This repository has my pipeline for training neural nets.
Main frameworks used:
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
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