The final project of Deep Learning 2021 course at Skoltech, Russia.

Team members:

• Ilya Kuk
• Razan Dibo
• Mohammed Deifallah
• Sergei Gostilovich
• Alexey Larionov
• Stanislav Krikunov
• Alexander Blagodarnyi

Inspired by the papers:

1. Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning.
2. Fundamentals of Coherent Optical Fiber Communications

• train.py - entry point for training of models (see Reproduce training and inference section)
• notebooks/training.ipynb - a quickstart Jupyter notebook for training or loading from a checkpoint
• configs/ - YAML files that define each experiment's parameters
• data/ - definitions of datasets (either preloaded or generated)
• materials/ - supplementary materials like reports, plots
• models/ - definitions of models and their training process (optimizers, learning rate schedulers)
• auxiliary/ - supporting files with utility functions
• 👉 PRESENTATION.pdf - final presentation
• 👉 REPORT.pdf - project final report
• 👉 VIDEO_PRESENTATION.txt - link to video with project presentation

A GPU is recommended to perform the experiments. You can use Google Colab with Jupyter notebooks provided in notebooks/ folder

Main prerequisites are:

• pytorch for models training
• pytorch-lightning with LightningCLI for CLI tools and low-boilerplate models training
• numpy for data generation

Optional:

• google-colab - if you want to mount a Google Drive to your Jupyter Notebook to store training artifacts
• gdown - if you want to download checkpoints from Google Drive by ID
• tensorboard - if you want to view training/validation metrics of different experiments
• torchvision - only if you want to debug the workflow with a trivial MNIST classifier example

The easiest way to start training of one of experiments listed in configs/, is to run

python train.py --config configs/your_chosen_experiment.yaml

After that you'll find new folders downloads/ with external downloaded files (like datasets) and logs/ which will contain folders for each distinct experiment. Under each such experiment folder you'll find results of all the runs of this very same experiment, namely folders like version_0/, version_1/, etc, which would finally contain:

• config.yaml with the parameters of the experiment for reproducibility (same parameters as in your_chosen_experiment.yaml)
• events.out.tfevents... file with logs of TensorBoard, ready to be visualized in it
• checkpoints/ directory with the best epoch checkpoint and the lastest epoch ckeckpoint (you can use those to resume training from them, or load them for inference)

A better approach to start training (or resuming or loading for inference), would be to use notebooks/training.ipynb Jupyter Notebook. In the first section you can set the parameters of further work. Other sections don't usually need any adjustments. After you "Run All" the notebook, either a training will start (a new one, or resumed), or only the model weights will be loaded (if you've chosen to 'load_model', see the notebook).

Anyway after the notebook has been run completely, you should be given model variable of type pytorch_lightning.LightningModule. You can do inference with it suing model.forward(x).