Reduce end to end training time from days to hours (or hours to minutes), and energy requirements/costs by an order of magnitude using coresets and data selection.

Logistic Regression model throws errors when we do back propagation. The fix for this is perhaps making freeze=False in forward function of utils/models/logreg_net.py

Hi, I tested the example with Supervised learning and Glister strategy. https://github.com/decile-team/cords/blob/main/examples/SL/image_classification/python_notebooks/CORDS_SL_CIFAR10_Custom_Train.ipynb But when I print the weight of the train loader, they are all 1.0. I believe that by using Glister strategy, we will get different weights.

tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
        1., 1.], device='cuda:0')

Is that a bug or something special? Thanks.

Hi,

I was trying to deploy CORDS selection to my training, but this error popped out Segmentation fault (core dumped).

I imitated code from https://github.com/decile-team/cords/blob/main/examples/SL/image_classification/python_notebooks/CORDS_SL_CIFAR10_Custom_Train.ipynb.

So basically I put my training and testing loader into GLISTERDataLoader, and switched this part into my code

for _, (inputs, targets, weights) in enumerate(dataloader): inputs = inputs.to(device) targets = targets.to(device, non_blocking=True) weights = weights.to(device) optimizer.zero_grad() outputs = model(inputs) losses = criterion_nored(outputs, targets) loss = torch.dot(losses, weights/(weights.sum())) loss.backward()

before modifying my code was running fine, so I believe there is an error inside the CORDS, my dataset is CIFAR10.

Thanks

Fixes #16
submodlib is now used for the CRAIG strategy/dataloader as well as the submodular strategy/dataloader. Please let me know if you have any feedback!

Notes:

• I am not sure if sum redundancy (a submodular function implemented in apricot) has an analogue in submodlib, so it is disabled as an option for now.
• It doesn't seem like submodularselectionstrategy.py is used in the corresponding dataloader. This may be a good opportunity to refactor, so that behavior is consistent between the two.
• Any existing code that specifies the "optimizer" (greedy algorithm) used by apricot will break, since the names used by submodlib are different than those used by apricot (e.g. 'LazyGreedy' instead of 'Lazy'). This includes configs that use this option.

There is a typo in the cords_cifar10_glister_train.ipynb notebook : https://github.com/decile-team/cords/blob/main/examples/SL/image_classification/cords_cifar10_glister_train.ipynb

glister_trn.configdata.train_args.print_every = 1
glister_trn.configdata.train_args.device = 'cuda'
glister_trn.configdata.dss_args.fraction = fraction

instead of

glister_trn.cfg.train_args.print_every = 1
glister_trn.cfg.train_args.device = 'cuda'
glister_trn.cfg.dss_args.fraction = fraction

Hello, thanks for a very interesting and useful project.

Could you mind providing an evaluation method for ImageNet? I tried to, adding loader for ImageNet to custom_dataset.py, but failed due to a GPU memory issue during subset selection.

Many thanks!

1. For GRAD-MATCH method, there are weights associated with each data point in X(subset of training set). Do the weights have physical significance? for example, if the value of the weight is higher, the relevant selected data has the greater contribution to the residual?
2. During the iteration, the selective index is in the selected indices, so the iteration break. why this happen? [email protected]

Hello! Thanks for your great work.

I'm currently working on this code and I want to ask a question about accuracy logging.

https://github.com/decile-team/cords/blob/ff629ff15fac911cd3b82394ffd278c42dacd874/train.py#L530-L541

In line 541 of train.py, val_acc contains cumulative accuracies over input batches. For example, if the loader contains 4500 examples and the batch size is 1000, then tst_acc has 5 accuracies per each evaluation. (the first element of tst_acc will be the accuracy over the first 1000 examples)

https://github.com/decile-team/cords/blob/ff629ff15fac911cd3b82394ffd278c42dacd874/train.py#L631-L633

In line 633, it prints the best value in tst_acc. In this case, the resulted best accuracies over different algorithms and seeds might be the values evaluated on different test samples.

Is this what you intended? In my experience, I think evaluating algorithms on an identical test dataset is a convention. In addition, is the reported test accuracies in the GRAD-MATCH paper the best values as above or the last test accuracy?

Best, Jang-Hyun

a) Implement gradient calculation for Squared Loss, Negative logistic loss, General loss function gradient computation, Hinge loss.

b) Integrate the new gradient calculation with different selection strategies

• Add a folder called benchmarks which has all the results/benchmarks for the various cases. We should remove the results from the main readme and point them to that folder. Also, add the notebooks to reproduce the benchmark results
• Rename notebooks to tutorials. Add different tutorials based on use-cases (NLP, Vision, SSL, Hyper-parameter tunings, NAS, etc.)

Hello, I ran some experiments with gradmatch and randomonline, and find these two actually reach similar performances after 300 epochs, which is around 93, is there something important to note for reproducing the results? Thanks for your help!

Implement the following versions of OMP:

1. FNNOMP (https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7012095)
2. SNNOMP (https://hal.univ-lorraine.fr/hal-01585253/document)

I tried to run some experiments as follows:

• Ran full cifar10 without any subset selection method to train resnet50 which took around 32m 31s.
• Ran Gradmatch cifar10 subset selection with 0.1 fractions taking longer time than full cifar10 i.e 22h 48m 40s.
• Ran Gradmatch cifar10 subset selection with 0.3 fractions taking longer time than 0.1 Gradmatch selection method.

I am using scaled resolution images of cifar10 i.e 224x224 resolution and accordingly defined resnet50 architecture. Can you let me know how to speed up experiments 2 and 3? In general subset selection method should faster the whole training process right?

1. Implement the CRUST strategy in the supervised learning setting.
2. Create the CRUST data loader class building it on top of adaptive_dataloader class.