This library is a work in progress to become a deep learning library for my own use.
The code in this library is heavily inspired by the miniai library developed as part of the part 2 course. I am writing the Deep Learning portions myself, but other parts of the library are taken directly from that library (ie memory cleaning functions, plotting helper functions, etc.). This is very much a work in progress at the very early/beginning stages. It’s not ready for practical use (yet!).
Install
Cone the repo and do an editable install. You will almost certainly need to modify the library as you use it given the early stages it is in.
pip install -e .[dev]
How to use
Dataloaders
HuggingFace Dataset Dict
from datasets import Dataset, load_datasetfrom AIsaac.dataloaders import DataLoadersimport torchvision.transforms.functional as TFfrom AIsaac.utils import inplacexmean,xstd =0.28, 0.35@inplacedef transformi(b): b['image'] = [(TF.to_tensor(o)-xmean)/xstd for o in b['image']]_dataset = load_dataset('fashion_mnist').with_transform(transformi)dls = DataLoaders.from_dataset_dict(_dataset, 64, num_workers=4)dls.show_batch(3)
Found cached dataset fashion_mnist (/home/.cache/huggingface/datasets/fashion_mnist/fashion_mnist/1.0.0/8d6c32399aa01613d96e2cbc9b13638f359ef62bb33612b077b4c247f6ef99c1)