Code Interview

An old code interview reworked to test multiple torch concepts.
I was supposed to write a CIFAR10 classifier using a ConvNet, you can still find the model here.

Table of Contents

• Setup
• Usage
• Architecture
• Room for Improvement
• Contact

Setup

To use the projet please:

• Install dvc
• Do dvc repro

Usage

To reproduce the all dvc pipeline: dvc repro
To download the dataset: python3 src/data_handler.py
To find the best learning rate and optimizer according to Optuna: python3 src/hyperparameters.py
To rerun only the model training: python3 src/main.py

Architecture

This project can be read in mutliple parts:

• The dvc pipeline to order every step
• The logs handler to load the log level regarding var env
• The yaml handler to load and write yaml
• The data handler to download and load CIFAR10
• The hyper-paramerters tuner using Optuna to find the best learning rate and optimizer
• The model and its layers to classify CIFAR10
  The model include multiple torch concepts:
  • hooks to cast every inputs to torch.Tensor and send it to the selected device
  • Torchscript and jit to optimize the model for deployment by scripting the model.
    Note: Here, we do not use torch.trace
  • pruning to optimize the model for deployment by pruning the model.
  • quantization to optimize the model for deployment by casting tensors to uint8.
• The main script to run the training loop and save resuling models

TODO

• Add torch profiler to the project
• Use CML w/ dvc
• Use Optuna to find the best hyperparameters
  • Write module docstring
• Use MLFlow

Contact

Created by @Thytu - feel free to contact me!