization of comparative results
- Numerical correctness verification
- Detailed Portuguese documentation

The notebook demonstrates:
- How to build CNNs with Joey
- Fair performance comparison with identical initial weights
- Statistical analysis with mean and standard deviation
- Visual comparisons through matplotlib charts

This provides a practical example for users to understand Joey's
performance characteristics compared to the industry-standard PyTorch.
Added a new section (0. Instalação) with:
- Automatic installation script for Joey and dependencies
- Multiple installation options (GitHub, local clone, manual)
- Dependency checking for torchvision and matplotlib
- Clear instructions in Portuguese for users
- Handles missing Joey package gracefully

This makes the notebook standalone and easier to use for new users.
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2 participants