wah

Think training a ResNet-18 on CIFAR-10 is a breeze? 🌬️💨

It might seem simple at first — until you find yourself drowning in boilerplate code:

• Setting up data loaders
• Defining model architectures
• Configuring loss functions
• Choosing and tuning optimizers
• ...and so much more! 🤯

What if you could skip all that hassle?

With this approach, you won't have to write a single line of code — just define a YAML configuration file:

# config.yaml
batch_size: 256
num_workers: 8
epochs: 90
init_lr: 1.e-1
optimizer: SGD
optimizer_cfg:
  momentum: 0.9
  weight_decay: 1.e-4
lr_scheduler: StepLR
lr_scheduler_cfg:
  step_size: 30
  gamma: 0.1
criterion: CrossEntropyLoss

and simply run:

wah train --dataset cifar10 --dataset-root ./dataset --model resnet18 \
    --cfg-path ./config.yaml --log-root ./logs --device auto

What Happens Next?

This single command will:

✅ Automatically download CIFAR-10 to ./dataset
✅ Train a ResNet-18 model on it
✅ Save checkpoints and TensorBoard logs to ./logs
✅ Detect available hardware (CPU/GPU) with multi-GPU support (DDP)

No tedious setup, no redundant scripting — just efficient, streamlined model training. 🚀

And that’s just the beginning!

You’ve found more than just a training tool — a powerful, flexible framework designed to accelerate deep learning research.

Produly presents:

WAH

Install

pip install wah

Requirements

You might want to manually install PyTorch for GPU computation.

lightning
matplotlib
numpy
pandas
pillow
requests
timm
torch
torchmetrics
torchvision
tqdm
yaml

Structure

wah

• classification
  • datasets
    • CIFAR10
    • CIFAR100
    • compute_mean_and_std
    • ImageNet
    • load_dataloader
    • portion_dataset
    • STL10
  • models
    • FeatureExtractor
    • load
    • load_state_dict
    • replace
      • gelu_with_relu
      • relu_with_gelu
      • bn_with_ln
      • ln_with_bn
    • summary
  • test
    • brier_score
    • ece
  • Trainer
• dicts
• fun
  • RecursionWrapper
• lists
• mods
• path
• random
• tensor
• time
• utils
  • ArgumentParser
  • download
  • zips