tempest

This repository has my pipeline for training neural nets.

Main frameworks used:

• hydra
• pytorch-lightning

The main ideas of the pipeline:

• all parameters and modules are defined in configs;
• prepare configs beforehand for different optimizers/schedulers and so on, so it is easy to switch between them;
• have templates for different deep learning tasks. Currently, image classification and named entity recognition are supported;

Examples of running the pipeline: This will run training on MNIST (data will be downloaded):

>>> python train.py --config-name mnist_config model.encoder.params.to_one_channel=True

Running on MPS (M1 macbook)

python train.py --config-name mnist_config model.encoder.params.to_one_channel=True trainer.accelerator=mps +trainer.devices=1 optimizer=adan training.lr=0.001

Running on MPS (M1 macbook) with schedule free optimizer https://github.com/facebookresearch/schedule_free/tree/main

python train.py --config-name mnist_config model.encoder.params.to_one_channel=True trainer.accelerator=mps trainer.devices=1 optimizer=adamwschedulefree training.lr=0.001 scheduler.params.patience=100

The default run:

>>> python train.py

The default version of the pipeline is run on imagenette dataset. To do it, download the data from this repository: https://github.com/fastai/imagenette unzip it and define the path to it in conf/datamodule/image_classification.yaml path