Having written hand-crafted networks for years, I have recently been testing the meta-experiments framework: you conduct thousands of AI experiments based on a range hyper-parameter values, do ablations, and choose the best performer. I have a near-mature repo which joins the strengths of ray-tune (generates random AI trials, multi-GPU support), neptune (tracks trial progress,metrics, and charts), and fastai. I am seeking interested individuals for a collab in radiology/cancer AI on 3D datasets (#KiTS, #LiTS challenge). Screenshots below showcase the features:
(ray-tune,NVIDI 3090x2)
Raytune live-feeding experiment progress into neptune via callbacks
Neptune in-depth: charts from a single experiment. Do you spot how the LR (figure on top-left) is flat and later decays. That’s the ReduceLROnPlateau callback!
A callback uploads training / validation results per epoch onto neptune in a color-coded scheme(3-classes 3 colors for KiTS). The image below is not captioned so, from the left images are in threes, a greyscale input image, color-coded prediction, (red=kidney, green=tumour) and color-coded ground truth. You will see a lot of black images, because they are slices without tumour.
Caveat:
This is a highly opinionated quasi-library of tools, designed with a very specific task in mind: medical image segmentation and classification.
P.S I work mostly with neovim highly adapted to fast python coding. Interested individuals may contact to share configs.
Thanks,
Usman
